AIMC Topic: Logistic Models

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Predicting Early Stage Drug Induced Parkinsonism using Unsupervised and Supervised Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Drug Induced Parkinsonism (DIP) is the most common, debilitating movement disorder induced by antipsychotics. There is no tool available in clinical practice to effectively diagnose the symptoms at the onset of the disease. In this study, the variati...
Supervised Machine Learning Parkinson Disease, Secondary Tremor Humans Algorithms Logistic Models
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Dr. Agent: Clinical predictive model via mimicked second opinions.

Journal of the American Medical Informatics Association : JAMIA Jul 1, 2020
OBJECTIVE: Prediction of disease phenotypes and their outcomes is a difficult task. In practice, patients routinely seek second opinions from multiple clinical experts for complex disease diagnosis. Our objective is to mimic such a practice of seekin...
Humans Disease Progression Deep Learning Length of Stay Hospital Mortality Neural Networks, Computer Computer Simulation Prognosis Logistic Models Referral and Consultation
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Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review.

Journal of the American Medical Informatics Association : JAMIA Jul 1, 2020
OBJECTIVE: To conduct a systematic scoping review of explainable artificial intelligence (XAI) models that use real-world electronic health record data, categorize these techniques according to different biomedical applications, identify gaps of curr...
Attitude of Health Personnel Logistic Models Bibliometrics Evaluation Studies as Topic Artificial Intelligence Humans Machine Learning Electronic Health Records Reproducibility of Results
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A Novel Machine Learning Model Developed to Assist in Patient Selection for Outpatient Total Shoulder Arthroplasty.

The Journal of the American Academy of Orthopaedic Surgeons Jul 1, 2020
INTRODUCTION: Patient selection for outpatient total shoulder arthroplasty (TSA) is important to optimizing patient outcomes. This study aims to develop a machine learning tool that may aid in patient selection for outpatient total should arthroplast...
Patient Selection Humans Pulmonary Disease, Chronic Obstructive Machine Learning Sex Factors Forecasting Decision Support Techniques Male Outpatients ROC Curve Comorbidity Female Arthroplasty, Replacement, Shoulder Logistic Models Length of Stay Aged Treatment Outcome Age Factors
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Comparison of Unplanned 30-Day Readmission Prediction Models, Based on Hospital Warehouse and Demographic Data.

Studies in health technology and informatics Jun 16, 2020
Anticipating unplanned hospital readmission episodes is a safety and medico-economic issue. We compared statistics (Logistic Regression) and machine learning algorithms (Gradient Boosting, Random Forest, and Neural Network) for predicting the risk of...
Machine Learning Reproducibility of Results Logistic Models Patient Readmission Demography
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EXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning.

Journal of the American Medical Informatics Association : JAMIA May 1, 2020
OBJECTIVE: Predicting patient outcomes using healthcare/genomics data is an increasingly popular/important area. However, some diseases are rare and require data from multiple institutions to construct generalizable models. To address institutional d...
Machine Learning Algorithms Computer Communication Networks Delivery of Health Care Blockchain Prognosis Female Male Area Under Curve Length of Stay Decision Support Systems, Clinical Humans Decision Support Systems, Management Genomics Datasets as Topic Logistic Models Computer Security
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A new risk stratification score for patients with suspected cardiac chest pain in emergency departments, based on machine learning.

Chinese medical journal Apr 5, 2020
Chi-Square Distribution Angina Pectoris Logistic Models Emergency Service, Hospital Chest Pain Humans Machine Learning Retrospective Studies ROC Curve
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Depression screening using mobile phone usage metadata: a machine learning approach.

Journal of the American Medical Informatics Association : JAMIA Apr 1, 2020
OBJECTIVE: Depression is currently the second most significant contributor to non-fatal disease burdens globally. While it is treatable, depression remains undiagnosed in many cases. As mobile phones have now become an integral part of daily life, th...
Telemedicine Depressive Disorder Humans Cell Phone Use Neural Networks, Computer Sensitivity and Specificity Mobile Applications Severity of Illness Index Logistic Models Machine Learning Depression Area Under Curve Algorithms Surveys and Questionnaires Adult
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Prediction of fatal adverse prognosis in patients with fever-related diseases based on machine learning: A retrospective study.

Chinese medical journal Mar 5, 2020
BACKGROUND: Fever is the most common chief complaint of emergency patients. Early identification of patients at an increasing risk of death may avert adverse outcomes. The aim of this study was to establish an early prediction model of fatal adverse ...
Retrospective Studies Humans Fever ROC Curve Machine Learning Prognosis Logistic Models Heart Rate Blood Pressure Odds Ratio
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Temporal convolutional networks allow early prediction of events in critical care.

Journal of the American Medical Informatics Association : JAMIA Mar 1, 2020
OBJECTIVE: Clinical interventions and death in the intensive care unit (ICU) depend on complex patterns in patients' longitudinal data. We aim to anticipate these events earlier and more consistently so that staff can consider preemptive action.
Logistic Models Critical Care Aged, 80 and over Humans Female Male Neural Networks, Computer Hospitalization Machine Learning Middle Aged Aged Computer Simulation Hospital Mortality Prognosis Intensive Care Units
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