AIMC Topic: Natural Language Processing

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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...

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...

Assessing Information Congruence of Documented Cardiovascular Disease between Electronic Dental and Medical Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Dentists are more often treating patients with Cardiovascular Diseases (CVD) in their clinics; therefore, dentists may need to alter treatment plans in the presence of CVD. However, it's unclear to what extent patient-reported CVD information is accu...

Utility of General and Specific Word Embeddings for Classifying Translational Stages of Research.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Conventional text classification models make a bag-of-words assumption reducing text into word occurrence counts per document. Recent algorithms such as word2vec are capable of learning semantic meaning and similarity between words in an entirely uns...

Disease comorbidity-guided drug repositioning: a case study in schizophrenia.

AMIA ... Annual Symposium proceedings. AMIA Symposium
UNLABELLED: The key to any computational drug repositioning is the availability of relevant data in machine-understandable format. While large amount of genetic, genomic and chemical data are publicly available, large-scale higher-level disease and d...

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...

The Role of a Deep-Learning Method for Negation Detection in Patient Cohort Identification from Electroencephalography Reports.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Detecting negation in biomedical texts entails the automatic identification of negation cues (e.g. "never", "not", "no longer") as well as the scope of these cues. When medical concepts or terms are identified within the scope of a negation cue, thei...

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...

Ensemble-based Methods to Improve De-identification of Electronic Health Record Narratives.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Text de-identification is an application of clinical natural language processing that offers significant efficiency and scalability advantages. Hence, various learning algorithms have been applied to this task to yield better performance. Instead of ...