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* Context.--Critically ill patients with acute kidney injury (AKI) requiring renal replacement therapy (RRT) have a poor prognosis. Several urinary AKI biomarkers have been proposed to predict renal recovery, but with limited discriminatory ability. Objective.--To validate the predictive performances of novel biomarkers to identify which critical patients with AKI may successfully wean from RRT. Design.--We prospectively recorded and analyzed clinical variables at several time points: (1) before starting RRT, (2) at the time of weaning off RRT, and (3) 24 hours after stopping RRT. A total of 140 critically ill patients who received RRT at a multicenter referral hospital from August 2016 to January 2019 were enrolled. The outcomes of interest were the ability to wean from RRT and 90-day mortality. Results.--The 90-day mortality rate was 13.6% (19 of 140), and 47.9% (67 of 140) of the patients were successfully weaned from RRT. Cluster analysis showed that the following biomarkers were correlated with estimated glomerular filtration rate at the time of weaning off RRT: urinary neutrophil gelatinase-associated lipocalin, kidney injury molecule 1, hemojuvelin, C-C motif chemokine ligand 14, interleukin 18, and liver-type fatty acid-binding protein (L-FABP). Among these, urinary L-FABP/ creatinine (uL-FABP/Cr) at the time of weaning off RRT showed the best predictive performance for mortality (area under the receiver operating characteristic curve = 0.79). Taking mortality as a competing risk, Cox proportional hazards analysis indicated that a low uL-FABP/Cr (log) level was an independent prognostic factor for weaning from RRT (subdistribution hazard ratio, 0.35; P = .01). Conclusions.--uL-FABP/Cr at the time of weaning off RRT could predict weaning from RRT and 90-day mortality. doi: 10.5858/arpa.2021-0411-OA, Acute kidney injury (AKI) is associated with a higher risk of chronic kidney disease (CKD), end-stage renal disease, and long-term adverse cardiovascular effects. (1,2) Recent studies have shown that for [...]

Background: Natural language processing (NLP) tools including recently developed large language models (LLMs) have myriad potential applications in medical care and research, including the efficient labeling and classification of unstructured text such as electronic health record (EHR) notes. This opens the door to large‐scale projects that rely on variables that are not typically recorded in a structured form, such as patient signs and symptoms. Objectives: This study is designed to acquaint the emergency medicine research community with the foundational elements of NLP, highlighting essential terminology, annotation methodologies, and the intricacies involved in training and evaluating NLP models. Symptom characterization is critical to urinary tract infection (UTI) diagnosis, but identification of symptoms from the EHR has historically been challenging, limiting large‐scale research, public health surveillance, and EHR‐based clinical decision support. We therefore developed and compared two NLP models to identify UTI symptoms from unstructured emergency department (ED) notes. Methods: The study population consisted of patients aged ≥ 18 who presented to an ED in a northeastern U.S. health system between June 2013 and August 2021 and had a urinalysis performed. We annotated a random subset of 1250 ED clinician notes from these visits for a list of 17 UTI symptoms. We then developed two task‐specific LLMs to perform the task of named entity recognition: a convolutional neural network‐based model (SpaCy) and a transformer‐based model designed to process longer documents (Clinical Longformer). Models were trained on 1000 notes and tested on a holdout set of 250 notes. We compared model performance (precision, recall, F1 measure) at identifying the presence or absence of UTI symptoms at the note level. Results: A total of 8135 entities were identified in 1250 notes; 83.6% of notes included at least one entity. Overall F1 measure for note‐level symptom identification weighted by entity frequency was 0.84 for the SpaCy model and 0.88 for the Longformer model. F1 measure for identifying presence or absence of any UTI symptom in a clinical note was 0.96 (232/250 correctly classified) for the SpaCy model and 0.98 (240/250 correctly classified) for the Longformer model. Conclusions: The study demonstrated the utility of LLMs and transformer‐based models in particular for extracting UTI symptoms from unstructured ED clinical notes; models were highly accurate for detecting the presence or absence of any UTI symptom on the note level, with variable performance for individual symptoms. [ABSTRACT FROM AUTHOR]

