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Chromatographic data processing has garnered attention due to multiple Food and Drug Administration 483 citations and warning letters, highlighting the need for a robust technological solution. The healthcare industry has the potential to greatly benefit from the adoption of digital technologies, but the process of implementing these technologies can be slow and complex. This article presents a "Digital by Design" managerial approach, adapted from pharmaceutical quality by design principles, for designing and implementing an artificial intelligence (AI)-based solution for chromatography peak integration process in the healthcare industry. We report the use of a convolutional neural network model to predict analytical variability for integrating chromatography peaks and propose a potential GxP framework for using AI in the healthcare industry that includes elements on data management, model management, and human-in-the-loop processes. The component on analytical variability prediction has a great potential to enable Industry 4.0 objectives on real-time release testing, automated quality control, and continuous manufacturing., (© 2023 Merck Serono S.p.A. Biotechnology and Bioengineering published by Wiley Periodicals LLC.)

Objectives: To evaluate AI biases and errors in estimating bone age (BA) by comparing AI and radiologists' clinical determinations of BA., Methods: We established three deep learning models from a Chinese private dataset (CHNm), an American public dataset (USAm), and a joint dataset combining the above two datasets (JOIm). The test data CHNt (n = 1246) were labeled by ten senior pediatric radiologists. The effects of data site differences, interpretation bias, and interobserver variability on BA assessment were evaluated. The differences between the AI models' and radiologists' clinical determinations of BA (normal, advanced, and delayed BA groups by using the Brush data) were evaluated by the chi-square test and Kappa values. The heatmaps of CHNm-CHNt were generated by using Grad-CAM., Results: We obtained an MAD value of 0.42 years on CHNm-CHNt; this result indicated an appropriate accuracy for the whole group but did not indicate an accurate estimation of individual BA because with a kappa value of 0.714, the agreement between AI and human clinical determinations of BA was significantly different. The features of the heatmaps were not fully consistent with the human vision on the X-ray films. Variable performance in BA estimation by different AI models and the disagreement between AI and radiologists' clinical determinations of BA may be caused by data biases, including patients' sex and age, institutions, and radiologists., Conclusions: The deep learning models outperform external validation in predicting BA on both internal and joint datasets. However, the biases and errors in the models' clinical determinations of child development should be carefully considered., Key Points: • With a kappa value of 0.714, clinical determinations of bone age by using AI did not accord well with clinical determinations by radiologists. • Several biases, including patients' sex and age, institutions, and radiologists, may cause variable performance by AI bone age models and disagreement between AI and radiologists' clinical determinations of bone age. • AI heatmaps of bone age were not fully consistent with human vision on X-ray films., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

This study examines the research landscape of smart learning environments by conducting a comprehensive bibliometric analysis of the field over the years. The study focused on the research trends, scholar's productivity, and thematic focus of scientific publications in the field of smart learning environments. A total of 1081 data consisting of peer-reviewed articles were retrieved from the Scopus database. A bibliometric approach was applied to analyse the data for a comprehensive overview of the trend, thematic focus, and scientific production in the field of smart learning environments. The result from this bibliometric analysis indicates that the first paper on smart learning environments was published in 2002; implying the beginning of the field. Among other sources, "Computers & Education," "Smart Learning Environments," and "Computers in Human Behaviour" are the most relevant outlets publishing articles associated with smart learning environments. The work of Kinshuk et al., published in 2016, stands out as the most cited work among the analysed documents. The United States has the highest number of scientific productions and remained the most relevant country in the smart learning environment field. Besides, the results also showed names of prolific scholars and most relevant institutions in the field. Keywords such as "learning analytics," "adaptive learning," "personalized learning," "blockchain," and "deep learning" remain the trending keywords. Furthermore, thematic analysis shows that "digital storytelling" and its associated components such as "virtual reality," "critical thinking," and "serious games" are the emerging themes of the smart learning environments but need to be further developed to establish more ties with "smart learning". The study provides useful contribution to the field by clearly presenting a comprehensive overview and research hotspots, thematic focus, and future direction of the field. These findings can guide scholars, especially the young ones in field of smart learning environments in defining their research focus and what aspect of smart leaning can be explored. [ABSTRACT FROM AUTHOR]

Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively. [ABSTRACT FROM AUTHOR]

Diagnosis of Cancer type is as important as the right classification of benign and malignant tumor cells. It is also very critical to achieve precision diagnosis to help doctor's find better clinical outcomes. Endometrial type of a cancer that starts in the uterus and found at an early stage as it frequently produces abnormal vaginal bleeding and hence it becomes critical to not only classify the tumor but also to detect the type and grade of the cancer. In the United States of America, endometrial cancer is a top five leading cancer with fifty-two thousand unique cases reported in the year 2014. This number raised to around sixty thousand in the year of 2016 and these numbers are further estimated to increase to around sixty-two thousand in 2019. The dataset used is composed of radiology and pathology images from Corpus Endometrial Carcinoma (CPTAC) Patients. This paper uses the Machine learning approaches that will classify the cell as malignant or benign. Deep learning approaches are used to provide Precision diagnosis that provides better outcomes to the clinicians. Convolutional Neural Network algorithms have been successfully used in diagnosis of Brain tumor, Skin Cancer and similar approaches can be used to tackle this problem. [ABSTRACT FROM AUTHOR]

Cancer remains a significant global health concern, with millions of deaths attributed to it annually. Environmental pollutants play a pivotal role in cancer etiology and contribute to the growing prevalence of this disease. The carcinogenic assessment of these pollutants is crucial for chemical health evaluation and environmental risk assessments. Traditional experimental methods are expensive and time-consuming, prompting the development of alternative approaches such as in silico methods. In this regard, deep learning (DL) has shown potential but lacks optimal performance and interpretability. This study introduces an interpretable DL model called CarcGC for chemical carcinogenicity prediction, utilizing a graph convolutional neural network (GCN) that employs molecular structural graphs as inputs. Compared to existing models, CarcGC demonstrated enhanced performance, with the area under the receiver operating characteristic curve (AUC ROC ) reaching 0.808 on the test set. Due to air pollution is closely related to the incidence of lung cancers, we applied the CarcGC to predict the potential carcinogenicity of chemicals listed in the United States Environmental Protection Agency's Hazardous Air Pollutants (HAPs) inventory, offering a foundation for environmental carcinogenicity screening. This study highlights the potential of artificially intelligent methods in carcinogenicity prediction and underscores the value of CarcGC interpretability in revealing the structural basis and molecular mechanisms underlying chemical carcinogenicity., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Simple Summary: Breast cancer is a significant cause of female cancer-related deaths in the US. Checking how severe the cancer is helps in planning treatment. Modern AI methods are good at grading cancer, but they are not used much in hospitals yet. We developed and utilized ensemble deep learning algorithms for addressing the tasks of classifying (1) breast cancer subtype and (2) breast cancer invasiveness from whole slide image (WSI) histopathology slides. The ensemble models used were based on convolutional neural networks (CNNs) known for extracting distinctive features crucial for accurate classification. In this paper, we provide a comprehensive analysis of these models and the used methodology for breast cancer diagnosis tasks. Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens. [ABSTRACT FROM AUTHOR]

