Information Technology

A Pilot Report on Extracting Symptom Onset Date and Time from Clinical Notes in Patients Presenting with Chest Pain

Acute coronary syndrome (ACS) is an acute heart disease that often evolves rapidly. In ACS patients presenting with no-ST-segment elevation (NSTE-ACS), the timing of symptom onset pre-hospital may inform the disease stage and prognosis. We pilot-tested two off-the-shelf natural language processing (NLP) pipelines, namely parsedatetime and regular expression (regex), to extract date and time (DateT...

A Natural Language Processing Pipeline based on the Columbia-Suicide Severity Rating Scale

Diagnostic codes in the Electronic Health Record (EHR) are known to be limited in reporting patient suicidality, and especially in differentiating the levels of suicide severity. The authors developed and validated a portable natural language processing (NLP) algorithm for detection of suicidal ideation (SI) and suicide-related behavior and attempts (SB/SA) in EHR data. The algorithm was then depl...

LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction.

Electronic health records (EHRs) contain valuable patient data for health-related prediction tasks, such as disease prediction. Traditional approaches...

Neural Granger Causal Discovery for Derangements in ICU-Acquired Acute Kidney Injury Patients.

Nowadays, healthcare systems increasingly utilize automated surveillance of electronic medical record (EMR) data to detect adverse events with specifi...

Federated Multiple Imputation for Variables that Are Missing Not At Random in Distributed Electronic Health Records.

Large electronic health records (EHR) have been widely implemented and are available for research activities. The magnitude of such databases often re...

Integrating Remote Patient Monitoring Data into Machine Learning Models for Predicting Emergency Department Utilization.

The integration of Remote Patient Monitoring (RPM) data into risk stratification models has emerged as a promising approach for improving healthcare d...

Optimizing Medication Querying Using Ontology-Driven Approach with OMOP: with an application to a large-scale COVID-19 EHR dataset.

Efficient querying for medication information in Electronic Health Record (EHR) datasets is crucial for effective patient care and clinical research. ...

A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes.

High-throughput phenotyping, the automated mapping of patient signs and symptoms to standardized ontology concepts, is essential for realizing value f...

Enhancing Semantic and Structure Modeling of Diseases for Diagnosis Prediction.

Electronic Health Records (EHRs) are valuable healthcare data, aiding researchers and doctors in improving diagnosis accuracy. Researchers have develo...

Combining Rule-based NLP-lite with Rapid Iterative Chart Adjudication for Creation of a Large, Accurately Curated Cohort from EHR data: A Case Study in the Context of a Clinical Trial Emulation.

The aim of this work was to create a gold-standard curated cohort of 10,000+ cases from the Veteran Affairs (VA) corporate data warehouse (CDW) for vi...

Federated Diabetes Prediction in Canadian Adults Using Real-world Cross-Province Primary Care Data.

Integrating Electronic Health Records (EHR) and the application of machine learning present opportunities for enhancing the accuracy and accessibility...

Optimizing Large Language Models for Discharge Prediction: Best Practices in Leveraging Electronic Health Record Audit Logs.

Electronic Health Record (EHR) audit log data are increasingly utilized for clinical tasks, from workflow modeling to predictive analyses of discharge...

BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records.

The advent of clinical language models integrated into electronic health records (EHR) for clinical decision support has marked a significant advancem...

Generative AI Demonstrated Difficulty Reasoning on Nursing Flowsheet Data.

Excessive documentation burden is linked to clinician burnout, thus motivating efforts to reduce burden. Generative artificial intelligence (AI) poses...

A Novel Sentence Transformer-based Natural Language Processing Approach for Schema Mapping of Electronic Health Records to the OMOP Common Data Model.

Mapping electronic health records (EHR) data to common data models (CDMs) enables the standardization of clinical records, enhancing interoperability ...

Artificial intelligence and health information: A bibliometric analysis of three decades of research.

Information on the application of artificial intelligence (AI) in healthcare is needed to align healthcare transformation efforts. This bibliometric a...

Dilemmas and prospects of artificial intelligence technology in the data management of medical informatization in China: A new perspective on SPRAY-type AI applications.

This study aims to address the critical challenges of data integrity, accuracy, consistency, and precision in the application of electronic medical r...

Current Scenario and Future Prospects of Adverse Drug Reactions (ADRs) Monitoring and Reporting Mechanisms in the Rural Areas of India.

Pharmacovigilance (PV) deals with the detection, collection, assessment, understanding, and prevention of adverse effects associated with drugs. The o...

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With the emergence of innovations and technological advancements – exemplified by telemedicine and more recently by the extremely rapid developm...

The Growing Impact of Natural Language Processing in Healthcare and Public Health.

Natural Language Processing (NLP) is a subset of Artificial Intelligence, specifically focused on understanding and generating human language. NLP tec...