AIMC Topic: Electronic Health Records
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Using Natural Language Processing Methods to Predict Topics Included in 2019 Ohio Syphilis Disease Intervention Specialist Records.
Sexually transmitted diseases
Feb 11, 2025
BACKGROUND: Free-text notes in disease intervention specialist (DIS) records may contain relevant information for sexual transmitted infection control. In their current form, the notes are not analyzable without manual reading, which is labor-intensi...
The good, the bad, and the ugly: Ethical considerations regarding artificial intelligence assistance in administrative physician tasks.
Clinics in dermatology
Feb 11, 2025
Artificial intelligence is a powerful tool that can potentially transform the diagnostic, therapeutic, and administrative practice of dermatology. Physicians are expected to complete electronic health record documentation in a timely fashion, prepare...
Detecting Opioid Use Disorder in Health Claims Data With Positive Unlabeled Learning.
IEEE journal of biomedical and health informatics
Feb 10, 2025
Accurate detection and prevalence estimation of behavioral health conditions, such as opioid use disorder (OUD), are crucial for identifying at-risk individuals, determining treatment needs, monitoring prevention and intervention efforts, and recruit...
RespBERT: A Multi-Site Validation of a Natural Language Processing Algorithm, of Radiology Notes to Identify Acute Respiratory Distress Syndrome (ARDS).
IEEE journal of biomedical and health informatics
Feb 10, 2025
Acute respiratory distress syndrome (ARDS) is a severe organ dysfunction associated with significant mortality and morbidity among critically ill patients admitted to the Intensive Care Unit (ICU). The etiology related to ARDS can be highly heterogen...
Network Analysis and Machine Learning for Signal Detection and Prioritization Using Electronic Healthcare Records and Administrative Databases: A Proof of Concept in Drug-Induced Acute Myocardial Infarction.
Drug safety
Feb 7, 2025
BACKGROUND: Safety signals for potential drug-induced adverse events (AEs) typically emerge from multiple data sources, primarily spontaneous reporting systems, despite known limitations. Increasingly, real-world data from sources such as electronic ...
Development and Validation of a Machine Learning Algorithm for Predicting Diabetes Retinopathy in Patients With Type 2 Diabetes: Algorithm Development Study.
JMIR medical informatics
Feb 7, 2025
BACKGROUND: Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. Machine learning (ML) systems can enhance DR in community-based screening. However, predictive power models for usability and performance are still being d...
A machine learning model using clinical notes to estimate PHQ-9 symptom severity scores in depressed patients.
Journal of affective disorders
Feb 3, 2025
BACKGROUND: Lack of widespread use of the Patient Health Questionnaire 9-item (PHQ-9) in clinical practice inhibits measurement of treatment follow-up for patients with major depressive disorder (MDD). This study developed, validated and applied a ma...
Clinician Experiences With Ambient Scribe Technology to Assist With Documentation Burden and Efficiency.
JAMA network open
Feb 3, 2025
IMPORTANCE: Timely evaluation of ambient scribing technology is warranted to assess whether this technology can lessen the burden of clinical documentation on clinicians.
A machine learning-based model for predicting paroxysmal and persistent atrial fibrillation based on EHR.
BMC medical informatics and decision making
Feb 3, 2025
BACKGROUND: There is no effective way to accurately predict paroxysmal and persistent atrial fibrillation (AF) subtypes unless electrocardiogram (ECG) observation is obtained. We aim to develop a predictive model using a machine learning algorithm fo...
Prediction of the Risk of Adverse Clinical Outcomes with Machine Learning Techniques in Patients with Noncommunicable Diseases.
Journal of medical systems
Feb 3, 2025
Decision-making in chronic diseases guided by clinical decision support systems that use models including multiple variables based on artificial intelligence requires scientific validation in different populations to optimize the use of limited human...
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