AIMC Topic: Natural Language Processing

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Automatic data source identification for clinical trial eligibility criteria resolution.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Clinical trial coordinators refer to both structured and unstructured sources of data when evaluating a subject for eligibility. While some eligibility criteria can be resolved using structured data, some require manual review of clinical notes. An i...

Resource Classification for Medical Questions.

AMIA ... Annual Symposium proceedings. AMIA Symposium
We present an approach for manually and automatically classifying the resource type of medical questions. Three types of resources are considered: patient-specific, general knowledge, and research. Using this approach, an automatic question answering...

Using Natural Language Processing and Network Analysis to Develop a Conceptual Framework for Medication Therapy Management Research.

AMIA ... Annual Symposium proceedings. AMIA Symposium
This paper describes a theory derivation process used to develop a conceptual framework for medication therapy management (MTM) research. The MTM service model and chronic care model were selected as parent theories. Review article abstracts targetin...

Combining Open-domain and Biomedical Knowledge for Topic Recognition in Consumer Health Questions.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Determining the main topics in consumer health questions is a crucial step in their processing as it allows narrowing the search space to a specific semantic context. In this paper we propose a topic recognition approach based on biomedical and open-...

CMedTEX: A Rule-based Temporal Expression Extraction and Normalization System for Chinese Clinical Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Time is an important aspect of information and is very useful for information utilization. The goal of this study was to analyze the challenges of temporal expression (TE) extraction and normalization in Chinese clinical notes by assessing the perfor...

Towards Comprehensive Clinical Abbreviation Disambiguation Using Machine-Labeled Training Data.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Abbreviation disambiguation in clinical texts is a problem handled well by fully supervised machine learning methods. Acquiring training data, however, is expensive and would be impractical for large numbers of abbreviations in specialized corpora. A...

Understanding patient satisfaction with received healthcare services: A natural language processing approach.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Important information is encoded in free-text patient comments. We determine the most common topics in patient comments, design automatic topic classifiers, identify comments ' sentiment, and find new topics in negative comments. Our annotation schem...

Automatic Generation of Conditional Diagnostic Guidelines.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The diagnostic workup for many diseases can be extraordinarily nuanced, and as such reference material text often contains extensive information regarding when it is appropriate to have a patient undergo a given procedure. In this work we employ a th...

RevManHAL: towards automatic text generation in systematic reviews.

Systematic reviews
BACKGROUND: Systematic reviews are a key part of healthcare evaluation. They involve important painstaking but repetitive work. A major producer of systematic reviews, the Cochrane Collaboration, employs Review Manager (RevMan) programme-a software w...

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...