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Background: Dementia is a cognitive decline that leads to the progressive deterioration of an individual's ability to perform daily activities independently. As a result, a considerable amount of time and resources are spent on caretaking. Early detection of dementia can significantly reduce the effort and resources needed for caretaking. Aims: This research proposes an approach for assessing cognitive decline by analysing speech data, specifically focusing on speech relevance as a crucial indicator for memory recall. Methods & Procedures: This is a cross‐sectional, online, self‐administered. The proposed method used deep learning architecture based on transformers, with BERT (Bidirectional Encoder Representations from Transformers) and Sentence‐Transformer to derive encoded representations of speech transcripts. These representations provide contextually descriptive information that is used to analyse the relevance of sentences in their respective contexts. The encoded information is then compared using cosine similarity metrics to measure the relevance of uttered sequences of sentences. The study uses the Pitt Corpus Dementia dataset for experimentation, which consists of speech data from individuals with and without dementia. The accuracy of the proposed multi‐QA‐MPNet (Multi‐Query Maximum Inner Product Search Pretraining) model is compared with other pretrained transformer models of Sentence‐Transformer. Outcomes & Results: The results show that the proposed approach outperforms the other models in capturing context level information, particularly semantic memory. Additionally, the study explores the suitability of different similarity measures to evaluate the relevance of uttered sequences of sentences. The experimentation reveals that cosine similarity is the most appropriate measure for this task. Conclusions & Implications: This finding has significant implications for the early warning signs of dementia, as it suggests that cosine similarity metrics can effectively capture the semantic relevance of spoken language. The persistent cognitive decline over time acts as one of the indicators for prevalence of dementia. Additionally early dementia could be recognised by analysis on other modalities like speech and brain images. WHAT THIS PAPER ADDS: What is already known on this subject: It is already known that speech‐ and language‐based detection methods can be useful for dementia diagnosis, as language difficulties are often early signs of the disease. Additionally, deep learning algorithms have shown promise in detecting and diagnosing dementia through analysing large datasets, particularly in speech‐ and language‐based detection methods. However, further research is needed to validate the performance of these algorithms on larger and more diverse datasets and to address potential biases and limitations. What this paper adds to existing knowledge: This study presents a unique and effective approach for cognitive decline assessment through analysing speech data. The study provides valuable insights into the importance of context and semantic memory in accurately detecting the potential in dementia and demonstrates the applicability of deep learning models for this purpose. The findings of this study have important clinical implications and can inform future research and development in the field of dementia detection and care. What are the potential or actual clinical implications of this work?: The proposed approach for cognitive decline assessment using speech data and deep learning models has significant clinical implications. It has the potential to improve the accuracy and efficiency of dementia diagnosis, leading to earlier detection and more effective treatments, which can improve patient outcomes and quality of life. [ABSTRACT FROM AUTHOR]

Despite the fact that machine learning (ML) algorithms to construct predictive models for pressure injury development are widely reported, the performance of the model remains unknown. The goal of the review was to systematically appraise the performance of ML models in predicting pressure injury. PubMed, Embase, Cochrane Library, Web of Science, CINAHL, Grey literature and other databases were systematically searched. Original journal papers were included which met the inclusion criteria. The methodological quality was assessed independently by two reviewers using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta‐analysis was performed with Metadisc software, with the area under the receiver operating characteristic curve, sensitivity and specificity as effect measures. Chi‐squared and I2 tests were used to assess the heterogeneity. A total of 18 studies were included for the narrative review, and 14 of them were eligible for meta‐analysis. The models achieved excellent pooled AUC of 0.94, sensitivity of 0.79 (95% CI [0.78–0.80]) and specificity of 0.87 (95% CI [0.88–0.87]). Meta‐regressions did not provide evidence that model performance varied by data or model types. The present findings indicate that ML models show an outstanding performance in predicting pressure injury. However, good‐quality studies should be conducted to verify our results and confirm the clinical value of ML in pressure injury development. [ABSTRACT FROM AUTHOR]

