A tool for pulling NIMH RDoC informed transdiagnostic phenotypes from medical documentation.
This code is described in High Throughput Phenotyping for Dimensional Psychopathology in Electronic Health Records. This approach to RDoC symptom estimates conceptually follows that reported in A Clinical Perspective on the Relevance of Research Domain Criteria in Electronic Health Records but with an increased focus on ease of distribution.
This software has been applied in:
- Genome-wide Association Study of Dimensional Psychopathology Using Electronic Health Records
- Stratifying risk for dementia onset using large-scale electronic health record data: a retrospective cohort study
- Research Domain Criteria scores estimated through natural language processing are associated with risk for suicide and accidental death
- Association between child psychiatric emergency room outcomes and dimensions of psychopathology
- Differences among Research Domain Criteria score trajectories by Diagnostic and Statistical Manual categorical diagnosis during inpatient hospitalization
- Electronic Health Record Documentation of Psychiatric Assessments in Massachusetts Emergency Department and Outpatient Settings During the Coronavirus Disease 2019 (COVID-19) Pandemic
- Mapping of Transdiagnostic Neuropsychiatric Phenotypes Across Patients in Two General Hospitals
- Mood Disorders and Outcomes of COVID-19 Hospitalizations
- Case-control study of neuropsychiatric symptoms in electronic health records following COVID-19 hospitalization in 2 academic health systems. Molecular Psychiatry 27.9 (2022): 3898-3903.
- Dimensional clinical phenotyping using post‐mortem brain donor medical records: post‐mortem RDoC profiling is associated with Alzheimer's disease neuropathology. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 15.3 (2023): e12464.
- Dimensional measures of psychopathology in children and adolescents using large language models. Biological Psychiatry (2024).
- Characterizing research domain criteria symptoms among psychiatric inpatients using large language models. Journal of Mood & Anxiety Disorders (2024): 100079.
This was developed on Python 2.7 and depends only on the standard lib. It accepts standard in, as such:
cat your_document.txt | python2 CQHDimensionalPhenotyper.py
For any serious usage you are likely to want to treat CQHDimensionalPhenotyper.py as a library wrapped in your own project specific batch data handling.