Liver transplantation is a life-saving procedure for patients with end-stage liver disease. There are two main challenges in liver transplant: finding the best matching patient for a donor and ensuring transplant equity among different subpopulations. The current MELD scoring system evaluates a patient's mortality risk if not receiving an organ within 90 days. However, the donor-patient matching should also consider post-transplant risk factors, such as cardiovascular disease, chronic rejection, etc., which are all common complications after transplant. Accurate prediction of these risk scores remains a significant challenge. In this study, we used predictive models to solve the above challenges. Specifically, we proposed a deep learning model to predict multiple risk factors after a liver transplant. By formulating it as a multi-task learning problem, the proposed deep neural network was trained to simultaneously predict the five post-transplant risks and achieve equal good performance by exploiting task-balancing techniques. We also proposed a novel fairness-achieving algorithm to ensure prediction fairness across different subpopulations. We used electronic health records of 160,360 liver transplant patients, including demographic information, clinical variables, and laboratory values, collected from the liver transplant records of the United States from 1987 to 2018. The model's performance was evaluated using various performance metrics such as AUROC and AUPRC. Our experiment results highlighted the success of our multi-task model in achieving task balance while maintaining accuracy. The model significantly reduced the task discrepancy by 39 %. Further application of the fairness-achieving algorithm substantially reduced fairness disparity among all sensitive attributes (gender, age group, and race/ethnicity) in each risk factor. It underlined the potency of integrating fairness considerations into the task-balancing framework, ensuring robust and fair predictions across multiple tasks and diverse demographic groups., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Purpose: To analyze the AI research in the field of nursing, to explore the current situation, hot topics, and prospects of AI research in the field of nursing, and to provide a reference for researchers to carry out related studies. Methods: We used the VOSviewer 1.6.17, SciMAT, and CiteSpace 5.8.R3 to generate visual cooperation network maps for the country, organizations, authors, citations, and keywords and perform burst detection, theme evolution, and so forth. Findings A total of 9318 articles were obtained from the Web of Science Core Collection database. Four hundred and thirty‐one AI research related to the field of nursing was published by 855 institutions from 54 countries. CIN‐Computers Informatics Nursing was the top productive journal. The United States was the dominant country. The transnational cooperation between authors from developed countries was closer than that between authors from developing countries. The main hot topics included nurse rostering, nursing diagnosis, nursing decision support, disease risk factor prediction, nursing big data management, expert system, support vector machine, decision tree, deep learning, natural language processing, and nursing education. Machine learning represented one of the cutting‐edge and most applicable branches of artificial intelligence in the field of nursing, and deep learning was the hottest technology among many machine learning methods in recent years. One of the most cited papers was published by Burke in 2004 and cited 500 times, which critically evaluated AI methods to deal with nurse scheduling problems. Conclusions: Although AI has been paid more and more attention to the field of nursing, there is still a lack of high‐yielding authors who have been engaged in this field for a long time. Most of the high contribution authors and institutions came from developed countries; therefore, more transnational and multi‐disciplinary cooperation is needed to promote the development of AI in the nursing field. This bibliometric analysis not only provided a comprehensive overview to help researchers to understand the important articles, journals, potential collaborators, and institutions in this field but also analyzed the history, hot spots, and future trends of the research topic to provide inspiration for researchers to choose research directions. [ABSTRACT FROM AUTHOR]

Background: Deep learning methods, where models do not use explicit features and instead rely on implicit features estimated during model training, suffer from an explainability problem. In text classification, saliency maps that reflect the importance of words in prediction are one approach toward explainability. However, little is known about whether the salient words agree with those identified by humans as important. Objectives: The current study examines in‐line annotations from human annotators and saliency map annotations from a deep learning model (ELECTRA transformer) to understand how well both humans and machines provide evidence for their assigned label. Methods: Data were responses to test items across a mix of United States subjects, states, and grades. Humans were trained to annotate responses to justify a crisis alert label and two model interpretability methods (LIME, Integrated Gradients) were used to obtain engine annotations. Human inter‐annotator agreement and engine agreement with the human annotators were computed and compared. Results and Conclusions: Human annotators agreed with one another at similar rates to those observed in the literature on similar tasks. The annotations derived using the integrated gradients (IG) agreed with human annotators at higher rates than LIME on most metrics; however, both methods underperformed relative to the human annotators. Implications: Saliency map‐based engine annotations show promise as a form of explanation, but do not reach human annotation agreement levels. Future work should examine the appropriate unit for annotation (e.g., word, sentence), other gradient based methods, and approaches for mapping the continuous saliency values to Boolean annotations. Lay Description: What is known about subject matter: Explainability of deep learning models is complicated by their large size, design, and complexity.Explainability methods in deep learning exist but have not been rigorously tested.Saliency maps, as a form of explainability, can be used to generate engine annotations.Human annotations can serve as basis for comparison. What paper adds to knowledge: How well human annotations work in one domain (crisis paper identification).How well two engine annotation methods match human annotations. Implications of study findings: Human annotations have reasonable reliability.Engine annotations have lower reliability than humans.Model‐based engine annotations perform better than model‐agnostic engine annotations. [ABSTRACT FROM AUTHOR]

Historically, individuals with hearing impairments have faced neglect, lacking the necessary tools to facilitate effective communication. However, advancements in modern technology have paved the way for the development of various tools and software aimed at improving the quality of life for hearing-disabled individuals. This research paper presents a comprehensive study employing five distinct deep learning models to recognize hand gestures for the American Sign Language (ASL) alphabet. The primary objective of this study was to leverage contemporary technology to bridge the communication gap between hearing-impaired individuals and individuals with no hearing impairment. The models utilized in this research include AlexNet, ConvNeXt, EfficientNet, ResNet-50, and VisionTransformer were trained and tested using an extensive dataset comprising over 87,000 images of the ASL alphabet hand gestures. Numerous experiments were conducted, involving modifications to the architectural design parameters of the models to obtain maximum recognition accuracy. The experimental results of our study revealed that ResNet-50 achieved an exceptional accuracy rate of 99.98%, the highest among all models. EfficientNet attained an accuracy rate of 99.95%, ConvNeXt achieved 99.51% accuracy, AlexNet attained 99.50% accuracy, while VisionTransformer yielded the lowest accuracy of 88.59%.

