AIMC Topic: Natural Language Processing

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Annotating Temporal Relations to Determine the Onset of Psychosis Symptoms.

Studies in health technology and informatics
For patients with a diagnosis of schizophrenia, determining symptom onset is crucial for timely and successful intervention. In mental health records, information about early symptoms is often documented only in free text, and thus needs to be extrac...

Identifying Suicidal Adolescents from Mental Health Records Using Natural Language Processing.

Studies in health technology and informatics
Suicidal ideation is a risk factor for self-harm, completed suicide and can be indicative of mental health issues. Adolescents are a particularly vulnerable group, but few studies have examined suicidal behaviour prevalence in large cohorts. Electron...

Identifying Diabetes in Clinical Notes in Hebrew: A Novel Text Classification Approach Based on Word Embedding.

Studies in health technology and informatics
NimbleMiner is a word embedding-based, language-agnostic natural language processing system for clinical text classification. Previously, NimbleMiner was applied in English and this study applied NimbleMiner on a large sample of inpatient clinical no...

An Exploratory Study on Pseudo-Data Generation in Prescription and Adverse Drug Reaction Extraction.

Studies in health technology and informatics
Prescription information and adverse drug reactions (ADR) are two components of detailed medication instructions that can benefit many aspects of clinical research. Automatic extraction of this information from free-text narratives via Information Ex...

Recurrent Deep Network Models for Clinical NLP Tasks: Use Case with Sentence Boundary Disambiguation.

Studies in health technology and informatics
Although a number of foundational natural language processing (NLP) tasks like text segmentation are considered a simple problem in the general English domain dominated by well-formed text, complexities of clinical documentation lead to poor performa...

Learning Portuguese Clinical Word Embeddings: A Multi-Specialty and Multi-Institutional Corpus of Clinical Narratives Supporting a Downstream Biomedical Task.

Studies in health technology and informatics
In this paper, we trained a set of Portuguese clinical word embedding models of different granularities from multi-specialty and multi-institutional clinical narrative datasets. Then, we assessed their impact on a downstream biomedical NLP task of Ur...

Word Embedding for French Natural Language in Healthcare: A Comparative Study.

Studies in health technology and informatics
Structuring raw medical documents with ontology mapping is now the next step for medical intelligence. Deep learning models take as input mathematically embedded information, such as encoded texts. To do so, word embedding methods can represent every...

Automatic Methods to Extract Prescription Status Quality Measures from Unstructured Health Records.

Studies in health technology and informatics
Hospital systems frequently implement quality measures to quantify healthcare processes and patient outcomes. One such measure that has previously been used is the Surgical Care Improvement Project (SCIP) quality measure of perioperative beta blocker...

Enhancing Case Capture, Quality, and Completeness of Primary Melanoma Pathology Records via Natural Language Processing.

JCO clinical cancer informatics
PURPOSE: Medical records contain a wealth of useful, informative data points valuable for clinical research. Most data points are stored in semistructured or unstructured legacy documents and require manual data abstraction into a structured format t...

Validation of a Semiautomated Natural Language Processing-Based Procedure for Meta-Analysis of Cancer Susceptibility Gene Penetrance.

JCO clinical cancer informatics
PURPOSE: Quantifying the risk of cancer associated with pathogenic mutations in germline cancer susceptibility genes-that is, penetrance-enables the personalization of preventive management strategies. Conducting a meta-analysis is the best way to ob...