AIMC Topic: Severity of Illness Index

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Learning fine-grained estimation of physiological states from coarse-grained labels by distribution restoration.

Scientific reports Dec 15, 2020
Due to its importance in clinical science, the estimation of physiological states (e.g., the severity of pathological tremor) has aroused growing interest in machine learning community. While the physiological state is a continuous variable, its cont...
Machine Learning Algorithms Severity of Illness Index Parkinson Disease Blood Pressure Tremor Humans Neural Networks, Computer
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Longitudinal Screening for Diabetic Retinopathy in a Nationwide Screening Program: Comparing Deep Learning and Human Graders.

Journal of diabetes research Dec 15, 2020
OBJECTIVE: To evaluate diabetic retinopathy (DR) screening via deep learning (DL) and trained human graders (HG) in a longitudinal cohort, as case spectrum shifts based on treatment referral and new-onset DR.
Deep Learning National Health Programs Male Incidence Middle Aged Cell Proliferation Prevalence Mass Screening Predictive Value of Tests Longitudinal Studies Thailand Fundus Oculi Severity of Illness Index Macular Edema Image Interpretation, Computer-Assisted Photography Humans Diabetic Retinopathy Female Reproducibility of Results Aged
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Machine-learning-driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis.

Proceedings of the National Academy of Sciences of the United States of America Dec 14, 2020
Contact dermatitis tremendously impacts the quality of life of suffering patients. Currently, diagnostic regimes rely on allergy testing, exposure specification, and follow-up visits; however, distinguishing the clinical phenotype of irritant and all...
Allergens Gene Expression Regulation Irritants Dermatitis, Allergic Contact Patch Tests Dermatitis, Irritant Gene Regulatory Networks Databases, Genetic Diagnosis, Differential Skin Leukocytes Reproducibility of Results Adult Male Severity of Illness Index Algorithms Transcriptome Humans Biomarkers Female Machine Learning
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Machine learning to reveal hidden risk combinations for the trajectory of posttraumatic stress disorder symptoms.

Scientific reports Dec 10, 2020
The nature of the recovery process of posttraumatic stress disorder (PTSD) symptoms is multifactorial. The Massive Parallel Limitless-Arity Multiple-testing Procedure (MP-LAMP), which was developed to detect significant combinational risk factors com...
Stress Disorders, Post-Traumatic Unemployment Predictive Value of Tests Walking Rest Aged Adult Male Middle Aged Severity of Illness Index Risk Emergency Shelter Humans Machine Learning Female Risk Factors
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A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Dec 9, 2020
Non-alcoholic steatohepatitis (NASH) is a fatty liver disease characterized by accumulation of fat in hepatocytes with concurrent inflammation and is associated with morbidity, cirrhosis and liver failure. After extraction of a liver core biopsy, tis...
Decision Support Techniques Retrospective Studies Clinical Decision-Making Female Reproducibility of Results Aged Humans Neural Networks, Computer Software Liver Severity of Illness Index Eosine Yellowish-(YS) Predictive Value of Tests Adolescent Hematoxylin Biopsy Microscopy Child Methyl Green Aged, 80 and over Non-alcoholic Fatty Liver Disease Azo Compounds Staining and Labeling Young Adult Coloring Agents Image Interpretation, Computer-Assisted Liver Cirrhosis Adult Male Middle Aged
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Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG.

Journal of Korean medical science Dec 7, 2020
BACKGROUND: This paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal.
Middle Aged Severity of Illness Index Deep Learning Electrocardiography Signal Processing, Computer-Assisted Humans Female Aged Adult Male Sleep Apnea Syndromes
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Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels.

IEEE journal of biomedical and health informatics Dec 4, 2020
Early and accurate diagnosis of Coronavirus disease (COVID-19) is essential for patient isolation and contact tracing so that the spread of infection can be limited. Computed tomography (CT) can provide important information in COVID-19, especially f...
Tomography, X-Ray Computed Male Humans Female Severity of Illness Index SARS-CoV-2 COVID-19 Deep Learning Algorithms Retrospective Studies
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Using machine learning-based analytics of daily activities to identify modifiable risk factors for falling in Parkinson's disease.

Parkinsonism & related disorders Nov 19, 2020
BACKGROUND: Although risk factors that lead to falling in Parkinson's disease (PD) have been previously studied, the established predictors are mostly non-modifiable. A novel method for fall risk assessment may provide more insight into preventable h...
Parkinson Disease Postural Balance Supervised Machine Learning Activities of Daily Living Antiparkinson Agents Risk Assessment Severity of Illness Index Accidental Falls Models, Theoretical Age Factors Female Risk Factors Humans Aged Prognosis Male Middle Aged
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Performance of Prediction Models for Diagnosing Severe Aortic Stenosis Based on Aortic Valve Calcium on Cardiac Computed Tomography: Incorporation of Radiomics and Machine Learning.

Korean journal of radiology Nov 3, 2020
OBJECTIVE: We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms.
Humans Machine Learning Retrospective Studies Tomography, X-Ray Computed Female Aortic Valve Stenosis Aged Aortic Valve Male Calcium Echocardiography Middle Aged Severity of Illness Index
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Predicting preventable hospital readmissions with causal machine learning.

Health services research Oct 30, 2020
OBJECTIVE: To assess both the feasibility and potential impact of predicting preventable hospital readmissions using causal machine learning applied to data from the implementation of a readmissions prevention intervention (the Transitions Program).
Female Risk Factors Aged Male Patient Readmission Middle Aged Health Services Research Risk Assessment Continuity of Patient Care Humans Diagnostic Techniques and Procedures Severity of Illness Index Machine Learning Health Status Retrospective Studies Age Factors Electronic Health Records Sex Factors
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