In this work, a machine learning‐assisted prediction model is proposed to analyze the reliability issues in the 28 nm resistive random access memory (RRAM) chip with raw data measured from RRAM test chip. The neural network of long‐short time memory (LSTM) is trained by the voltages and resistance during the endurance test (input vectors) and generates the output of the dichotomy states with a satisfied testing result >83.08%. According to the prediction results, the "real fail" state or "fake fail" state of the devices can get in the future. By collaborating with a well‐designed resurrection algorithm (RA), the percentage of real and fake failed cells dropped by 35% and 29%, respectively. Besides, the tail bits in retention significantly reduced from 33% to 14.6% due to the reduction of oxygen vacancies in the gaps of conducting filaments by applying ten consecutive cycles of RA. This intelligent prediction and repair module can prolong the lifetime of RRAM chips effectively in practical applications. [ABSTRACT FROM AUTHOR]

Background and aims: Low outcome prevalence, often observed with opioid‐related outcomes, poses an underappreciated challenge to accurate predictive modeling. Outcome class imbalance, where non‐events (i.e. negative class observations) outnumber events (i.e. positive class observations) by a moderate to extreme degree, can distort measures of predictive accuracy in misleading ways, and make the overall predictive accuracy and the discriminatory ability of a predictive model appear spuriously high. We conducted a simulation study to measure the impact of outcome class imbalance on predictive performance of a simple SuperLearner ensemble model and suggest strategies for reducing that impact. Design, Setting, Participants: Using a Monte Carlo design with 250 repetitions, we trained and evaluated these models on four simulated data sets with 100 000 observations each: one with perfect balance between events and non‐events, and three where non‐events outnumbered events by an approximate factor of 10:1, 100:1, and 1000:1, respectively. Measurements: We evaluated the performance of these models using a comprehensive suite of measures, including measures that are more appropriate for imbalanced data. Findings Increasing imbalance tended to spuriously improve overall accuracy (using a high threshold to classify events vs non‐events, overall accuracy improved from 0.45 with perfect balance to 0.99 with the most severe outcome class imbalance), but diminished predictive performance was evident using other metrics (corresponding positive predictive value decreased from 0.99 to 0.14). Conclusion: Increasing reliance on algorithmic risk scores in consequential decision‐making processes raises critical fairness and ethical concerns. This paper provides broad guidance for analytic strategies that clinical investigators can use to remedy the impacts of outcome class imbalance on risk prediction tools. [ABSTRACT FROM AUTHOR]

An editorial is presented on the use of artificial intelligence (AI) and machine learning (ML) in the field of obstetrics and gynaecology. It discusses the potential benefits of AI and ML in improving diagnostic accuracy, patient outcomes, and efficiency of care. It further explores the various applications of AI and ML including predicting preterm birth, monitoring high-risk pregnancies in real-time, and identifying signs of various gynaecological conditions.

