Uncertainty-Aware Prediction of Differential Responses to Antidepressants: Leveraging EHR and Genomics
About This Grant
Background: Depression is a serious mental disorder, with treatment selection largely relying on trial and error, often prolonging patients' suffering. The increased availability of electronic health records (EHRs) and advancements in AI offer new opportunities to address this clinical challenge. However, current EHR-based approaches have shortcomings: a. they underutilize information in unstructured data that could be important for outcome prediction and confounding adjustments; b. they lack accuracy in cohort definition and treatment response assessments; c. they omit genomic information, which is known to affect treatment response; and d. they are not aware of uncertainties arising from the fitness of assumptions required to produce reliable predictions, potentially providing misleading estimates. In addition, genetic tests currently available are limited to select genetic variations, failing to utilize information from the full genome. Research: We propose to address these limitations by crafting advanced AI models for predicting differential antidepressant treatment responses, leveraging the latest developments in natural language processing (NLP), predictive modeling, causal inference, and the inclusion of both EHR and genomic data. Aim 1 will involve developing a large language model-based, human-in-the-loop active learning framework to identify an incident-user cohort started on antidepressants for depression, assess treatment responses, and extract key depression-related information from clinical notes. Aim 2 will develop uncertainty-aware, EHR-based prediction models for differential antidepressant responses, accounting for cases where a patient-antidepressant pairing falls outside the training data and for residual confounding. Aim 3 will combine EHR and three classes of genomic predictors for response prediction: genome-wide and pathway-specific polygenic risk scores, and variations associated with cytochrome P450 enzymes. This effort will enhance our understanding of integrating EHR and genomic data to predict personalized treatment responses, paving the way for future comprehensive systems. Candidate's Career Development, Goals, and Environment: The research objectives and the candidate's career development will be facilitated by the abundant resources at Massachusetts General Hospital and Harvard Medical School, as well as formal training and mentorship in (G1) advanced clinical NLP, (G2) integration and analysis of large-scale EHR and genomic data, (G3) ‘causal machine learning’ and its uncertainty assessments, and (G4) grantsmanship, leadership, effective collaborations, and research management. The mentorship team comprises Mentor Dr. Jordan Smoller, a leader in precision psychiatry and clinical predictive analytics; Co-Mentor Dr. Tianxi Cai, an authority in bioinformatics and healthcare predictive modeling; and Consultants Dr. Timothy Miller, an expert in NLP and AI, Dr. Issa Dahabreh, a specialist in causal inference, and Dr. Tian Ge, a renowned statistician and geneticist. This award will equip the candidate with the advanced skillset to become an independent researcher in precision psychiatry.
Grant Summary
Uncertainty-Aware Prediction of Differential Responses to Antidepressants: Leveraging EHR and Genomics is a NIMH - National Institute of Mental Health grant providing up to $791K for university, nonprofit, healthcare org. Applications are due 2030-03-31 (open). Check eligibility and apply with FindGrants.
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Up to $791K
2030-03-31
- 1Confirm your organization is eligible for Uncertainty-Aware Prediction of Differential Responses to Antidepressants: Leveraging EHR and Genomics from NIMH - National Institute of Mental Health, checking organization type, location, and any population or project requirements.
- 2Gather the required documents and information, including your organization details, project plan, and budget figures.
- 3Draft your application narrative and budget addressing the funder's priorities and review criteria. FindGrants can draft each section for you to review and edit.
- 4Review every section against the requirements checklist, then export a submission-ready application pack and submit it to NIMH - National Institute of Mental Health before the deadline.
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Uncertainty-Aware Prediction of Differential Responses to Antidepressants: Leveraging EHR and Genomics: Frequently Asked Questions
Who is eligible for the Uncertainty-Aware Prediction of Differential Responses to Antidepressants: Leveraging EHR and Genomics?
Uncertainty-Aware Prediction of Differential Responses to Antidepressants: Leveraging EHR and Genomics is offered by NIMH - National Institute of Mental Health and is generally open to university, nonprofit, healthcare org. It is open to organizations nationwide unless the funder specifies otherwise. Review the specific eligibility terms before applying, since funders set their own requirements around organization type, location, and the population or project being served.
How much funding does the Uncertainty-Aware Prediction of Differential Responses to Antidepressants: Leveraging EHR and Genomics provide?
Uncertainty-Aware Prediction of Differential Responses to Antidepressants: Leveraging EHR and Genomics provides up to $791K per award from NIMH - National Institute of Mental Health. Actual award sizes depend on the scope of your project, available program funds, and the number of applicants, so build a budget that reflects realistic, allowable costs rather than the maximum figure.
When is the Uncertainty-Aware Prediction of Differential Responses to Antidepressants: Leveraging EHR and Genomics deadline?
Applications for Uncertainty-Aware Prediction of Differential Responses to Antidepressants: Leveraging EHR and Genomics are due 2030-03-31 (open). Because deadlines can change, verify the date with the funder, NIMH - National Institute of Mental Health, and give yourself enough time to prepare a complete, competitive application before the close date.
How do you apply for the Uncertainty-Aware Prediction of Differential Responses to Antidepressants: Leveraging EHR and Genomics?
To apply for Uncertainty-Aware Prediction of Differential Responses to Antidepressants: Leveraging EHR and Genomics, confirm your eligibility, gather the required documents, and prepare a narrative and budget that address the funder's priorities. FindGrants guides you step by step and can draft each section, then exports a submission-ready application pack for this grant from NIMH - National Institute of Mental Health.