AIMC Topic: Electronic Health Records
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Applying machine learning approaches for predicting obesity risk using US health administrative claims database.
BMJ open diabetes research & care
Sep 26, 2024
INTRODUCTION: Body mass index (BMI) is inadequately recorded in US administrative claims databases. We aimed to validate the sensitivity and positive predictive value (PPV) of BMI-related diagnosis codes using an electronic medical records (EMR) clai...
Processing of Short-Form Content in Clinical Narratives: Systematic Scoping Review.
Journal of medical Internet research
Sep 26, 2024
BACKGROUND: Clinical narratives are essential components of electronic health records. The adoption of electronic health records has increased documentation time for hospital staff, leading to the use of abbreviations and acronyms more frequently. Th...
Using Natural Language Processing to develop risk-tier specific suicide prediction models for Veterans Affairs patients.
Journal of psychiatric research
Sep 24, 2024
Suicide is a leading cause of death. Suicide rates are particularly elevated among Department of Veterans Affairs (VA) patients. While VA has made impactful suicide prevention advances, efforts primarily target high-risk patients with documented suic...
Prevention of adverse HIV treatment outcomes: machine learning to enable proactive support of people at risk of HIV care disengagement in Tanzania.
BMJ open
Sep 24, 2024
OBJECTIVES: This study aimed to develop a machine learning (ML) model to predict disengagement from HIV care, high viral load or death among people living with HIV (PLHIV) with the goal of enabling proactive support interventions in Tanzania. The alg...
Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study.
JMIR medical informatics
Sep 24, 2024
BACKGROUND: Natural language processing (NLP) techniques can be used to analyze large amounts of electronic health record texts, which encompasses various types of patient information such as quality of life, effectiveness of treatments, and adverse ...
Machine-Learning Model Identifies Patients With Alpha-1 Antitrypsin Deficiency Using Claims Records.
COPD
Sep 23, 2024
Identifying patients with rare diseases like alpha-1 antitrypsin deficiency (AATD) is challenging. Machine-learning models may be trained to identify patients with rare diseases using large-scale, real-world databases, whereas electronic medical reco...
Integration of an electronic hand hygiene auditing system with electronic health records using machine learning to predict hospital-acquired infection in a health care setting.
American journal of infection control
Sep 21, 2024
BACKGROUND: Hospital-acquired infections (HAIs) increase morbidity, mortality, and health care costs. Effective hand hygiene (HH) is crucial for prevention, but achieving high compliance remains challenge. This study explores using machine learning t...
The Impact of Collaborative Documentation on Person-Centered Care: Textual Analysis of Clinical Notes.
JMIR medical informatics
Sep 20, 2024
BACKGROUND: Collaborative documentation (CD) is a behavioral health practice involving shared writing of clinic visit notes by providers and consumers. Despite widespread dissemination of CD, research on its effectiveness or impact on person-centered...
Development of a Natural Language Processing (NLP) model to automatically extract clinical data from electronic health records: results from an Italian comprehensive stroke center.
International journal of medical informatics
Sep 19, 2024
INTRODUCTION: Data collection often relies on time-consuming manual inputs, with a vast amount of information embedded in unstructured texts such as patients' medical records and clinical notes. Our study aims to develop a pipeline that combines acti...
Identifying Facilitators and Barriers to Implementation of AI-Assisted Clinical Decision Support in an Electronic Health Record System.
Journal of medical systems
Sep 18, 2024
Recent advancements in computing have led to the development of artificial intelligence (AI) enabled healthcare technologies. AI-assisted clinical decision support (CDS) integrated into electronic health records (EHR) was demonstrated to have a signi...
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