AIMC Topic: Machine Learning

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Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review.

BMJ health & care informatics Mar 1, 2021
OBJECTIVES: Unstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of t...
Electronic Health Records Humans Natural Language Processing Feedback Machine Learning Patient Outcome Assessment Bayes Theorem Social Media Patient Satisfaction
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Enhancing Military Burn- and Trauma-Related Acute Kidney Injury Prediction Through an Automated Machine Learning Platform and Point-of-Care Testing.

Archives of pathology & laboratory medicine Mar 1, 2021
CONTEXT.—: Delayed recognition of acute kidney injury (AKI) results in poor outcomes in military and civilian burn-trauma care. Poor predictive ability of urine output (UOP) and creatinine contribute to the delayed recognition of AKI.
Wounds and Injuries Burns Creatinine Military Personnel Predictive Value of Tests Female Algorithms Humans Aged Machine Learning Adult Point-of-Care Testing Biomarkers Male Lipocalin-2 Acute Kidney Injury Aged, 80 and over Middle Aged
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Various machine learning approaches coupled with molecule simulation in the screening of natural compounds with xanthine oxidase inhibitory activity.

Food & function Mar 1, 2021
Gout is a common inflammatory arthritis associated with various comorbidities, such as cardiovascular disease and metabolic syndrome. Xanthine oxidase inhibitors (XOIs) have emerged as effective substances to control gout. Much attention has been giv...
Drug Discovery Gout Suppressants Hydrophobic and Hydrophilic Interactions Enzyme Inhibitors Databases, Chemical Xanthine Oxidase Machine Learning Molecular Docking Simulation
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Machine learning: at the heart of failure diagnosis.

Current opinion in cardiology Mar 1, 2021
PURPOSE OF REVIEW: Refinement in machine learning (ML) techniques and approaches has rapidly expanded artificial intelligence applications for the diagnosis and classification of heart failure (HF). This review is designed to provide the clinician wi...
Algorithms Humans Electrocardiography Artificial Intelligence Heart Failure Machine Learning
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Artificial intelligence and complex statistical modeling in glaucoma diagnosis and management.

Current opinion in ophthalmology Mar 1, 2021
PURPOSE OF REVIEW: The field of artificial intelligence has grown exponentially in recent years with new technology, methods, and applications emerging at a rapid rate. Many of these advancements have been used to improve the diagnosis and management...
Tomography, Optical Coherence Diagnostic Techniques, Ophthalmological Photography Humans Glaucoma Artificial Intelligence Machine Learning Algorithms Models, Statistical
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The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World.

American journal of clinical dermatology Mar 1, 2021
Artificial intelligence (AI) algorithms have been shown to diagnose skin lesions with impressive accuracy in experimental settings. The majority of the literature to date has compared AI and dermatologists as opponents in skin cancer diagnosis. Howev...
Man-Machine Systems Diagnostic Errors Clinical Competence Attitude of Health Personnel Skin Dermatologists Image Interpretation, Computer-Assisted Humans Machine Learning Skin Neoplasms
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Letter to the editor in response to "Risk prediction of delirium in hospitalized patients using machine learning: an implementation and prospective evaluation study".

Journal of the American Medical Informatics Association : JAMIA Mar 1, 2021
Prospective Studies Delirium Patients Humans Machine Learning
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Clinician involvement in research on machine learning-based predictive clinical decision support for the hospital setting: A scoping review.

Journal of the American Medical Informatics Association : JAMIA Mar 1, 2021
OBJECTIVE: The study sought to describe the prevalence and nature of clinical expert involvement in the development, evaluation, and implementation of clinical decision support systems (CDSSs) that utilize machine learning to analyze electronic healt...
Electronic Health Records Decision Support Systems, Clinical Nursing Staff, Hospital Humans Machine Learning Research Decision Making, Computer-Assisted Software Design Medical Staff, Hospital Hospital Administration
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Development of a "meta-model" to address missing data, predict patient-specific cancer survival and provide a foundation for clinical decision support.

Journal of the American Medical Informatics Association : JAMIA Mar 1, 2021
OBJECTIVE: Like most real-world data, electronic health record (EHR)-derived data from oncology patients typically exhibits wide interpatient variability in terms of available data elements. This interpatient variability leads to missing data and can...
Survival Analysis Humans Prognosis Machine Learning Neoplasms Decision Support Systems, Clinical ROC Curve Models, Theoretical Area Under Curve
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An explainable machine learning platform for pyrazinamide resistance prediction and genetic feature identification of Mycobacterium tuberculosis.

Journal of the American Medical Informatics Association : JAMIA Mar 1, 2021
OBJECTIVE: Tuberculosis is the leading cause of death from a single infectious agent. The emergence of antimicrobial resistant Mycobacterium tuberculosis strains makes the problem more severe. Pyrazinamide (PZA) is an important component for short-co...
Machine Learning Drug Resistance, Bacterial Microbial Sensitivity Tests Humans Neural Networks, Computer Tuberculosis Mycobacterium tuberculosis Pyrazinamide Support Vector Machine Mutation Antitubercular Agents
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