AI Medical Compendium Topic:
Electronic Health Records

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Achievability to Extract Specific Date Information for Cancer Research.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Accurate identification of temporal information such as date is crucial for advancing cancer research which often requires precise date information associated with related cancer events. However, there is a gap for existing natural language processin...

Using Natural Language Processing to improve EHR Structured Data-based Surgical Site Infection Surveillance.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Surgical Site Infection surveillance in healthcare systems is labor intensive and plagued by underreporting as current methodology relies heavily on manual chart review. The rapid adoption of electronic health records (EHRs) has the potential to allo...

Interpretation of machine learning predictions for patient outcomes in electronic health records.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Electronic health records are an increasingly important resource for understanding the interactions between patient health, environment, and clinical decisions. In this paper we report an empirical study of predictive modeling of seven patient outcom...

Regional Variations in Documentation of Sexual Trauma Concepts in Electronic Medical Records in the United States Veterans Health Administration.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Experiences of sexual trauma are associated with adverse patient and health system outcomes, but are not systematically documented in electronic health records (EHR). To describe variations in how sexual trauma is documented in the Veterans Health ...

Machine Learned Mapping of Local EHR Flowsheet Data to Standard Information Models using Topic Model Filtering.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Electronic health record (EHR) data must be mapped to standard information models for interoperability and to support research across organizations. New information models are being developed and validated for data important to nursing, but a signifi...

Machine Learning Based Opioid Overdose Prediction Using Electronic Health Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Opioid addiction in the United States has come to national attention as opioid overdose (OD) related deaths have risen at alarming rates. Combating opioid epidemic becomes a high priority for not only governments but also healthcare providers. This d...

Predicting Adverse Drug Reactions on Distributed Health Data using Federated Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Using electronic health data to predict adverse drug reaction (ADR) incurs practical challenges, such as lack of adequate data from any single site for rare ADR detection, resource constraints on integrating data from multiple sources, and privacy co...

Determination of Marital Status of Patients from Structured and Unstructured Electronic Healthcare Data.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Social Determinants of Health, including marital status, are becoming increasingly identified as key drivers of health care utilization. This paper describes a robust method to determine the marital status of patients using structured and unstructure...

Towards Reliable ARDS Clinical Decision Support: ARDS Patient Analytics with Free-text and Structured EMR Data.

AMIA ... Annual Symposium proceedings. AMIA Symposium
In this work, we utilize a combination of free-text and structured data to build Acute Respiratory Distress Syndrome(ARDS) prediction models and ARDS phenotype clusters. We derived 'Patient Context Vectors' representing patientspecific contextual ARD...

Evaluating the Portability of an NLP System for Processing Echocardiograms: A Retrospective, Multi-site Observational Study.

AMIA ... Annual Symposium proceedings. AMIA Symposium
While natural language processing (NLP) of unstructured clinical narratives holds the potential for patient care and clinical research, portability of NLP approaches across multiple sites remains a major challenge. This study investigated the portabi...