AIMC Topic: Electronic Health Records
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Anomaly-based threat detection in smart health using machine learning.
BMC medical informatics and decision making
Nov 19, 2024
BACKGROUND: Anomaly detection is crucial in healthcare data due to challenges associated with the integration of smart technologies and healthcare. Anomaly in electronic health record can be associated with an insider trying to access and manipulate ...
Classifying Tumor Reportability Status From Unstructured Electronic Pathology Reports Using Language Models in a Population-Based Cancer Registry Setting.
JCO clinical cancer informatics
Nov 19, 2024
PURPOSE: Population-based cancer registries (PBCRs) collect data on all new cancer diagnoses in a defined population. Data are sourced from pathology reports, and the PBCRs rely on manual and rule-based solutions. This study presents a state-of-the-a...
Predicting Asthma Exacerbations Using Machine Learning Models.
Advances in therapy
Nov 18, 2024
INTRODUCTION: Although clinical, functional, and biomarker data predict asthma exacerbations, newer approaches providing high accuracy of prognosis are needed for real-world decision-making in asthma. Machine learning (ML) leverages mathematical and ...
Artificial Intelligence as a Tool for Creating Patient Visit Summary: A Scoping Review and Guide to Implementation in an Erectile Dysfunction Clinic.
Current urology reports
Nov 18, 2024
PURPOSE OF REVIEW: In modern healthcare, the integration of artificial intelligence (AI) has revolutionized clinical practices, particularly in data management and patient visit summary creation. Manual creation of patient summary is repetitive, time...
Artificial intelligence-based drug repurposing with electronic health record clinical corroboration: A case for ketamine as a potential treatment for amphetamine-type stimulant use disorder.
Addiction (Abingdon, England)
Nov 17, 2024
BACKGROUND AND AIMS: Amphetamine-type stimulants are the second-most used illicit drugs globally, yet there are no US Food and Drug Administration (FDA)-approved treatments for amphetamine-type stimulant use disorders (ATSUD). The aim of this study w...
Smart data-driven medical decisions through collective and individual anomaly detection in healthcare time series.
International journal of medical informatics
Nov 17, 2024
BACKGROUND: Anomalies in healthcare refer to deviation from the norm of unusual or unexpected patterns or activities related to patients, diseases or medical centres. Detecting these anomalies is crucial for timely interventions and efficient decisio...
Natural Language Processing to Adjudicate Heart Failure Hospitalizations in Global Clinical Trials.
Circulation. Heart failure
Nov 16, 2024
BACKGROUND: Medical record review by a physician clinical events committee is the gold standard for identifying cardiovascular outcomes in clinical trials, but is labor-intensive and poorly reproducible. Automated outcome adjudication by artificial i...
Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field.
Journal of neurodevelopmental disorders
Nov 15, 2024
Machine learning (ML) is increasingly used to identify patterns that could predict neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). One key source of multilevel data for ...
Extracting social determinants of health from inpatient electronic medical records using natural language processing.
Journal of epidemiology and population health
Nov 14, 2024
BACKGROUND: Social determinants of health (SDOH) have been shown to be important predictors of health outcomes. Here we developed methods to extract them from inpatient electronic medical record (EMR) data using techniques compatible with current EMR...
Transformer-based deep learning model for the diagnosis of suspected lung cancer in primary care based on electronic health record data.
EBioMedicine
Nov 13, 2024
BACKGROUND: Due to its late stage of diagnosis lung cancer is the commonest cause of death from cancer in the UK. Existing epidemiological risk models in clinical usage, which have Positive Predictive Values (PPV) of less than 10%, do not consider th...
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