This article explores the use of natural language processing (NLP) techniques to identify incarceration status in electronic health records (EHR) in emergency department settings. The study developed and validated NLP models to accurately identify incarceration status from clinical notes, which could potentially help address health disparities and tailor interventions for individuals with a history of incarceration. The study found that the Clinical-Longformer NLP model was effective in accurately identifying incarceration history, although it had limitations in distinguishing temporal labels. It is important to approach patients in a non-judgmental manner and confirm incarceration history before offering resources to avoid stigma. The NLP model can serve as a screening tool, but manual confirmation is recommended to avoid false positives or misplacements of electronic labels in patient files. [Extracted from the article]

The repair of injured or transected nerves is frequently encountered in head and neck surgery. Recently, CO2 lasers with milliwatt capability and micrometer spot sizes have been developed that may be used in nerve anastomosis. A comparative study was performed between microsuture and CO2 laser repair of transected sciatic nerves in rats. Nerve regeneration was measured in terms of morphology, electrophysiology, and function. Histologic studies revealed no difference in the size and number of regenerated axons, although there was less scar tissue formation at the anastomotic site with the laser repair. EMG and nerve conduction velocity were similar for the two repair methods. Functional recovery, as determined objectively using measurements of gait footprints, showed no difference between suture and laser repair. Laser-repaired nerves did have a higher dehiscence rate, although this problem can probably be prevented by splinting the rats postoperatively. However, laser repair was faster and simpler than suture repair and required less manipulation of the nerve. This study shows laser repair of peripheral nerves is possible with results comparable to conventional microsuture neurorrhaphy. Laser nerve anastomosis may be an effective alternative to suture nerve repair.

Reports some of the progress made over the years toward exploring information beyond the text domain. Describes the Multimedia Analysis and Retrieval Systems (MARS), developed to increase access to non-textual information. Addresses the following aspects of MARS: (1) visual feature extraction; (2) retrieval models; (3) query reformulation techniques; (4) efficient execution speed performance; and (5) user interface considerations. (AEF)

Background: Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that can generate conversation-style responses to user input. Objective: This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. Methods: We used 2 sets of multiple-choice questions to evaluate ChatGPT's performance, each with questions pertaining to Step 1 and Step 2. The first set was derived from AMBOSS, a commonly used question bank for medical students, which also provides statistics on question difficulty and the performance on an exam relative to the user base. The second set was the National Board of Medical Examiners (NBME) free 120 questions. ChatGPT's performance was compared to 2 other large language models, GPT-3 and InstructGPT. The text output of each ChatGPT response was evaluated across 3 qualitative metrics: logical justification of the answer selected, presence of information internal to the question, and presence of information external to the question. Results: Of the 4 data sets, AMBOSS-Step1, AMBOSS-Step2, NBME-Free-Step1, and NBME-Free-Step2, ChatGPT achieved accuracies of 44% (44/100), 42% (42/100), 64.4% (56/87), and 57.8% (59/102), respectively. ChatGPT outperformed InstructGPT by 8.15% on average across all data sets, and GPT-3 performed similarly to random chance. The model demonstrated a significant decrease in performance as question difficulty increased (P=.01) within the AMBOSS-Step1 data set. We found that logical justification for ChatGPT's answer selection was present in 100% of outputs of the NBME data sets. Internal information to the question was present in 96.8% (183/189) of all questions. The presence of information external to the question was 44.5% and 27% lower for incorrect answers relative to correct answers on the NBME-Free-Step1 (P<.001) and NBME-Free-Step2 (P=.001) data sets, respectively. Conclusions: ChatGPT marks a significant improvement in natural language processing models on the tasks of medical question answering. By performing at a greater than 60% threshold on the NBME-Free-Step-1 data set, we show that the model achieves the equivalent of a passing score for a third-year medical student. Additionally, we highlight ChatGPT's capacity to provide logic and informational context across the majority of answers. These facts taken together make a compelling case for the potential applications of ChatGPT as an interactive medical education tool to support learning. [ABSTRACT FROM AUTHOR]

