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Unified Medical Language System

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The Sublanguage of Clinical Problem Lists: A Corpus Analysis.

AMIA ... Annual Symposium proceedings. AMIA Symposium
.Summary-level clinical text is an important part of the overall clinical record as it provides a condensed and efficient view into the issues pertinent to the patient, or their "problem list." These problem lists contain a wealth of information pert...

Combine Factual Medical Knowledge and Distributed Word Representation to Improve Clinical Named Entity Recognition.

AMIA ... Annual Symposium proceedings. AMIA Symposium
There has been an increasing interest in developing deep learning methods to recognize clinical concepts from narrative clinical text. Recently, several studies have reported that Recurrent Neural Networks (RNNs) outperformed traditional machine lear...

A Preliminary Study of Clinical Concept Detection Using Syntactic Relations.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Concept detection is an integral step in natural language processing (NLP) applications in the clinical domain. Clinical concepts are detailed (e.g., "pain in left/right upper/lower arm/leg") and expressed in diverse phrase types (e.g., noun, verb, a...

Computer-Assisted Diagnostic Coding: Effectiveness of an NLP-based approach using SNOMED CT to ICD-10 mappings.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Computer-assisted (diagnostic) coding (CAC) aims to improve the operational productivity and accuracy of clinical coders. The level of accuracy, especially for a wide range of complex and less prevalent clinical cases, remains an open research proble...

A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Acute Respiratory Distress Syndrome (ARDS) is a syndrome of respiratory failure that may be identified using text from radiology reports. The objective of this study was to determine whether natural language processing (NLP) with machine learning per...

CIBS: A biomedical text summarizer using topic-based sentence clustering.

Journal of biomedical informatics
Automatic text summarizers can reduce the time required to read lengthy text documents by extracting the most important parts. Multi-document summarizers should produce a summary that covers the main topics of multiple related input texts to diminish...

Comparison of MetaMap and cTAKES for entity extraction in clinical notes.

BMC medical informatics and decision making
BACKGROUND: Clinical notes such as discharge summaries have a semi- or unstructured format. These documents contain information about diseases, treatments, drugs, etc. Extracting meaningful information from them becomes challenging due to their narra...

A comparison of word embeddings for the biomedical natural language processing.

Journal of biomedical informatics
BACKGROUND: Word embeddings have been prevalently used in biomedical Natural Language Processing (NLP) applications due to the ability of the vector representations being able to capture useful semantic properties and linguistic relationships between...

Risk prediction using natural language processing of electronic mental health records in an inpatient forensic psychiatry setting.

Journal of biomedical informatics
OBJECTIVE: Instruments rating risk of harm to self and others are widely used in inpatient forensic psychiatry settings. A potential alternate or supplementary means of risk prediction is from the automated analysis of case notes in Electronic Health...

Evaluating semantic relations in neural word embeddings with biomedical and general domain knowledge bases.

BMC medical informatics and decision making
BACKGROUND: In the past few years, neural word embeddings have been widely used in text mining. However, the vector representations of word embeddings mostly act as a black box in downstream applications using them, thereby limiting their interpretab...