The PCM (phase contrast microscopy) method for asbestos counting needs special sample treatments, hence it is time consuming and rather expensive. As an alternative, we implemented a deep learning procedure on images directly acquired from the untreated airborne samples using standard Mixed Cellulose Ester (MCE) filters. Several samples with a mix of chrysotile and crocidolite with different concentration loads have been prepared. Using a 20x objective lens coupled with a backlight illumination system a number of 140 images were collected from these samples, which along with additional 13 highly fibre loaded artificial images constituted the database. About 7500 fibres were manually recognised and annotated following the National Institute for Occupational Safety and Health (NIOSH) fibre counting Method 7400 as input for the training and validation of the model. The best trained model provides a total precision of 0.84 with F1-Score of 0.77 at a confidence of 0.64. A further post-detection refinement to ignore detected fibres < 5 µm in length improves the final precision. This method can be considered as a reliable and competent alternative to conventional PCM., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

While deep learning (DL) models are effective in rainfall-runoff modelling, their dependence on data and lack of physical mechanisms can limit their use in hydrology. As there is yet no consensus on the consideration of the fundamental water balance for DL models, this paper presents an in-depth investigation of the effects of water balance constraint on the long-short term memory (LSTM) network. Specifically, based on the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset, the LSTM and its architecturally mass-conserving variant (MC-LSTM) are trained basin-wise to provide rainfall-runoff prediction and then the robustness of the LSTM and MC-LSTM against data sparsity, random parameters initialization and contrasting climate conditions are assessed across the contiguous United States. Through large-sample tests, the results show that the water balance constraint evidently improves the robustness of the basin-wise trained LSTM. On the one hand, as the amount of training data increases from 1 year to 15 years, the incorporation of the water balance constraint into the LSTM network decreases the sensitivity from 95.0 % to 32.7 %. On the other hand, the water balance constraint contributes to the stability of the LSTM for 450 (85 %) basins when there are 3 years' training data. In the meantime, the water balance constraint improves the transferability of the LSTM from the driest years to the wettest years for 318 (67 %) basins. Overall, the in-depth investigations of this paper facilitate insights into the use of DL models for rainfall-runoff modelling. [ABSTRACT FROM AUTHOR]

Throughout the pandemic era, COVID-19 was one of the remarkable unexpected situations over the past few years, but with the decentralization and globalization of efforts and knowledge, a successful vaccine-based control strategy was efficiently designed and applied worldwide. On the other hand, excused confusion and hesitation have widely impacted public health. This paper aims to reduce COVID-19 vaccine hesitancy taking into consideration the patient's medical history. The dataset used in this study is the Vaccine Adverse Event Reporting System (VAERS) dataset which was created as a corporation between the Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC) to gather reported side effects that may be caused by PFIEZER, JANSSEN, and MODERNA vaccines. In this paper, a Deep Learning (DL) model has been developed to identify the relationship between a certain type of COVID-19 vaccine (i.e. PFIEZER, JANSSEN, and MODERNA) and the adverse reactions that may occur in vaccinated patients. The adverse reactions under study are the recovery condition, possibility to be hospitalized, and death status. In the first phase of the proposed model, the dataset has been pre-proceesed, while in the second phase, the Pigeon swarm optimization algorithm is used to optimally select the most promising features that affect the performance of the proposed model. The patient's status after vaccination dataset is grouped into three target classes (Death, Hospitalized, and Recovered). In the third phase, Recurrent Neural Network (RNN) is implemented for both each vaccine type and each target class. The results show that the proposed model gives the highest accuracy scores which are 96.031% for the Death target class in the case of PFIEZER vaccination. While in JANSSEN vaccination, the Hospitalized target class has shown the highest performance with an accuracy of 94.7%. Finally, the model has the best performance for the Recovered target class in MODERNA vaccination with an accuracy of 97.794%. Based on the accuracy and the Wilcoxon Signed Rank test, we can conclude that the proposed model is promising for identifying the relationship between the side effects of COVID-19 vaccines and the patient's status after vaccination. The study displayed that certain side effects were increased in patients according to the type of COVID-19 vaccines. Side effects related to CNS and hemopoietic systems demonstrated high values in all studied COVID-19 vaccines. In the frame of precision medicine, these findings can support the medical staff to select the best COVID-19 vaccine based on the medical history of the patient., (© 2023. The Author(s).)

The limited number of ozone monitoring stations imposes uncertainty in various applications, calling for accurate approaches to capturing ozone values in all regions, particularly those with no in-situ measurements. This study uses deep learning (DL) to accurately estimate daily maximum 8-hr average (MDA8) ozone and examines the spatial contribution of several factors on ozone levels over the contiguous U.S. (CONUS) in 2019. A comparison between in-situ observations and DL-estimated MDA8 ozone values shows a correlation coefficient (R) of 0.95, an index of agreement (IOA) of 0.97, and a mean absolute bias (MAB) of 2.79 ppb, highlighting the promising performance of the deep convolutional neural network (Deep-CNN) at estimating surface MDA8 ozone. Spatial cross-validation also confirms the high spatial accuracy of the model, which obtains an R of 0.91, and IOA of 0.96 and an MAB of 3.46 ppb when it is trained and tested on separate stations. To interpret the black-box nature of our DL model, we use Shapley additive explanations (SHAP) to generate a spatial feature contribution map (SFCM), the results of which confirm an advanced ability of Deep-CNN to capture the interactions between most predictor variables and ozone. For instance, the model shows that solar radiation (SRad) SFCM, with higher values, enhances the formation of ozone, particularly in the south and southwestern CONUS. As SRad triggers ozone precursors to produce ozone via photochemical reactions, it increases ozone concentrations. The model also shows that humidity, with its low values, increases ozone concentrations in the western mountainous regions. The negative correlation between humidity and ozone levels can be attributed to factors such as higher ozone decomposition resulting from increased levels of humidity and OH radicals. This study is the first to introduce the SFCM to investigate the spatial role of predictor variables on changes in estimated MDA8 ozone levels., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Objective: As the opioid epidemic continues across the United States, methods are needed to accurately and quickly identify patients at risk for opioid use disorder (OUD). The purpose of this study is to develop two predictive algorithms: one to predict opioid prescription and one to predict OUD., Materials and Methods: We developed an informatics algorithm that trains two deep learning models over patient Electronic Health Records (EHRs) using the MIMIC-III database. We utilize both the structured and unstructured parts of the EHR and show that it is possible to predict both challenging outcomes., Results: Our deep learning models incorporate elements from EHRs to predict opioid prescription with an F1-score of 0.88 ± 0.003 and an AUC-ROC of 0.93 ± 0.002. We also constructed a model to predict OUD diagnosis achieving an F1-score of 0.82 ± 0.05 and AUC-ROC of 0.94 ± 0.008., Discussion: Our model for OUD prediction outperformed prior algorithms for specificity, F1 score and AUC-ROC while achieving equivalent sensitivity. This demonstrates the importance of a) deep learning approaches in predicting OUD and b) incorporating both structured and unstructured data for this prediction task. No prediction models for opioid prescription as an outcome were found in the literature and therefore our model is the first to predict opioid prescribing behavior., Conclusion: Algorithms such as those described in this paper will become increasingly important to understand the drivers underlying this national epidemic., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

A key challenge for artificial intelligence in the legal field is to determine from the text of a party's litigation brief whether, and why, it will succeed or fail. This paper shows a proof-of-concept test case from the United States: predicting outcomes of post-grant inter partes review (IPR) proceedings for invalidating patents. The objectives are to compare decision-tree and deep learning methods, validate interpretability methods, and demonstrate outcome prediction based on party briefs. Specifically, this study compares and validates two distinct approaches: (1) representing documents with term frequency inverse document frequency (TF-IDF), training XGBoost gradient-boosted decision-tree models, and using SHAP for interpretation. (2) Deep learning of document text in context, using convolutional neural networks (CNN) with attention, and comparing LIME and attention visualization for interpretability. The methods are validated on the task of automatically determining case outcomes from unstructured written decision opinions, and then used to predict trial institution or denial based on the patent owner's preliminary response brief. The results show how interpretable deep learning architecture classifies successful/unsuccessful response briefs on temporally separated training and test sets. More accurate prediction remains challenging, likely due to the fact-specific, technical nature of patent cases and changes in applicable law and jurisprudence over time. [ABSTRACT FROM AUTHOR]

