DOI: 10.5061/dryad.0vt4b8h3g
title: Extraction of clinical phenotypes for Alzheimer disease dementia from clinical notes using natural language processing

description: Objectives There is much interest in utilizing clinical data for
 developing prediction models for Alzheimer disease (AD) risk, progression,
 and outcomes. Existing studies have mostly utilized curated research
 registries, image analysis, and structured Electronic Health Record (EHR)
 data. However, much critical information resides in relatively
 inaccessible unstructured clinical notes within the EHR. Materials and
 Methods We developed a natural language processing (NLP)-based pipeline to
 extract AD-related clinical phenotypes, documenting strategies for success
 and assessing the utility of mining unstructured clinical notes. We
 evaluated the pipeline against gold-standard manual annotations performed
 by two clinical dementia experts for AD-related clinical phenotypes
 including medical comorbidities, biomarkers, neurobehavioral test scores,
 behavioral indicators of cognitive decline, family history, and
 neuroimaging findings. Results Documentation rates for each phenotype
 varied in the structured versus unstructured EHR. Inter-annotator
 agreement was high (Cohen’s kappa = 0.72–1) and positively correlated with
 the NLP-based phenotype extraction pipeline’s performance (average
 F1-score = 0.65-0.99) for each phenotype. Discussion We developed an
 automated NLP-based pipeline to extract informative phenotypes that may
 improve the performance of eventual machine-learning predictive models for
 AD. In the process, we examined documentation practices for each phenotype
 relevant to the care of AD patients and identified factors for success.
 Conclusion Success of our NLP-based phenotype extraction pipeline depended
 on domain-specific knowledge and focus on a specific clinical domain
 instead of maximizing generalizability.
description: We developed a natural language processing (NLP)-based pipeline
 which contains independent NLP modules that target the extraction of ten
 clinical phenotypes relevant to Alzheimer disease dementia progression.
 The pipeline was trained on unstructured clinical notes originating from
 Allscripts TouchWorks associated with AD dementia patient office vsits
 that occurred between June 1, 2013, to May 31, 2018, extracted from the
 Washington University in St. Louis Research Data Core (RDC), a repository
 of patient clinical data from BJC HealthCare and Washington University
 Physicians. The targeted phenotypes included neurobehavioral test scores
 (Clinical Dementia Rating and Mini-Mental State Exam) and their
 corresponding test dates, comorbidities (hypertension and depression),
 neuroimaging findings (presence of atrophy or infarct), behavioral
 indicators of dementia (repeating and misplacing), biomarker levels (total
 and phosphorylated tau protein levels), and family history (whether there
 was a family history of dementia, and if yes, which family
 member(s)). The clinical notes extracted from EHR were
 in rich text format (RTF) contained within tab-delimited files (TXT)
 alongside metadata such as the patient medical record number, author, and
 date authored. These were preprocessed before being analyzed by the
 NLP-based phenotype extraction pipeline. This entailed converting the TXT
 files to comma-separated files (CSV), accounting for additional tab,
 quote, and newline characters present, and stripping the RTF
 formatting.
description: Data preprocessing steps were performed using the Python
 <em>Pandas</em> and <em>striprtf</em> (version
 0.0.10) packages. Linguamatics I2E query files (*.i2qy)
 and Enterprise Architect Simulation Library (EASL) code for each NLP
 module can be found on the Linguamatics Community webpage (https://community.linguamatics.com/queries), accessible with the creation of a free account. Linguamatics I2E software is required to open the query files (*.i2qy) directly, but the logic underlying the NLP modules can be understood by referencing the EASL code.