Background: There has been increasing interest in online professional learning networks in a variety of social media platforms, especially in Twitter. Twitter offers immediacy, personalization, and support of networks to increase professional knowledge and the sense of membership. Knowing the topics discussed in Twitter and the factors that affect the duration of a topic would help to sustain and reconstruct Twitter‐based professional learning activities. Objectives: The purpose of this study is to analyse the topics discussed and what factors affect the duration of a specific topic in 6 years within a virtual professional learning network (VPLN) using #Edchat in Twitter, based on media richness features. Methods: Internet‐mediated research and digital methods are used for data collection and analysis. Various text, natural language processing, and machine learning algorithms were used along with the quantitative multilevel models. This study examined 504,998 tweets posted by 72,342 unique users by using #Edchat. Results: There were 150 topics discussed over the 6 years and multilevel random intercept regression model revealed that a specific topic discussed in the #Edchat VPLN is discussed longer when it has more tweets, rather than retweets, posted by a high number of different users along with moderate text, high or moderate mentions with more hashtags. Takeaways: The study developed an automated social media richness feature extraction framework that can be adapted for other theoretical applications in educational context. Emergent topics discussed in Twitter among #Edchat VPLN members for professional development were identified. It extends the social media richness theory for educational context and explore factors that affect an online professional learning activity in Twitter. Lay Description: What is already known about this topic: Professional Learning Communities have started to use both synchronous and asynchronous Web 2.0 and social media platforms where they may exchange information and develop a sense of belonging with others who share common interests.Synchronous online activity in social networking sites, such as Twitter, can be an effective, informal and a free way to convey information and create and develop personalized networks.Twitter offers immediacy, personalization, and support of networks to increase professional knowledge and the sense of membership.There have been a great number of hashtags in Twitter for educational context and those hashtags are used by teachers to find information and resources and gain new perspectives and ideas from their colleagues or experts.The richness of a social media post varies according to how it is structured and constructed. What this paper adds: By using #edchat in Twitter, educators discussed 150 topics in 6 years.The most discussed topics are Creating, changing school culture; Classroom management, teaching methods; Classroom settings, Educational technologies; Support – Needs; Students' subject skills and interest; and School environment.A specific topic stays longer when it has more tweets, rather than retweets, posted by a high number of different users.Topics that have moderate text, high or moderate mentions with more hashtags are discussed longer on Twitter.The duration of a topic can be changed according to the educators' behaviours in a synchronous online chat. Implications for practice and/or policy: Importance of social media for professional learning and how to sustain a topic for better learning and understanding.Developing and applying an automated computational discourse analysis social media learning.Applying media richness theory to understand the affordances of social media learning.Quantifying the influence of various factors on the discourse in social media learning. [ABSTRACT FROM AUTHOR]

Primary delays are the driving force behind delay propagation, and predicting the number of affected trains (NAT) and the total time of affected trains (TTAT) due to primary delay (PD) can provide reliable decision support for real‐time train dispatching. In this paper, based on real operation data from 2015 to 2016 at several stations along the Wuhan–Guangzhou high‐speed railway, NAT and TTAT influencing factors were determined after analyzing the PD propagation mechanism. The eXtreme Gradient BOOSTing (XGBOOST) algorithm was used to establish a NAT predictive model, and several machine learning methods were compared. The importance of different delayinfluencing factors was investigated. Then, the TTAT predictive model (using support vector regression (SVR) algorithms) was established based on the NAT predictive model. Results indicated that the XGBOOST algorithm performed well with the NAT predictive model, and SVR was the optimal model for TTAT prediction under the verification index (i.e., the ratio of the difference between the actual and predicted value was less than 1/2/3/4/5 min). Real operational data in 2018 were used to test the applicability of the NAT and TTAT models over time, and findings suggest that these models exhibit sound applicability over time based on XGBOOST and SVR, respectively. [ABSTRACT FROM AUTHOR]

Many real‐world data mining applications involve obtaining predictive models using imbalanced datasets. Frequently, the least common target variables present within datasets are associated with events that are highly relevant for end users. When these variables are nominal, we have a class‐imbalance problem which has been thoroughly studied within machine learning. As for regression tasks where target variables are continuous, few predictive models and evaluation techniques exist. This paper proposes a solution to these challenges. First, we introduce a cost‐sensitive learning algorithm based on a neural network trained on the minimization of a biased loss function. Results show a higher or comparable performance and convergence speed to existent techniques. Second, we develop new approaches for performance assessment of regression tasks within imbalanced domains by proposing new scalar measures, namely Geometric Mean Error (GME) and Class‐Weighted Error (CWE), as well as new graphical‐based measures, namely RECTPR, RECTNR, RECG − Mean and RECCWA curves. Unlike standard measures, our evaluation strategies are shown to be more robust to data imbalance as they reflect the performance of both rare and frequent events. [ABSTRACT FROM AUTHOR]