Objectives: This paper uses deep (machine) learning techniques to develop and test how motor behaviors, derived from location and movement sensor tracking data, may be associated with falls, delirium, and urinary tract infections (UTIs) in long-term care (LTC) residents., Design: Longitudinal observational study., Setting and Participants: A total of 23 LTC residents (81,323 observations) with cognitive impairment or dementia in 2 northeast Department of Veterans Affairs LTC facilities., Methods: More than 18 months of continuous (24/7) monitoring of motor behavior and activity levels used objective radiofrequency identification sensor data to track and record movement data. Occurrence of acute events was recorded each week. Unsupervised deep learning models were used to classify motor behaviors into 5 clusters; supervised decision tree algorithms used these clusters to predict acute health events (falls, delirium, and UTIs) the week before the week of the event., Results: Motor behaviors were classified into 5 categories (Silhouette score = 0.67), and these were significantly different from each other. Motor behavior classifications were sensitive and specific to falls, delirium, and UTI predictions 1 week before the week of the event (sensitivity range = 0.88-0.91; specificity range = 0.71-0.88)., Conclusion and Implications: Intraindividual changes in motor behaviors predict some of the most common and detrimental acute events in LTC populations. Study findings suggest real-time locating system sensor data and machine learning techniques may be used in clinical applications to effectively prevent falls and lead to the earlier recognition of risk for delirium and UTIs in this vulnerable population., (Copyright © 2022 AMDA – The Society for Post-Acute and Long-Term Care Medicine. All rights reserved.)

Characterizing the electric energy curve can improve the energy efficiency of existing buildings without any structural change and is the basis for controlling and optimizing building performance. Artificial Intelligence (AI) techniques show much potential due to their accuracy and malleability in the field of pattern recognition, and using these models it is possible to adjust the building services in real time. Thus, the objective of this paper is to determine the AI technique that best forecasts electrical loads. The suggested techniques are random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), long short-term memory (LSTM), and temporal convolutional network (Conv-1D). The conducted research applies a methodology that considers the bias and variance of the models, enhancing the robustness of the most suitable AI techniques for modeling and forecasting the electricity consumption in buildings. These techniques are evaluated in a single-family dwelling located in the United States. The performance comparison is obtained by analyzing their bias and variance by using a 10-fold cross-validation technique. By means of the evaluation of the models in different sets, i.e., validation and test sets, their capacity to reproduce the results and the ability to properly forecast on future occasions is also evaluated. The results show that the model with less dispersion, both in the validation set and test set, is LSTM. It presents errors of −0.02% of nMBE and 2.76% of nRMSE in the validation set and −0.54% of nMBE and 4.74% of nRMSE in the test set. [ABSTRACT FROM AUTHOR]

The prolonged COVID-19 pandemic has tied up significant medical resources, and its management poses a challenge for the public health care decision making. Accurate predictions of the hospitalizations are crucial for the decision makers to make informed decision for the medical resource allocation. This paper proposes a method named County Augmented Transformer (CAT). To generate accurate predictions of four-week-ahead COVID-19 related hospitalizations for every states in the United States. Inspired by the modern deep learning techniques, our method is based on a self-attention model (known as the transformer model) that is actively used in Natural Language Processing. Our transformer based model can capture both short-term and long-term dependencies within the time series while enjoying computational efficiency. Our model is a data based approach that utilizes the publicly available information including the COVID-19 related number of confirmed cases, deaths, hospitalizations data, and the household median income data. Our numerical experiments demonstrate the strength and the usability of our model as a potential tool for assisting the medical resources allocation. [ABSTRACT FROM AUTHOR]

This paper presents a deep learning-aided deterioration modeling approach for highway bridge components (i.e., decks, superstructures, and substructures). The method trains a set of deep learning models for the maximum likelihood estimation of parameters in a Markov chain. The likelihood function is based on observed numbers of transitions between different condition states. The deep learning is leveraged for efficient representation of various factors that influence the deterioration process. Aided by deep learning, the proposed method is suitable for deterioration modeling using large-scale and high-dimensional bridge datasets. The study demonstrates an application of this approach using the historical National Bridge Inventory database from 1993 to 2019. The Convolutional Neural Network is adopted as the deep learning model. The proposed method is validated with ten-fold cross-validation that encompasses a nationwide selection of 88,596, 101,414, and 96,244 bridge decks, superstructures, and substructures, respectively. The validation shows this approach achieved a robust and low prediction error with the maximum mean-squared-error near 0.5 in a 26-years-forecast. The proposed method is also compared with the conventional Artificial Neural Network and four selected Markov-chain based deterioration modeling approaches. The study shows this approach can be a promising data-driven tool for deterioration modeling of highway bridge components. [ABSTRACT FROM AUTHOR]

Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have, in turn, led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgent need to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. To that end, in this paper, we present our work on integrating multiple data sources into SmokeyNet, a deep learning model using spatiotemporal information to detect smoke from wildland fires. We present Multimodal SmokeyNet and SmokeyNet Ensemble for multimodal wildland fire smoke detection using satellite-based fire detections, weather sensor measurements, and optical camera images. An analysis is provided to compare these multimodal approaches to the baseline SmokeyNet in terms of accuracy metrics, as well as time-to-detect, which is important for the early detection of wildfires. Our results show that incorporating weather data in SmokeyNet improves performance numerically in terms of both F1 and time-to-detect over the baseline with a single data source. With a time-to-detect of only a few minutes, SmokeyNet can be used for automated early notification of wildfires, providing a useful tool in the fight against destructive wildfires. [ABSTRACT FROM AUTHOR]

Background: Deep learning-assisted eye disease diagnosis technology is increasingly applied in eye disease screening. However, no research has suggested the prerequisites for health care service providers and residents willing to use it., Objective: The aim of this paper is to reveal the preferences of health care service providers and residents for using artificial intelligence (AI) in community-based eye disease screening, particularly their preference for accuracy., Methods: Discrete choice experiments for health care providers and residents were conducted in Shanghai, China. In total, 34 medical institutions with adequate AI-assisted screening experience participated. A total of 39 medical staff and 318 residents were asked to answer the questionnaire and make a trade-off among alternative screening strategies with different attributes, including missed diagnosis rate, overdiagnosis rate, screening result feedback efficiency, level of ophthalmologist involvement, organizational form, cost, and screening result feedback form. Conditional logit models with the stepwise selection method were used to estimate the preferences., Results: Medical staff preferred high accuracy: The specificity of deep learning models should be more than 90% (odds ratio [OR]=0.61 for 10% overdiagnosis; P<.001), which was much higher than the Food and Drug Administration standards. However, accuracy was not the residents' preference. Rather, they preferred to have the doctors involved in the screening process. In addition, when compared with a fully manual diagnosis, AI technology was more favored by the medical staff (OR=2.08 for semiautomated AI model and OR=2.39 for fully automated AI model; P<.001), while the residents were in disfavor of the AI technology without doctors' supervision (OR=0.24; P<.001)., Conclusions: Deep learning model under doctors' supervision is strongly recommended, and the specificity of the model should be more than 90%. In addition, digital transformation should help medical staff move away from heavy and repetitive work and spend more time on communicating with residents., (©Senlin Lin, Liping Li, Haidong Zou, Yi Xu, Lina Lu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.09.2022.)

