AIMC Topic: Electronic Health Records

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EHR phenotyping via jointly embedding medical concepts and words into a unified vector space.

BMC medical informatics and decision making
BACKGROUND: There has been an increasing interest in learning low-dimensional vector representations of medical concepts from Electronic Health Records (EHRs). Vector representations of medical concepts facilitate exploratory analysis and predictive ...

Chronic Kidney Disease stratification using office visit records: Handling data imbalance via hierarchical meta-classification.

BMC medical informatics and decision making
BACKGROUND: Chronic Kidney Disease (CKD) is one of several conditions that affect a growing percentage of the US population; the disease is accompanied by multiple co-morbidities, and is hard to diagnose in-and-of itself. In its advanced forms it car...

Use of natural language processing in electronic medical records to identify pregnant women with suicidal behavior: towards a solution to the complex classification problem.

European journal of epidemiology
We developed algorithms to identify pregnant women with suicidal behavior using information extracted from clinical notes by natural language processing (NLP) in electronic medical records. Using both codified data and NLP applied to unstructured cli...

Validation of Prediction Models for Critical Care Outcomes Using Natural Language Processing of Electronic Health Record Data.

JAMA network open
IMPORTANCE: Accurate prediction of outcomes among patients in intensive care units (ICUs) is important for clinical research and monitoring care quality. Most existing prediction models do not take full advantage of the electronic health record, usin...

A comparative quantitative study of utilizing artificial intelligence on electronic health records in the USA and China during 2008-2017.

BMC medical informatics and decision making
BACKGROUND: The application of artificial intelligence techniques for processing electronic health records data plays increasingly significant role in advancing clinical decision support. This study conducts a quantitative comparison on the research ...

Selecting Test Cases from the Electronic Health Record for Software Testing of Knowledge-Based Clinical Decision Support Systems.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Software testing of knowledge-based clinical decision support systems is challenging, labor intensive, and expensive; yet, testing is necessary since clinical applications have heightened consequences. Thoughtful test case selection improves testing ...

Clinical text annotation - what factors are associated with the cost of time?

AMIA ... Annual Symposium proceedings. AMIA Symposium
Building high-quality annotated clinical corpora is necessary for developing statistical Natural Language Processing (NLP) models to unlock information embedded in clinical text, but it is also time consuming and expensive. Consequently, it important...

FABLE: A Semi-Supervised Prescription Information Extraction System.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Prescription information is an important component of electronic health records (EHRs). This information contains detailed medication instructions that are crucial for patients' well-being and is often detailed in the narrative portions of EHRs. As a...

A Frame-Based NLP System for Cancer-Related Information Extraction.

AMIA ... Annual Symposium proceedings. AMIA Symposium
We propose a frame-based natural language processing (NLP) method that extracts cancer-related information from clinical narratives. We focus on three frames: cancer diagnosis, cancer therapeutic procedure, and tumor description. We utilize a deep le...