AIMC Topic: Electronic Health Records

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Interpretable Deep Models for ICU Outcome Prediction.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Exponential surge in health care data, such as longitudinal data from electronic health records (EHR), sensor data from intensive care unit (ICU), etc., is providing new opportunities to discover meaningful data-driven characteristics and patterns of...

Mining peripheral arterial disease cases from narrative clinical notes using natural language processing.

Journal of vascular surgery
OBJECTIVE: Lower extremity peripheral arterial disease (PAD) is highly prevalent and affects millions of individuals worldwide. We developed a natural language processing (NLP) system for automated ascertainment of PAD cases from clinical narrative n...

Comprehensible knowledge model creation for cancer treatment decision making.

Computers in biology and medicine
BACKGROUND: A wealth of clinical data exists in clinical documents in the form of electronic health records (EHRs). This data can be used for developing knowledge-based recommendation systems that can assist clinicians in clinical decision making and...

Development and empirical user-centered evaluation of semantically-based query recommendation for an electronic health record search engine.

Journal of biomedical informatics
OBJECTIVE: The utility of biomedical information retrieval environments can be severely limited when users lack expertise in constructing effective search queries. To address this issue, we developed a computer-based query recommendation algorithm th...

Artificial intelligence in medicine.

Metabolism: clinical and experimental
Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech ...

An Evolving Ecosystem for Natural Language Processing in Department of Veterans Affairs.

Journal of medical systems
In an ideal clinical Natural Language Processing (NLP) ecosystem, researchers and developers would be able to collaborate with others, undertake validation of NLP systems, components, and related resources, and disseminate them. We captured requireme...

Development of Type 2 Diabetes Mellitus Phenotyping Framework Using Expert Knowledge and Machine Learning Approach.

Journal of diabetes science and technology
BACKGROUND: Phenotyping is an automated technique that can be used to distinguish patients based on electronic health records. To improve the quality of medical care and advance type 2 diabetes mellitus (T2DM) research, the demand for T2DM phenotypin...

Large-scale identification of patients with cerebral aneurysms using natural language processing.

Neurology
OBJECTIVE: To use natural language processing (NLP) in conjunction with the electronic medical record (EMR) to accurately identify patients with cerebral aneurysms and their matched controls.