Aims: Likelihood of alcohol dependence (AD) is increased among people who transition to greater levels of alcohol involvement at a younger age. Indicated interventions delivered early may be effective in reducing risk, but could be costly. One way to increase cost‐effectiveness would be to develop a prediction model that targeted interventions to the subset of youth with early alcohol use who are at highest risk of subsequent AD. Design: A prediction model was developed for DSM‐IV AD onset by age 25 years using an ensemble machine‐learning algorithm known as 'Super Learner'. Shapley additive explanations (SHAP) assessed variable importance. Setting and Participants: Respondents reporting early onset of regular alcohol use (i.e. by 17 years of age) who were aged 25 years or older at interview from 14 representative community surveys conducted in 13 countries as part of WHO's World Mental Health Surveys. Measurements The primary outcome to be predicted was onset of life‐time DSM‐IV AD by age 25 as measured using the Composite International Diagnostic Interview, a fully structured diagnostic interview. Findings AD prevalence by age 25 was 5.1% among the 10 687 individuals who reported drinking alcohol regularly by age 17. The prediction model achieved an external area under the curve [0.78; 95% confidence interval (CI) = 0.74–0.81] higher than any individual candidate risk model (0.73–0.77) and an area under the precision‐recall curve of 0.22. Overall calibration was good [integrated calibration index (ICI) = 1.05%]; however, miscalibration was observed at the extreme ends of the distribution of predicted probabilities. Interventions provided to the 20% of people with highest risk would identify 49% of AD cases and require treating four people without AD to reach one with AD. Important predictors of increased risk included younger onset of alcohol use, males, higher cohort alcohol use and more mental disorders. Conclusions: A risk algorithm can be created using data collected at the onset of regular alcohol use to target youth at highest risk of alcohol dependence by early adulthood. Important considerations remain for advancing the development and practical implementation of such models. [ABSTRACT FROM AUTHOR]

Aim: To develop and validate models based on logistic regression and artificial intelligence for prognostic prediction of molar survival in periodontally affected patients. Materials and Methods: Clinical and radiographic data from four different centres across four continents (two in Europe, one in the United States, and one in China) including 515 patients and 3157 molars were collected and used to train and test different types of machine‐learning algorithms for their prognostic ability of molar loss over 10 years. The following models were trained: logistic regression, support vector machine, K‐nearest neighbours, decision tree, random forest, artificial neural network, gradient boosting, and naive Bayes. In addition, different models were aggregated by means of the ensembled stacking method. The primary outcome of the study was related to the prediction of overall molar loss (MLO) in patients after active periodontal treatment. Results: The general performance in the external validation settings (aggregating three cohorts) revealed that the ensembled model, which combined neural network and logistic regression, showed the best performance among the different models for the prediction of MLO with an area under the curve (AUC) = 0.726. The neural network model showed the best AUC of 0.724 for the prediction of periodontitis‐related molar loss. In addition, the ensembled model showed the best calibration performance. Conclusions: Through a multi‐centre collaboration, both prognostic models for the prediction of molar loss were developed and externally validated. The ensembled model showed the best performance in terms of both discrimination and validation, and it is made freely available to clinicians for widespread use in clinical practice. [ABSTRACT FROM AUTHOR]

Bayesian additive regression trees (BART) is a flexible prediction model/machine learning approach that has gained widespread popularity in recent years. As BART becomes more mainstream, there is an increased need for a paper that walks readers through the details of BART, from what it is to why it works. This tutorial is aimed at providing such a resource. In addition to explaining the different components of BART using simple examples, we also discuss a framework, the General BART model that unifies some of the recent BART extensions, including semiparametric models, correlated outcomes, and statistical matching problems in surveys, and models with weaker distributional assumptions. By showing how these models fit into a single framework, we hope to demonstrate a simple way of applying BART to research problems that go beyond the original independent continuous or binary outcomes framework. [ABSTRACT FROM AUTHOR]

Diabetic foot ulcer (DFU) is one of the most serious and alarming diabetic complications, which often leads to high amputation rates in diabetic patients. Machine learning is a part of the field of artificial intelligence, which can automatically learn models from data and better inform clinical decision‐making. We aimed to develop an accurate and explainable prediction model to estimate the risk of in‐hospital amputation in patients with DFU. A total of 618 hospitalised patients with DFU were included in this study. The patients were divided into non‐amputation, minor amputation or major amputation group. Light Gradient Boosting Machine (LightGBM) and 5‐fold cross‐validation tools were used to construct a multi‐class classification model to predict the three outcomes of interest. In addition, we used the SHapley Additive exPlanations (SHAP) algorithm to interpret the predictions of the model. Our area under the receiver‐operating‐characteristic curve (AUC) demonstrated a 0.90, 0.85 and 0.86 predictive ability for non‐amputation, minor amputation and major amputation outcomes, respectively. Taken together, our data demonstrated that the developed explainable machine learning model provided accurate estimates of the amputation rate in patients with DFU during hospitalisation. Besides, the model could inform individualised analyses of the patients' risk factors. [ABSTRACT FROM AUTHOR]

