Using Machine Learning to Predict Antidepressant Treatment Outcome From Electronic Health Records.

Objective: To evaluate if a machine learning approach can accurately predict antidepressant treatment outcome using electronic health records (EHRs) from patients with depression.

Method: This study examined 808 patients with depression at a New York City-based outpatient mental health clinic between June 13, 2016 and June 22, 2020. Antidepressant treatment outcome was defined based on trend in depression symptom severity over time and was categorized as either "Recovering" or "Worsening" (i.

Prediction of left ventricular ejection fraction changes in heart failure patients using machine learning and electronic health records: a multi-site study.

Left ventricular ejection fraction (EF) is a key measure in the diagnosis and treatment of heart failure (HF) and many patients experience changes in EF overtime. Large-scale analysis of longitudinal changes in EF using electronic health records (EHRs) is limited. In a multi-site retrospective study using EHR data from three academic medical centers, we investigated longitudinal changes in EF measurements in patients diagnosed with HF.

Characterizing variability of electronic health record-driven phenotype definitions.

Objective: The aim of this study was to analyze a publicly available sample of rule-based phenotype definitions to characterize and evaluate the variability of logical constructs used.

Materials And Methods: A sample of 33 preexisting phenotype definitions used in research that are represented using Fast Healthcare Interoperability Resources and Clinical Quality Language (CQL) was analyzed using automated analysis of the computable representation of the CQL libraries.

Results: Most of the phenotype definitions include narrative descriptions and flowcharts, while few provide pseudocode or executable artifacts.

Design and validation of a FHIR-based EHR-driven phenotyping toolbox.

Objectives: To develop and validate a standards-based phenotyping tool to author electronic health record (EHR)-based phenotype definitions and demonstrate execution of the definitions against heterogeneous clinical research data platforms.

Materials And Methods: We developed an open-source, standards-compliant phenotyping tool known as the PhEMA Workbench that enables a phenotype representation using the Fast Healthcare Interoperability Resources (FHIR) and Clinical Quality Language (CQL) standards. We then demonstrated how this tool can be used to conduct EHR-based phenotyping, including phenotype authoring, execution, and validation.

Development of a repository of computable phenotype definitions using the clinical quality language.

Objective: The objective of this study is to create a repository of computable, technology-agnostic phenotype definitions for the purposes of analysis and automatic cohort identification.

Materials And Methods: We selected phenotype definitions from PheKB and excluded definitions that did not use structured data or were not used in published research. We translated these definitions into the Clinical Quality Language (CQL) and Fast Healthcare Interoperability Resources (FHIR) and validated them using code review and automated tests.

CQL4NLP: Development and Integration of FHIR NLP Extensions in Clinical Quality Language for EHR-driven Phenotyping.

Lack of standardized representation of natural language processing (NLP) components in phenotyping algorithms hinders portability of the phenotyping algorithms and their execution in a high-throughput and reproducible manner. The objective of the study is to develop and evaluate a standard-driven approach - CQL4NLP - that integrates a collection of NLP extensions represented in the HL7 Fast Healthcare Interoperability Resources (FHIR) standard into the clinical quality language (CQL). A minimal NLP data model with 11 NLP-specific data elements was created, including six FHIR NLP extensions.

StayHome: A FHIR-Native Mobile COVID-19 Symptom Tracker and Public Health Reporting Tool.

As the COVID-19 pandemic continues to unfold and states experience the impacts of reopened economies, it is critical to efficiently manage new outbreaks through widespread testing and monitoring of both new and possible cases. Existing labor-intensive public health workflows may benefit from information collection directly from individuals through patient-reported outcomes (PROs) systems. Our objective was to develop a reusable, mobile-friendly application for collecting PROs and experiences to support COVID-19 symptom self-monitoring and data sharing with appropriate public health agencies, using Fast Healthcare Interoperability Resources (FHIR) for interoperability.

Data-driven discovery of probable Alzheimer's disease and related dementia subphenotypes using electronic health records.

Introduction: We sought to assess longitudinal electronic health records (EHRs) using machine learning (ML) methods to computationally derive probable Alzheimer's Disease (AD) and related dementia subphenotypes.

Methods: A retrospective analysis of EHR data from a cohort of 7587 patients seen at a large, multi-specialty urban academic medical center in New York was conducted. Subphenotypes were derived using hierarchical clustering from 792 probable AD patients (cases) who had received at least one diagnosis of AD using their clinical data.

Subphenotyping depression using machine learning and electronic health records.

Objective: To identify depression subphenotypes from Electronic Health Records (EHRs) using machine learning methods, and analyze their characteristics with respect to patient demographics, comorbidities, and medications.

Materials And Methods: Using EHRs from the INSIGHT Clinical Research Network (CRN) database, multiple machine learning (ML) algorithms were applied to analyze 11 275 patients with depression to discern depression subphenotypes with distinct characteristics.

Results: Using the computational approaches, we derived three depression subphenotypes: Phenotype_A (n = 2791; 31.

Toward cross-platform electronic health record-driven phenotyping using Clinical Quality Language.

Introduction: Electronic health record (EHR)-driven phenotyping is a critical first step in generating biomedical knowledge from EHR data. Despite recent progress, current phenotyping approaches are manual, time-consuming, error-prone, and platform-specific. This results in duplication of effort and highly variable results across systems and institutions, and is not scalable or portable.

Considerations for Improving the Portability of Electronic Health Record-Based Phenotype Algorithms.

With the increased adoption of electronic health records, data collected for routine clinical care is used for health outcomes and population sciences research, including the identification of phenotypes. In recent years, research networks, such as eMERGE, OHDSI and PCORnet, have been able to increase statistical power and population diversity by combining patient cohorts. These networks share phenotype algorithms that are executed at each participating site.

Evaluating the Portability of an NLP System for Processing Echocardiograms: A Retrospective, Multi-site Observational Study.

While natural language processing (NLP) of unstructured clinical narratives holds the potential for patient care and clinical research, portability of NLP approaches across multiple sites remains a major challenge. This study investigated the portability of an NLP system developed initially at the Department of Veterans Affairs (VA) to extract 27 key cardiac concepts from free-text or semi-structured echocardiograms from three academic edical centers: Weill Cornell Medicine, Mayo Clinic and Northwestern Medicine. While the NLP system showed high precision and recall easurements for four target concepts (aortic valve regurgitation, left atrium size at end systole, mitral valve regurgitation, tricuspid valve regurgitation) across all sites, we found moderate or poor results for the remaining concepts and the NLP system performance varied between individual sites.