In recent years, our world has experienced significant disruptions due to the COVID-19 pandemic, and Russia's 2022 invasion of Ukraine, impacting human activities and the global environment. This paper explored air quality changes in Ukraine due to COVID-19, and Russia's invasion of Ukraine using on-demand with a what-you-see-is-what-you-get approach. During the COVID-19 pandemic, strict quarantine policies in Ukraine led to a 2% reduction in tropospheric NO2 concentration before the lockdown and 4% during the lockdown period. Cities like Kyiv, Donetsk, and Dnipro exhibited reductions of 5%, 11%, and 16%, respectively. Total SO2 column concentration decreased by 6% before the lockdown and 2.5% during the lockdown period, except in high population density areas. Kyiv showed the highest reduction of 17% in SO2 concentration, while Donetsk and Dnipro exhibited an 11% reduction. However, during the Russian invasion, there was a significant increase in tropospheric NO2 concentration in heavily destroyed Kharkiv while most eastern regions experienced a reduction. The total SO2 column was 48% higher before the war but reduced throughout the country after the war, except for in Kyiv and a few central regions. These findings can contribute to analyzing air pollution and building digital twin simulations for future reconstruction scenarios. [ABSTRACT FROM AUTHOR]

* Context.--Critically ill patients with acute kidney injury (AKI) requiring renal replacement therapy (RRT) have a poor prognosis. Several urinary AKI biomarkers have been proposed to predict renal recovery, but with limited discriminatory ability. Objective.--To validate the predictive performances of novel biomarkers to identify which critical patients with AKI may successfully wean from RRT. Design.--We prospectively recorded and analyzed clinical variables at several time points: (1) before starting RRT, (2) at the time of weaning off RRT, and (3) 24 hours after stopping RRT. A total of 140 critically ill patients who received RRT at a multicenter referral hospital from August 2016 to January 2019 were enrolled. The outcomes of interest were the ability to wean from RRT and 90-day mortality. Results.--The 90-day mortality rate was 13.6% (19 of 140), and 47.9% (67 of 140) of the patients were successfully weaned from RRT. Cluster analysis showed that the following biomarkers were correlated with estimated glomerular filtration rate at the time of weaning off RRT: urinary neutrophil gelatinase-associated lipocalin, kidney injury molecule 1, hemojuvelin, C-C motif chemokine ligand 14, interleukin 18, and liver-type fatty acid--binding protein (L-FABP). Among these, urinary L-FABP/creatinine (uL-FABP/Cr) at the time of weaning off RRT showed the best predictive performance for mortality (area under the receiver operating characteristic curve = 0.79). Taking mortality as a competing risk, Cox proportional hazards analysis indicated that a low uL-FABP/Cr (log) level was an independent prognostic factor for weaning from RRT (subdistribution hazard ratio, 0.35; P = .01). Conclusions.--uL-FABP/Cr at the time of weaning off RRT could predict weaning from RRT and 90-day mortality. [ABSTRACT FROM AUTHOR]

In this paper, we present a fusion approach to solve the nonrigid shape recovery problem, which takes advantage of both the appearance information and the local features. We have two major contributions. First, we propose a novel progressive finite Newton optimization scheme for the feature-based nonrigid surface detection problem, which is reduced to only solving a set of linear equations. The key is to formulate the nonrigid surface detection as an unconstrained quadratic optimization problem that has a closed-form solution for a given set of observations. Second, we propose a deformable Lucas-Kanade algorithm that triangulates the template image into small patches and constrains the deformation through the second-order derivatives of the mesh vertices. We formulate it into a sparse regularized least squares problem, which is able to reduce the computational cost and the memory requirement. The inverse compositional algorithm is applied to efficiently solve the optimization problem. We have conducted extensive experiments for performance evaluation on various environments, whose promising results show that the proposed algorithm is both efficient and effective. Index Terms--Image processing and computer vision, nonrigid detection, real-time deformable registration, nonrigid augmented reality, medical image registration.

We propose a five-layer hierarchical space-time model (HSTM) for representing and searching human actions in videos. From a features point of view, both invariance and selectivity are desirable characteristics, which seem to contradict each other. To make these characteristics coexist, we introduce a coarse-to-fine search and verification scheme for action searching, based on the HSTM model. Because going through layers of the hierarchy corresponds to progressively turning the knob between invariance and selectivity, this strategy enables search for human actions ranging from rapid movements of sports to subtle motions of facial expressions. The introduction of the Histogram of Gabor Orientations feature makes the searching for actions go smoothly across the hierarchical layers of the HSTM model. The efficient matching is achieved by applying integral histograms to compute the features in the top two layers. The HSTM model was tested on three selected challenging video sequences and on the KTH human action database. And it achieved improvement over other state-of-the-art algorithms. These promising results validate that the HSTM model is both selective and robust for searching human actions. Index Terms--Action recognition, action search, hierarchical space-time model (HSTM), Histogram of Gabor Orientations (HIGO).

