AIMC Topic: Natural Language Processing

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Obtaining Knowledge in Pathology Reports Through a Natural Language Processing Approach With Classification, Named-Entity Recognition, and Relation-Extraction Heuristics.

JCO clinical cancer informatics
PURPOSE: Robust institutional tumor banks depend on continuous sample curation or else subsequent biopsy or resection specimens are overlooked after initial enrollment. Curation automation is hindered by semistructured free-text clinical pathology no...

Underserved populations with missing race ethnicity data differ significantly from those with structured race/ethnicity documentation.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We aimed to address deficiencies in structured electronic health record (EHR) data for race and ethnicity by identifying black and Hispanic patients from unstructured clinical notes and assessing differences between patients with or withou...

Identifying vulnerable older adult populations by contextualizing geriatric syndrome information in clinical notes of electronic health records.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Geriatric syndromes such as functional disability and lack of social support are often not encoded in electronic health records (EHRs), thus obscuring the identification of vulnerable older adults in need of additional medical and social s...

Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing Algorithm.

Journal of digital imaging
Radiological measurements are reported in free text reports, and it is challenging to extract such measures for treatment planning such as lesion summarization and cancer response assessment. The purpose of this work is to develop and evaluate a natu...

Toward Complete Structured Information Extraction from Radiology Reports Using Machine Learning.

Journal of digital imaging
Unstructured and semi-structured radiology reports represent an underutilized trove of information for machine learning (ML)-based clinical informatics applications, including abnormality tracking systems, research cohort identification, point-of-car...

Automatic extraction of imaging observation and assessment categories from breast magnetic resonance imaging reports with natural language processing.

Chinese medical journal
BACKGROUND: Structured reports are not widely used and thus most reports exist in the form of free text. The process of data extraction by experts is time-consuming and error-prone, whereas data extraction by natural language processing (NLP) is a po...

Named Entity Recognition and Classification for Medical Prospectuses.

Studies in health technology and informatics
Structuring and processing natural language is a growing challenge in the medical field. Researchers are looking for new ways to extract knowledge to create databases and applications to help doctors treat patients and minimize medical errors. A very...

An Information Extraction Algorithm for Detecting Adverse Events in Neurosurgery Using Documents Written in a Natural Rich-in-Morphology Language.

Studies in health technology and informatics
Rich-in-morphology language, such as Russian, present a challenge for extraction of professional medical information. In this paper, we report on our solution to identify adverse events (complications) in neurosurgery based on natural language proces...

Prediction of Personal Experience Tweets of Medication Use via Contextual Word Representations.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Continuous monitoring the safe use of medication is an important task in pharmacovigilance. The first-hand experiences of medication effects come from the consumers of the pharmaceuticals. Social media have been considered as a possible alternative d...

Automating the Capture of Structured Pathology Data for Prostate Cancer Clinical Care and Research.

JCO clinical cancer informatics
PURPOSE: Cancer pathology findings are critical for many aspects of care but are often locked away as unstructured free text. Our objective was to develop a natural language processing (NLP) system to extract prostate pathology details from postopera...