The development of a new type of international relations is the advancement and improvement of diplomatic thinking among contemporary nations. It also serves as a crucial yardstick for assessing the future global pattern and the direction of order changes. Proper interaction between major powers can foster the growth of new international relations and has a significant impact on advancing global cooperation and the promotion of human peace. The goal of this essay is to examine how friendly interactions between major powers have affected the development of new international relations. A deep learning network model is presented for this purpose. The deep learning model was used to identify the emotions of the survey results, analyze each person's emotional tendencies, and summarize and compare the data. Relevant questionnaire surveys were conducted using the online questionnaire survey method on individuals in various countries. The survey results in this paper demonstrate that 96.5 percent of Chinese, 89.3 percent of Russians, and 81.6 percent of Americans support friendly relations between major nations. Only a very small percentage of the investigators supported hostile relations, with their support being 1.06 percent, 3.11 percent, and 2.94 percent, respectively. Therefore, creating a win-win partnership between major powers is exactly what the people of all nations are calling for. In contrast to the past, it is no longer hostile and violent. People anticipate that more great powers will coexist peacefully., Competing Interests: The author does not have any possible conflicts of interest., (Copyright © 2022 Yanhong Ma.)

Electricity usage of buildings (including offices, malls, and residential apartments) represents a significant portion of a nation's energy expenditure and carbon footprint. In the United States, the buildings' appliances consume 72% of the total produced electricity approximately. In this regard, cyber‐physical system (CPS) researchers have put forth associated research questions to reduce cyber‐physical building environment energy consumption by minimizing the energy dissipation while securing occupants' comfort. Some of the questions in CPS building include finding the optimal HVAC control, monitoring appliances' energy usage, detecting insulation problems, estimating the occupants' number and activities, managing thermal comfort, intelligently interacting with the smart grid. Various machine learning (ML) applications have been studied in recent CPS researches to improve building energy efficiency by addressing these questions. In this paper, we comprehensively review and report on the contemporary applications of ML algorithms such as deep learning, transfer learning, active learning, reinforcement learning, and other emerging techniques that propose and envision to address the above challenges in the CPS building environment. Finally, we conclude this article by discussing diverse existing open questions and prospective future directions in the CPS building environment research. This article is categorized under:Technologies > Machine LearningTechnologies > Reinforcement Learning [ABSTRACT FROM AUTHOR]

Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited-use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep learning approaches. The performance of multiple deep learning models is thoroughly examined to discover which algorithm surpasses the others, followed by an investigation of the network's prediction accuracy. The simulation outcomes indicate that automated prediction models can predict colon cancer patient survival. Deep autoencoders displayed the best performance outcomes attaining 97% accuracy and 95% area under curve-receiver operating characteristic (AUC-ROC)., Competing Interests: There is no conflict of interest., (Copyright © 2022 Surbhi Gupta et al.)

In this paper, Transfer Learning is used in LSTM networks to forecast new COVID cases and deaths. Models trained in data from early COVID infected countries like Italy and the United States are used to forecast the spread in other countries. Single and multistep forecasting is performed from these models. The results from these models are tested with data from Germany, France, Brazil, India, and Nepal to check the validity of the method. The obtained forecasts are promising and can be helpful for policymakers coping with the threats of COVID-19., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)

Human falls, especially for elderly people, can cause serious injuries that might lead to permanent disability. Approximately 20–30% of the aged people in the United States who experienced fall accidents suffer from head trauma, injuries, or bruises. Fall detection is becoming an important public healthcare problem. Timely and accurate fall incident detection could enable the instant delivery of medical services to the injured. New advances in vision-based technologies, including deep learning, have shown significant results in action recognition, where some focus on the detection of fall actions. In this paper, we propose an automatic human fall detection system using multi-stream convolutional neural networks with fusion. The system is based on a multi-level image-fusion approach of every 16 frames of an input video to highlight movement differences within this range. This results of four consecutive preprocessed images are fed to a new proposed and efficient lightweight multi-stream CNN model that is based on a four-branch architecture (4S-3DCNN) that classifies whether there is an incident of a human fall. The evaluation included the use of more than 6392 generated sequences from the Le2i fall detection dataset, which is a publicly available fall video dataset. The proposed method, using three-fold cross-validation to validate generalization and susceptibility to overfitting, achieved a 99.03%, 99.00%, 99.68%, and 99.00% accuracy, sensitivity, specificity, and precision, respectively. The experimental results prove that the proposed model outperforms state-of-the-art models, including GoogleNet, SqueezeNet, ResNet18, and DarkNet19, for fall incident detection. [ABSTRACT FROM AUTHOR]

Due to the strong propagation causality of delays between airports, this paper proposes a delay prediction model based on a deep graph neural network to study delay prediction from the perspective of an airport network. We regard airports as nodes of a graph network and use a directed graph network to construct airports' relationship. For adjacent airports, weights of edges are measured by the spherical distance between them, while the number of flight pairs between them is utilized for airports connected by flights. On this basis, a diffusion convolution kernel is constructed to capture characteristics of delay propagation between airports, and it is further integrated into the sequence-to-sequence LSTM neural network to establish a deep learning framework for delay prediction. We name this model as deep graph-embedded LSTM (DGLSTM). To verify the model's effectiveness and superiority, we utilize the historical delay data of 325 airports in the United States from 2015 to 2018 as the model training set and test set. The experimental results suggest that the proposed method is superior to the existing mainstream methods in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

Accurate measurement of solar irradiance is of great significance in many applications, such as climatology, energy and engineering. Deep learning models have achieved good results in solar irradiance prediction for a single site. However, most studies take meteorological parameters as the model inputs and the irradiance values as the model outputs. Because different regions have different climates, only considering the relationship between meteorological parameters and irradiance has limitations. This paper presents a novel scheme for forecasting irradiance. The method considers the hourly irradiance prediction model to be the superposition of two parts: a daily average irradiance prediction model and the irradiance amplitude prediction model. Two submodels were constructed by using deep bidirectional long short-term memory (BiLSTM) network. For the task of irradiance prediction for 25 stations located in the United States, which are located in five different climates, the proposed method performs best for 21 stations (84%) in terms of the root mean square error, 18 stations (72%) in terms of the mean absolute error, and 17 stations (68%) in terms of the coefficient of determination. Moreover, the method adopted in this study displays a stronger irradiance prediction ability than the traditional methods for 80% of the climates included in the experiment. [ABSTRACT FROM AUTHOR]

Wind power is a popular renewable energy source, and the accurate prediction of wind speed plays an important role in improving the power generation efficiency of wind turbines and ensuring the normal operation of wind power equipment. Due to the instability and randomness of wind speed, it is difficult to achieve accurate prediction by traditional prediction methods. To improve the power generation efficiency of wind turbines and realize the predictability of wind speed, a hybrid wind speed prediction model based on GRUs (gated recurrent units) was constructed in this paper based on a deep neural network and feature extraction method. The hybrid model feature extraction module was implemented based on a combination of Tsfresh (a python package for time series feature extraction) and sparse PCA (sparse principal component analysis), and the network structure and other hyperparameters of the GRU module were determined through experiments. The model was validated using actual wind measurement data from a wind farm on the west coast of the United States. The results showed that the proposed model had less computational time and higher computational accuracy than the SARIMAX (seasonal auto-regressive integrated moving average with exogenous factors) and LSTM (long short-term memory) models. [ABSTRACT FROM AUTHOR]