This paper introduces a head pose estimation system that automatically localizes the nose-tips of the faces and estimates head poses in images simultaneously. In the training stage, the nose-tips of the faces are first manually labeled. The appearance variations caused by head pose changes are then characterized by a Tensorposes model. Given an image with unknown head pose and nose-tip location, the nose-tip of the face is automatically localized in a coarse-to-fine fashion after the skin color segmentation. The head pose is also estimated simultaneously. The performance of our system is evaluated on the Pointing'04 head pose image data set. We first evaluate the classification performance of the Tensorposes models with image patches of the faces cropped according to the manually labeled nose-tip locations of the faces in the Pointing '04 data set. By leaving-one-person-out evaluation strategy, we obtain the optimal parameters of the Tensorposes model, and evaluate the discriminative power of the Tensorposes model built based on high order singular value decomposition (HOSVD) and multilinear independent component analysis (MICA), and naive principal component analysis (PCA) subspace models. It is shown Tensorposes model by HOSVD and MICA decomposition performs similarly good but much better than naive PCA subspace models. The Tensorposes model is then utilized to automatically localize nose-tip location in the testing image and to simultaneously estimate the head pose. The nose-tip localization and pose estimation accuracy of the proposed system are evaluated against the ground truth. Finally, cross-database evaluation of the performance of our system is carried out on Pointing'04 database, a selected subset of CMU PIE database, and some pictures from CLEAR'07 head pose evaluation database. The experiments show that our system generalizes reasonably well to the real-world scenarios. Index Terms--Face image, head pose estimation, nose-tip, Pointing'04, Tensorposes.

Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions, despite the fact that deliberate behavior differs in visual appearance, audio profile, and timing from spontaneously occurring behavior. To address this problem, efforts to develop algorithms that can process naturally occurring human affective behavior have recently emerged. Moreover, an increasing number of efforts are reported toward multimodal fusion for human affect analysis, including audiovisual fusion, linguistic and paralinguistic fusion, and multicue visual fusion based on facial expressions, head movements, and body gestures. This paper introduces and surveys these recent advances. We first discuss human emotion perception from a psychological perspective. Next, we examine available approaches for solving the problem of machine understanding of human affective behavior and discuss important issues like the collection and availability of training and test data. We finally outline some of the scientific and engineering challenges to advancing human affect sensing technology. Index Terms--Evaluation/methodology, human-centered computing, affective computing, introductory, survey.

Background: Clinical decisions regarding the appropriate timing of weaning off renal replacement therapy (RRT) in critically ill patients are complex and multifactorial. The aim of the current study was to identify which critical patients with acute kidney injury (AKI) may be more likely to be successfully weaned off RRT using consensus cluster analysis. Methods: In this study, critically ill patients who received RRT at three multicenter referral hospitals at several timepoints from August 2016 to July 2018 were enrolled. An unsupervised consensus clustering algorithm was used to identify distinct phenotypes. The outcomes of interest were the ability to wean off RTT and 90-day mortality. Results: A total of 124 patients with AKI requiring RRT (AKI-RRT) were enrolled. The 90-day mortality rate was 30.7% (38/124), and 49.2% (61/124) of the patients were successfully weaned off RRT for over 90 days. The consensus clustering algorithm identified three clusters from a total of 45 features. The three clusters had distinct features and could be separated according to the combination of urinary neutrophil gelatinase-associated lipocalin to creatinine ratio (uNGAL/Cr), Sequential Organ Failure Assessment (SOFA) score, and estimated glomerular filtration rate at the time of weaning off RRT. uNGAL/Cr (hazard ratio [HR] 2.43, 95% confidence interval [CI]: 1.36–4.33) and clustering phenotype (cluster 1 vs. 3, HR 2.7, 95% CI: 1.11–6.57; cluster 2 vs. 3, HR 44.5, 95% CI: 11.92–166.39) could predict 90-day mortality or re-dialysis. Conclusions: Almost half of the critical patients with AKI-RRT could wean off dialysis for over 90 days. Urinary NGAL/Cr and distinct clustering phenotypes could predict 90-day mortality or re-dialysis. [ABSTRACT FROM AUTHOR]