Background: Accurate clinical prediction supports the effective treatment of alcohol use disorder (AUD) and other psychiatric disorders. Traditional statistical techniques have identified patient characteristics associated with treatment outcomes. However, less work has focused on systematically leveraging these associations to create optimal predictive models. The current study demonstrates how machine learning can be used to predict clinical outcomes in people completing outpatient AUD treatment. Method: We used data from the COMBINE multisite clinical trial (n = 1383) to develop and test predictive models. We identified three priority prediction targets, including (1) heavy drinking during the first month of treatment, (2) heavy drinking during the last month of treatment, and (3) heavy drinking between weekly/bi‐weekly sessions. Models were generated using the random forest algorithm. We used "leave sites out" partitioning to externally validate the models in trial sites that were not included in the model training. Stratified model development was used to test for sex differences in the relative importance of predictive features. Results: Models predicting heavy alcohol use during the first and last months of treatment showed internal cross‐validation area under the curve (AUC) scores ranging from 0.67 to 0.74. AUC was comparable in the external validation using data from held‐out sites (AUC range = 0.69 to 0.72). The model predicting between‐session heavy drinking showed strong classification accuracy in internal cross‐validation (AUC = 0.89) and external test samples (AUC range = 0.80 to 0.87). Stratified analyses showed substantial sex differences in optimal feature sets. Conclusion: Machine learning techniques can predict alcohol treatment outcomes using routinely collected clinical data. This technique has the potential to greatly improve clinical prediction accuracy without requiring expensive or invasive assessment methods. More research is needed to understand how best to deploy these models. [ABSTRACT FROM AUTHOR]

Problem: Autism spectrum disorders (ASD) are pervasive neurodevelopmental disorders and generally accompanied by social disorders, verbal or nonverbal communication defects, inability to concentrate and other negative symptoms that affect the autistic person's normal life. However, traditional screening methods are time‐consuming and public health resources are limited. Methods: This study proposed a novel technique that combined eye‐movement data and machine learning algorithms for predicting autistic traits. We converted raw eye movement data into features, trained and tested a model for early screening. Findings: In the preliminary experiment, 107 participants (average age = 24.84 ± 5.24 years) wore HTC Vive to watch a VR scene for 15–20 s. We explored eight classification models, among which the ensemble model performed best, with 0.73 accuracy, 0.68 precision, 0.81 recall, 0.74 F1‐score, and an area under the curve of 0.90. And in the test experiment, 22 participants (average age = 12.68 ± 7.61 years) diagnosed as ASD took the experiment and the ensemble model showed a recall of 0.77. Eye movement data is an effectively distinguishable tool and we find that the proportion of time to observe figure and animal region continuously can distinguish participants with obvious and unobvious autistic traits effectively in the model. Conclusion: This study focuses on the detection of autistic traits, and proposes a more objective and faster method for undertaking early screening, which provides possibilities to save precious time to intervene and alleviate its symptoms before making a definite diagnosis. [ABSTRACT FROM AUTHOR]

Predictive models based on the Choquet integral have been applied successfully in machine learning and multi criteria decision making context. The ability of the Choquet integral to recognize and capture non‐linear dependencies and its comprehensibility make it a very appealing tool. Yet, its complexity is often an obstacle to estimate model parameters in the presence of a large number of attributes. The other concern is the so‐called numerical instability, that is, learning the Choquet integral for a high degree of complexity exhibits a numerical instability. This study addresses complexity reduction and presents a novel algorithm in order to enhance the quality of optimization results and computational efficiency of learning predictive models underlying the Choquet integral. [ABSTRACT FROM AUTHOR]

