AIMC Topic: Natural Language Processing

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Unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients.

Journal of biomedical informatics
BACKGROUND: Allowing patients to access their own electronic health record (EHR) notes through online patient portals has the potential to improve patient-centered care. However, EHR notes contain abundant medical jargon that can be difficult for pat...

Relating Complexity and Error Rates of Ontology Concepts. More Complex NCIt Concepts Have More Errors.

Methods of information in medicine
OBJECTIVES: Ontologies are knowledge structures that lend support to many health-information systems. A study is carried out to assess the quality of ontological concepts based on a measure of their complexity. The results show a relation between com...

Structuring Legacy Pathology Reports by openEHR Archetypes to Enable Semantic Querying.

Methods of information in medicine
BACKGROUND: Clinical information is often stored as free text, e.g. in discharge summaries or pathology reports. These documents are semi-structured using section headers, numbered lists, items and classification strings. However, it is still challen...

Early recognition of multiple sclerosis using natural language processing of the electronic health record.

BMC medical informatics and decision making
BACKGROUND: Diagnostic accuracy might be improved by algorithms that searched patients' clinical notes in the electronic health record (EHR) for signs and symptoms of diseases such as multiple sclerosis (MS). The focus this study was to determine if ...

Creation of a simple natural language processing tool to support an imaging utilization quality dashboard.

International journal of medical informatics
BACKGROUND: Testing for venous thromboembolism (VTE) is associated with cost and risk to patients (e.g. radiation). To assess the appropriateness of imaging utilization at the provider level, it is important to know that provider's diagnostic yield (...

Clinical Word Sense Disambiguation with Interactive Search and Classification.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Resolving word ambiguity in clinical text is critical for many natural language processing applications. Effective word sense disambiguation (WSD) systems rely on training a machine learning based classifier with abundant clinical text that is accura...

Improving Endpoint Detection to Support Automated Systematic Reviews.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Authors of biomedical articles use comparison sentences to communicate the findings of a study, and to compare the results of the current study with earlier studies. The Claim Framework defines a comparison claim as a sentence that includes at least ...

Ensembles of NLP Tools for Data Element Extraction from Clinical Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Natural Language Processing (NLP) is essential for concept extraction from narrative text in electronic health records (EHR). To extract numerous and diverse concepts, such as data elements (i.e., important concepts related to a certain medical condi...

Semantic Role Labeling of Clinical Text: Comparing Syntactic Parsers and Features.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Semantic role labeling (SRL), which extracts shallow semantic relation representation from different surface textual forms of free text sentences, is important for understanding clinical narratives. Since semantic roles are formed by syntactic consti...

Differentiating Sense through Semantic Interaction Data.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Words which have different representations but are semantically related, such as dementia and delirium, can pose difficult issues in understanding text. We explore the use of interaction frequency data between semantic elements as a means to differen...