This paper is devoted to the relationship between news sentiment and changes in housing market movements. It provides a novel and straightforward approach to account for heterogeneous expectations of market actors within a probabilistic framework utilizing machine learning. Our novel sentiment index shows a persistent and statistically significant explanatory power for the prediction of the housing market, in contrast to common dictionary approaches. This holds for news headlines and abstracts and different definitions of sentiment indices. Our results can be regarded as the first sentiment-based evidence of heterogeneous actors in the housing market and underline the importance of different expectations for measuring non-fundamental drivers. [ABSTRACT FROM AUTHOR]

Empirical accounting is a relatively new course. It was first produced in the United States in the 1960s. After years of research and development, a relatively complete theoretical system has been formed. Empirical accounting teaching is a new content of accounting higher education in our country, which plays an important role in popularizing and improving the empirical study of accounting in our country. After investigating and analysing the empirical accounting teaching in most colleges in my country, this paper summarizes and analyzes the current status of empirical accounting teaching and discusses the optimization measures of its empirical accounting teaching mode based on the Internet of Things and deep learning. The experimental results show that this method can effectively improve students' learning efficiency and teachers' teaching satisfaction. [ABSTRACT FROM AUTHOR]

Recent years have witnessed wider prevalence of vertebral column pathologies due to lifestyle changes, sedentary behaviors, or injuries. Spondylolisthesis and scoliosis are two of the most common ailments with an incidence of 5% and 3% in the United States population, respectively. Both of these abnormalities can affect children at a young age and, if left untreated, can progress into severe pain. Moreover, severe scoliosis can even lead to lung and heart problems. Thus, early diagnosis can make it easier to apply remedies/interventions and prevent further disease progression. Current diagnosis methods are based on visual inspection by physicians of radiographs and/or calculation of certain angles (e.g., Cobb angle). Traditional artificial intelligence-based diagnosis systems utilized these parameters to perform automated classification, which enabled fast and easy diagnosis supporting tools. However, they still require the specialists to perform error-prone tedious measurements. To this end, automated measurement tools were proposed based on processing techniques of X-ray images. In this paper, we utilize advances in deep transfer learning to diagnose spondylolisthesis and scoliosis from X-ray images without the need for any measurements. We collected raw data from real X-ray images of 338 subjects (i.e., 188 scoliosis, 79 spondylolisthesis, and 71 healthy). Deep transfer learning models were developed to perform three-class classification as well as pair-wise binary classifications among the three classes. The highest mean accuracy and maximum accuracy for three-class classification was 96.73% and 98.02%, respectively. Regarding pair-wise binary classification, high accuracy values were achieved for most of the models (i.e., > 98%). These results and other performance metrics reflect a robust ability to diagnose the subjects' vertebral column disorders from standard X-ray images. The current study provides a supporting tool that can reasonably help the physicians make the correct early diagnosis with less effort and errors, and reduce the need for surgical interventions. [ABSTRACT FROM AUTHOR]

Grant support (GS) in the MEDLINE database refers to funding agencies and contract numbers. It is important for funding organizations to track their funding outcomes from the GS information. As such, how to accurately and automatically extract funding information from biomedical literature is challenging. In this paper, we present a pipeline system called GrantExtractor that is able to accurately extract GS information from fulltext biomedical literature. GrantExtractor effectively integrates several advanced machine learning techniques. In particular, we use a sentence classifier to identify funding sentences from articles first. A bi-directional LSTM and the CRF layer (BiLSTM-CRF), and pattern matching are then used to extract entities of grant numbers and agencies from these identified funding sentences. After removing noisy numbers by a multi-class model, we finally match each grant number with its corresponding agency. Experimental results on benchmark datasets have demonstrated that GrantExtractor clearly outperforms all baseline methods. It is further evident that GrantExtractor won the first place in Task 5C of 2017 BioASQ challenge, with achieving the Micro-recall of 0.9526 for 22,610 articles. Moreover, GrantExtractor has achieved the Micro F-measure score as high as 0.90 in extracting grant pairs.

Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of cancer death in the United States. Predicting tumors like PDACs (including both classification and segmentation) from medical images by deep learning is becoming a growing trend, but usually a large number of annotated data are required for training, which is very labor-intensive and time-consuming. In this paper, we consider a partially supervised setting, where cheap image-level annotations are provided for all the training data, and the costly per-voxel annotations are only available for a subset of them. We propose an Inductive Attention Guidance Network (IAG-Net) to jointly learn a global image-level classifier for normal/PDAC classification and a local voxel-level classifier for semi-supervised PDAC segmentation. We instantiate both the global and the local classifiers by multiple instance learning (MIL), where the attention guidance, indicating roughly where the PDAC regions are, is the key to bridging them: For global MIL based normal/PDAC classification, attention serves as a weight for each instance (voxel) during MIL pooling, which eliminates the distraction from the background; For local MIL based semi-supervised PDAC segmentation, the attention guidance is inductive, which not only provides bag-level pseudo-labels to training data without per-voxel annotations for MIL training, but also acts as a proxy of an instance-level classifier. Experimental results show that our IAG-Net boosts PDAC segmentation accuracy by more than 5% compared with the state-of-the-arts. [ABSTRACT FROM AUTHOR]

Background: The growing interest suggests that the widespread application of radiomics has facilitated the development of neurological disease diagnosis, prognosis, and classification. The application of artificial intelligence methods in radiomics has increasingly achieved outstanding prediction results in recent years. However, there are few studies that have systematically analyzed this field through bibliometrics. Our destination is to study the visual relationships of publications to identify the trends and hotspots in radiomics research and encourage more researchers to participate in radiomics studies. Methods: Publications in radiomics in the field of neurological disease research can be retrieved from the Web of Science Core Collection. Analysis of relevant countries, institutions, journals, authors, keywords, and references is conducted using Microsoft Excel 2019, VOSviewer, and CiteSpace V. We analyze the research status and hot trends through burst detection. Results: On October 23, 2022, 746 records of studies on the application of radiomics in the diagnosis of neurological disorders were retrieved and published from 2011 to 2023. Approximately half of them were written by scholars in the United States, and most were published in Frontiers in Oncology, European Radiology, Cancer, and SCIENTIFIC REPORTS. Although China ranks first in the number of publications, the United States is the driving force in the field and enjoys a good academic reputation. NORBERT GALLDIKS and JIE TIAN published the most relevant articles, while GILLIES RJ was cited the most. RADIOLOGY is a representative and influential journal in the field. "Glioma" is a current attractive research hotspot. Keywords such as "machine learning," "brain metastasis," and "gene mutations" have recently appeared at the research frontier. Conclusion: Most of the studies focus on clinical trial outcomes, such as the diagnosis, prediction, and prognosis of neurological disorders. The radiomics biomarkers and multi-omics studies of neurological disorders may soon become a hot topic and should be closely monitored, particularly the relationship between tumor-related non-invasive imaging biomarkers and the intrinsic micro- environment of tumors. [ABSTRACT FROM AUTHOR]