Project success is critical in the crowdfunding domain. Rather than the existing project‐centric prediction methods, we propose a novel backer‐centric prediction method. We identify each backer's preferences based on their pledge history and calculate the cosine similarity between backer's preferences and the project as each backer's persuasibility. Finally, we aggregate all the backers' persuasibility to predict project success. To validate our method, we crawled data on 183,886 projects launched during or before December 2014 on Kickstarter, a crowdfunding website. We selected 4,922 backers with a total of 442,793 pledges to identify backers' preferences. The results show that a backer is more likely to be persuaded by a project that is more similar to the backer's preferences. Our findings not only demonstrate the efficacy of backers' pledge history for predicting crowdfunding project success but also verify that a backer‐centric method can supplement the existing project‐centric approaches. Our model and findings enable crowdfunding platform agencies, fund‐seeking entrepreneurs, and investors to predict the success of a crowdfunding project. [ABSTRACT FROM AUTHOR]

Objectives The US Veterans Health Administration (VHA) has begun using predictive modeling to identify Veterans at high suicide risk to target care. Initial analyses are reported here. Methods A penalized logistic regression model was compared with an earlier proof-of-concept logistic model. Exploratory analyses then considered commonly-used machine learning algorithms. Analyses were based on electronic medical records for all 6,360 individuals classified in the National Death Index as having died by suicide in fiscal years 2009-2011 who used VHA services the year of their death or prior year and a 1% probability sample of time-matched VHA service users alive at the index date ( n = 2,112,008). Results A penalized logistic model with 61 predictors had sensitivity comparable to the proof-of-concept model (which had 381 predictors) at target thresholds. The machine learning algorithms had relatively similar sensitivities, the highest being for Bayesian additive regression trees, with 10.7% of suicides occurred among the 1.0% of Veterans with highest predicted risk and 28.1% among the 5.0% of with highest predicted risk. Conclusions Based on these results, VHA is using penalized logistic regression in initial intervention implementation. The paper concludes with a discussion of other practical issues that might be explored to increase model performance. [ABSTRACT FROM AUTHOR]

Liver cancer has become the third cause that leads to the cancer death. For hepatocellular carcinoma (HCC), as the highly malignant type of liver cancer, its recurrence rate after operation is still very high because there is no reliable clinical data to provide better advice for patients after operation. To solve the challenging issue, in this work, we design a novel prediction model for recurrence of HCC using neighbor2vec based algorithm. It consists of three stages: (a) In the preparation stage, the Pearson correlation coefficient was used to explore the independent predictors of HCC recurrence, (b) due to the low correlation between individual dimension and prediction target, K‐nearest neighbors (KNN) were found as a K‐vectors list for each patient (neighbor2vec), (c) all vectors lists were applied as the input of machine learning methods such as logistic regression, KNN, decision tree, naive Bayes (NB), and deep neural network to establish the neighbor2vec based prediction model. From the experimental results on the real data from Shandong Provincial Hospital in China, the proposed neighbor2vec based prediction model outperforms all the other models. Especially, the NB model with neighbor2vec achieves up to 83.02, 82.86, 77.6%, in terms of accuracy, recall rates, and precision. This article is categorized under:Technologies > Data PreprocessingTechnologies > ClassificationTechnologies > Machine Learning [ABSTRACT FROM AUTHOR]

Aim: Predictions from statistical models may be uncalibrated, meaning that the predicted values do not have the nominal coverage probability. This is easiest seen with probability predictions in machine‐learning classification, including the common species occurrence probabilities. Here, a predicted probability of, say,.7 should indicate that out of 100 cases with these environmental conditions, and hence the same predicted probability, the species should be present in 70 and absent in 30. Innovation: A simple calibration plot shows that this is not necessarily the case, particularly not for overfitted models or algorithms that use non‐likelihood target functions. As a consequence, 'raw' predictions from such a model could easily be off by.2, are unsuitable for averaging across model types, and resulting maps hence be substantially distorted. The solution, a flexible calibration regression, is simple and can be applied whenever deviations are observed. Main conclusions: 'Raw', uncalibrated probability predictions should be calibrated before interpreting or averaging them in a probabilistic way. [ABSTRACT FROM AUTHOR]