Recent research has demonstrated the success of supervised dimensionality reduction algorithms 2DLDA and 2DMFA, which are based on the image-as-matrix representation, in small sample size cases. To solve the convergence problem in 2DLDA and 2DMFA, we propose in this work two new schemes, called Null Space based 2DLDA (NS2DLDA) and Null Space based 2DMFA (NS2DMFA), and apply them to the challenging multi-view face recognition task. First, we convert each 2-D face image (matrix) into a vector and compute the first projection matrix P1 from the null space of the intra-class scatter matrix, such that the samples from the same class are projected to the same point. Then the data are projected and reconstructed with P1. Finally, we re-organize the reconstructed datum into a matrix and then compute the second projection direction P2, in the form of a Kronecker product of two matrices, by maximizing the inter-class scatter. A proof of algorithmic convergence is provided. The experiments on two benchmark multi-view face databases, the CMU PIE and FERET databases, demonstrate that NS2DLDA outperforms Fisherface, Null Space LDA (NSLDA) and 2DLDA. Additionally, NS2DMFA is also demonstrated to be more accurate than MFA and 2DMFA for face recognition. Index Terms--LDA, MFA, multiview face recognition, null space LDA, 2DLDA, 2DMFA.

Conventional subspace learning or recent feature extraction methods consider globality as the key criterion to design discriminative algorithms for image classification. We demonstrate in this paper that applying the local manner in sample space, feature space, and learning space via linear subspace learning can sufficiently boost the discriminating power, as measured by discriminating power coefficient (DPC). The proposed solution achieves good classification accuracy gains and shows computationally efficient. Particularly, we approximate the global nonlinearity through a multimodal localized piecewise subspace learning framework, in which three locality criteria can work individually or jointly for any new subspace learning algorithm design. It turns out that most existing subspace learning methods can be unified in such a common framework embodying either the global or local learning manner. On the other hand, we address the problem of numerical difficulty in the large-size pattern classification case, where many local variations cannot be adequately handled by a single global model. By localizing the modeling, the classification error rate estimation is also localized and thus it appears to be more robust and flexible for the model selection among different model candidates. As a new algorithm design based on the proposed framework, the query-driven locally adaptive (QDLA) mixture-of-experts model for robust face recognition and head pose estimation is presented. Experiments demonstrate the local approach to be effective, robust, and fast for large size, multiclass, and multivariance data sets. Index Terms--Discriminating power coefficient (DPC), face recognition, globality, head pose estimation, human-centered computing (HCC), locality, mixture-of-experts model, subspace learning.

Beyond conventional linear and kernel-based feature extraction, we present a more generalized formulation for feature extraction in this paper. Two representative algorithms using the correlation metric are proposed based on this formulation. Correlation Embedding Analysis (CEA), which incorporates both correlational mapping and discriminant analysis, boosts the discriminating power by mapping the data from a high-dimensional hypersphere onto another low-dimensional hypersphere and preserving the neighboring relations with local-sensitive graph modeling. Correlational Principal Component Analysis (CPCA) generalizes the Principal Component Analysis (PCA) algorithm to the case with data distributed on a high-dimensional hypersphere. Their advantages stem from two facts: 1) directly working on normalized data, which are often the outputs from data preprocessing, and 2) directly designed with the correlation metric, which is shown to be generally better than euclidean distance for classification purpose in many real-world applications. Extensive visual recognition experiments compared with existing feature extraction algorithms demonstrate the effectiveness of the proposed algorithms. Index Terms--Feature extraction, graph embedding, correlation embedding analysis, correlational principal component analysis, face recognition.

Factor analysis (FA) seeks to reveal the relationship between an observed vector variable and a latent variable of reduced dimension. It has been widely used in many applications involving high-dimensional data, such as image representation and face recognition. An intrinsic limitation of FA lies in its potentially poor performance when the data dimension is high, a problem known as curse of dimensionality. Motivated by the fact that images are inherently matrices, we develop, in this brief, an FA model for matrix-variate variables and present an efficient parameter estimation algorithm. Experiments on both toy and real-world image data demonstrate that the proposed matrix-variant FA model is more efficient and accurate than the classical FA approach, especially when the observed variable is high-dimensional and the samples available are limited. Index Terms--Conditional expectation maximization (EM), face recognition, factor analysis (FA), matrix.

The article proposes locally adjusted robust regressor (LARR), a novel method for robust learning and prediction of aging patterns. The efficiency of LARR in age estimation is observed.