Obtaining reliable and high-quality wind speed probability forecast results is of great significance to wind energy utilization and power system management. In this paper, multi-network deep ensemble method, which combines the intelligent optimization algorithm and deep ensemble method, is proposed to deal with the probabilistic prediction problems. This method can effectively integrate a variety of different deep learning neural networks and provide reliable uncertainty estimates for prediction. Furthermore, spatiotemporal multi-network deep ensemble model, which employs multi-network deep ensemble method, is proposed to deal with the probabilistic spatiotemporal wind speed forecasting problems. In the model, three advanced convolutional recurrent neural networks are integrated to capture spatiotemporal information from the underlying meteorological variables. Intelligent optimization algorithm is used to assign weights to each network in the ensemble. In addition, an uncertainty quantification method, which quantify the uncertainty by adjusting the network structure and optimize the uncertainty by utilizing the truncated negative log-likelihood scoring rule, is introduced to provide reliable probability forecasts. The proposed model is applied to a real-world case in the United States. The test results demonstrate that spatiotemporal multi-network deep ensemble model can not only provide high-precision point prediction results, but also provide suitable interval predictions and reliable probability prediction results. Moreover, the impact of input features on model prediction results is also evaluated. [ABSTRACT FROM AUTHOR]

Background: Prescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging-requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter.Methods: We experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority "abuse/misuse" class.Results: Our proposed fusion-based model performs significantly better than the best traditional model (F1-score [95% CI]: 0.67 [0.64-0.69] vs. 0.45 [0.42-0.48]). We illustrate, via experimentation using varying training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter.Conclusions: BERT, BERT-like and fusion-based models outperform traditional machine learning and deep learning models, achieving substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. Several challenges associated with the lack of context and the nature of social media language need to be overcome to further improve BERT and BERT-like models. These experimental driven challenges are represented as potential future research directions. [ABSTRACT FROM AUTHOR]

In this paper, we propose bag of adversarial features (BAFs) for identifying mild traumatic brain injury (MTBI) patients from their diffusion magnetic resonance images (MRIs) (obtained within one month of injury) by incorporating unsupervised feature learning techniques. MTBI is a growing public health problem with an estimated incidence of over 1.7 million people annually in USA. Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking. Unlike most of the previous works, which use hand-crafted features extracted from different parts of brain for MTBI classification, we employ feature learning algorithms to learn more discriminative representation for this task. A major challenge in this field thus far is the relatively small number of subjects available for training. This makes it difficult to use an end-to-end convolutional neural network to directly classify a subject from MRIs. To overcome this challenge, we first apply an adversarial auto-encoder (with convolutional structure) to learn patch-level features, from overlapping image patches extracted from different brain regions. We then aggregate these features through a bag-of-words approach. We perform an extensive experimental study on a dataset of 227 subjects (including 109 MTBI patients, and 118 age and sex-matched healthy controls) and compare the bag-of-deep-features with several previous approaches. Our experimental results show that the BAF significantly outperforms earlier works relying on the mean values of MR metrics in selected brain regions. [ABSTRACT FROM AUTHOR]

Ordinary differential equations (ODEs) are widely used to model complex dynamics that arise in biology, chemistry, engineering, finance, physics, etc. Calibration of a complicated ODE system using noisy data is generally challenging. In this paper, we propose a two-stage nonparametric approach to address this problem. We first extract the de-noised data and their higher order derivatives using boundary kernel method, and then feed them into a sparsely connected deep neural network with rectified linear unit (ReLU) activation function. Our method is able to recover the ODE system without being subject to the curse of dimensionality and the complexity of the ODE structure. We have shown that our method is consistent if the ODE possesses a general modular structure with each modular component involving only a few input variables, and the network architecture is properly chosen. Theoretical properties are corroborated by an extensive simulation study that also demonstrates the effectiveness of the proposed method in finite samples. Finally, we use our method to simultaneously characterize the growth rate of COVID-19 cases from the 50 states of the United States. • ODEs are used for modeling complex dynamics in various fields. • Calibrating complex ODE models with noisy data is difficult. • A two-stage nonparametric approach is proposed based on deep learning. • The approach is proven to be consistent. [ABSTRACT FROM AUTHOR]

Personal and family history of suicidal thoughts and behaviors (PSH and FSH, respectively) are significant risk factors associated with suicides. Research is limited in automatic identification of such data from clinical notes in Electronic Health Records. This study developed deep learning (DL) tools utilizing transformer models (Bio_ClinicalBERT and GatorTron) to detect PSH and FSH in clinical notes derived from three academic medical centers, and compared their performance with a rule-based natural language processing tool. For detecting PSH, the rule-based approach obtained an F1-score of 0.75 ± 0.07, while the Bio_ClinicalBERT and GatorTron DL tools scored 0.83 ± 0.09 and 0.84 ± 0.07, respectively. For detecting FSH, the rule-based approach achieved an F1-score of 0.69 ± 0.11, compared to 0.89 ± 0.10 for Bio_ClinicalBERT and 0.92 ± 0.07 for GatorTron. Across sites, the DL tools identified more than 80% of patients at elevated risk for suicide who remain undiagnosed and untreated. [ABSTRACT FROM AUTHOR]

Several reservoirs across the United States are filling with sediment, which jeopardizes their functionality and increases maintenance costs. USACE developed the Reservoir Sedimentation Information (RSI) system to assess reservoir aggradation and track dam operation suitability for water resource management and dam safety. The RSI data set contains historical elevation-capacity data for approximately 400 dams (excluding navigation structures), which correspond to less than 1% of dams across the United States. Thus, there is a critical need to develop methods for estimating reservoir sedimentation for unmonitored sites. The goal of this project was to create a generalized method for estimating reservoir sedimentation rates using reservoir design information and watershed data. To meet this objective, geospatial tools were used to build a refined composite data set to complement the RSI system's data with precipitation and watershed characteristics. Nine deep learning models were then used on the benchmark data set to determine its accuracy at predicting capacity loss for the RSI reservoirs: four supervised machine learning models, four deep neural network (DNN) models, and a multilinear power regression model. A DNN model, containing a progressively increasing node and layer construction, was deemed the most accurate, with R2 values from its calibration and validation data sets being 0.83 and 0.70, respectively. The best model was recalibrated over the entire data set, which showed greater accuracy on the prediction of the RSI reservoir's capacity loss, with an R2 of 0.81. This predictive model could be used to evaluate the capacity loss of unmonitored reservoirs, forecast sedimentation rates under future climate conditions, and identify reservoirs with the highest risk of losing functionality. Practical Applications: Many communities depend on reservoirs for a variety of socioeconomic benefits, such as providing reliable water sources and flood mitigation. Rivers entering reservoirs are a constant source of silt, sand, and gravel particles (i.e., sediments) that deposit slowly, filling the reservoirs over time, thus reducing their volume capacity and effectiveness. Surveys to measure reservoir capacities are labor intensive and expensive and many sites go unmonitored. This study provides prediction tools to estimate capacity loss over time using data derived from publicly available data sources (e.g., digital elevation models, monthly precipitation data, and the National Inventory of Dams). A key role of this tool is to forecast reservoir capacity loss over time under varying climate change conditions and relate the associated sedimentation processes to local and regional conditions. The tool can also be applied broadly to hindcast capacity loss for reservoirs with or without prior surveys for identifying high-risk sites that should be investigated further. USACE plans to use these prediction tools with the RSI database to conduct a national assessment of reservoir impacts, which will inform distributions of federal resources to address water security concerns related to reservoir sedimentation. [ABSTRACT FROM AUTHOR]

Developing wind speed forecasting is a prerequisite for the safe and effective utilization of wind power. In this study, an enhanced spatiotemporal wind speed forecasting model is proposed for short-term wind speed prediction, which consists of convolutional long short-term memory network, quantile regression, and error correction modules. The model makes use of the powerful time-series mining ability of long short-term memory (LSTM) and the measurement of variable uncertainty by quantile regression so that the model has the advantages of advanced certainty and uncertainty prediction at the same time. In addition, the error correction module is added to further improve the forecast accuracy. The proposed model has been validated in three large-scale regions in the United States and compared with three other state-of-the-art models. In the deterministic prediction, compared with the best-performing LSTM among the baseline models, the mean absolute error and root mean square error are reduced by 30.71% and 26.99%, respectively. In probabilistic prediction, the proposed model performs better than Gaussian process regression with higher reliability. The results of statistical testing demonstrate that the proposed model can obtain both accurate deterministic prediction and reliable probabilistic prediction. This indicates that the model has advantages in the spatiotemporal prediction of large-scale regions. [ABSTRACT FROM AUTHOR]

Outlier detection plays a critical role in building operation optimization and data quality maintenance. However, existing methods often struggle with the complexity and variability of building energy data, leading to poorly generalized and explainable results. To address the gap, this study introduces a novel Vision-based Outlier Detection (VOD) approach, leveraging computer vision models to spot outliers in the building energy records. The models are trained to identify outliers by analyzing the load shapes in 2D time series plots derived from the energy data. The VOD approach is tested on four years of workday time-series electricity consumption data from 290 commercial buildings in the United States. Two distinct models are developed for different usage purposes, namely a classification model for broad-level outlier detection and an object detection model for the demands of precise pinpointing of outliers. The classification model is also interpreted via Grad-CAM to enhance its usage reliability. The classification model achieves an F1 score of 0.88, and the object detection model achieves an Average Precision (AP) of 0.84. VOD is a very efficient path to identifying energy consumption outliers in building operations, paving the way for the enhancement of building energy data quality, operation efficiency, and energy savings. [ABSTRACT FROM AUTHOR]

Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to analyze publications on artificial intelligence (AI) for falls in older people from a bibliometric perspective. Methods: The Web of Science database was searched for titles of English‐language articles containing the words "artificial intelligence," "deep learning," "machine learning," "natural language processing,", "neural artificial network," "fall," "geriatric," "elderly," "aging," "older," and "old age." An R‐based application (Biblioshiny for bibliometrics) and VOSviewer software were used for analysis. Results: Thirty‐seven English articles published between 2018 and 2024 were included. The year 2023 is the year with the most publications with 16 articles. The most productive research field was "Engineering Electrical Electronic" with seven articles. The most productive country was the United States, followed by China. The most common words were "injuries," "people," and "risk factors." Conclusion: Publications on AI and falls in the elderly are both few in number and the number of publications has increased in recent years. Future research should include relevant analyses in scientific databases, such as Scopus and PubMed. [ABSTRACT FROM AUTHOR]

Noncommunicable diseases (NCD), such as obesity, diabetes, and cardiovascular disease, are defining healthcare challenges of the 21st century. Medical infrastructure, which for decades sought to reduce the incidence and severity of communicable diseases, has proven insufficient in meeting the intensive, long‐term monitoring needs of many NCD disease patient groups. In addition, existing portable devices with rigid electronics are still limited in clinical use due to unreliable data, limited functionality, and lack of continuous measurement ability. Here, a wearable system for at‐home cardiovascular monitoring of postpartum women—a group with urgently unmet NCD needs in the United States—using a cloud‐integrated soft sternal device with conformal nanomembrane sensors is introduced. A supporting mobile application provides device data to a custom cloud architecture for real‐time waveform analytics, including medical device‐grade blood pressure prediction via deep learning, and shares the results with both patient and clinician to complete a robust and highly scalable remote monitoring ecosystem. Validated in a month‐long clinical study with 20 postpartum Black women, the system demonstrates its ability to remotely monitor existing disease progression, stratify patient risk, and augment clinical decision‐making by informing interventions for groups whose healthcare needs otherwise remain unmet in standard clinical practice. [ABSTRACT FROM AUTHOR]

Background. MRI is an important tool for accurate detection and targeted biopsy of prostate lesions. However, the imaging appearances of some prostate cancers are similar to those of the surrounding normal tissue on MRI, which are referred to as MRI-invisible prostate cancers (MIPCas). The detection of MIPCas remains challenging and requires extensive systematic biopsy for identification. In this study, we developed a weakly supervised UNet (WSUNet) to detect MIPCas. Methods. The study included 777 patients (training set: 600; testing set: 177), all of them underwent comprehensive prostate biopsies using an MRI-ultrasound fusion system. MIPCas were identified in MRI based on the Gleason grade (≥7) from known systematic biopsy results. Results. The WSUNet model underwent validation through systematic biopsy in the testing set with an AUC of 0.764 (95% CI: 0.728-0.798). Furthermore, WSUNet exhibited a statistically significant precision improvement of 91.3% (p < 0.01) over conventional systematic biopsy methods in the testing set. This improvement resulted in a substantial 47.6% (p < 0.01) decrease in unnecessary biopsy needles, while maintaining the same number of positively identified cores as in the original systematic biopsy. Conclusions. In conclusion, the proposed WSUNet could effectively detect MIPCas, thereby reducing unnecessary biopsies. [ABSTRACT FROM AUTHOR]

The rising severity and frequency of wildfires in recent years in the United States have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution monitoring capabilities. Satellites are one of the tools that can be used for wildfire monitoring. However, none of the currently available satellite systems provide both high temporal and spatial resolution. For example, GOES-17 geostationary satellite fire products have high temporal (1–5 min) but low spatial resolution (≥2 km), and VIIRS polar orbiter satellite fire products have low temporal (~12 h) but high spatial resolution (375 m). This work aims to leverage currently available satellite data sources, such as GOES and VIIRS, along with deep learning (DL) advances to achieve an operational high-resolution, both spatially and temporarily, wildfire monitoring tool. Specifically, this study considers the problem of increasing the spatial resolution of high temporal but low spatial resolution GOES-17 data products using low temporal but high spatial resolution VIIRS data products. The main idea is using an Autoencoder DL model to learn how to map GOES-17 geostationary low spatial resolution satellite images to VIIRS polar orbiter high spatial resolution satellite images. In this context, several loss functions and DL architectures are implemented and tested to predict both the fire area and the corresponding brightness temperature. These models are trained and tested on wildfire sites from 2019 to 2021 in the western U.S. The results indicate that DL models can improve the spatial resolution of GOES-17 images, leading to images that mimic the spatial resolution of VIIRS images. Combined with GOES-17 higher temporal resolution, the DL model can provide high-resolution near-real-time wildfire monitoring capability as well as semi-continuous wildfire progression maps. [ABSTRACT FROM AUTHOR]