Friday, October 3, 2025 10/3/2025

Developing and Evaluating Multi-Modal Clinical Diagnostic Reasoning Models for Automated Diagnosis Generation

Award Number: R00LM014308
ORGANIZATION: NATIONAL LIBRARY OF MEDICINE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/01/2024
PERIOD OF PERFORMANCE END DATE: 08/31/2027

Group Awards By:

View Award Description

Developing and Evaluating Multi-Modal Clinical Diagnostic Reasoning Models for Automated Diagnosis Generation - PROJECT ABSTRACT Diagnostic errors affect 12 million patients in the U.S. and contribute to 80,000 deaths per year. The main causes for diagnostic errors include cognitive biases introduced by healthcare providers, miscommunication between healthcare teams, lack of access to key data, and not recognizing time-sensitive data in the electronic health record (EHR). The cognitive burden from information overload in the EHR cause clinicians to take decisional shortcuts with biased heuristics and miss critical data in the EHR, leading to missed opportunities for timely and accurate diagnoses. Artificial Intelligence (AI) and clinical Natural Language Processing (cNLP) provide opportunity to help understand medical text and can automate EHR analysis, pointing to the promising direction of invoking medical knowledge and clinical experience as humans do. However, the majority of the cNLP tasks are not designed for bedside application to generate diagnoses and augment bedside decision-making. We have have gathered preliminary data and designed cNLP benchmark tasks for clinical diagnostic reasoning. Our tasks address key cognitive processes to build models in this proposal that can synthesize EHR data to generate diagnoses that align with evidence-based medicine and medical knowledge representation. The proposal aims to develop novel cNLP models that understand and integrate multi-modal EHR data, and conduct reasoning over a large-scale medical knowledge base to build a model that provides higher accuracy than current neural network models. I will first develop a multi-modal generative model that reads in both structured and unstructured EHR data to output diagnoses using a two-stage training process (Aim 1). In a separate aim, I will construct a knowledge base using a neural symbolic approach from medical concepts and relations sourced from the National Library of Medicine's Unified Medical Language System (UMLS). The knowledge base will be part of the model to generate diagnoses given the information from a daily care note collected in the EHR (Aim 2). The third aim will design and pilot a clinical diagnostic decision support system using human-centered design principles. The best models from Aims 1 and 2 will be evaluated for diagnostic accuracy by clinicians in the system using previously validated instruments for patient safety and diagnostic error (Aim 3). Completion of the aims will inform future clinical studies on developing NLP-driven clinical decision support tools for reducing diagnostic error. I will complete this project under the direct supervision of my co-mentors and advisors who have expertise in developing clinical neural language models, implementation of AI-driven tools in health systems, and clinical decision support systems with augmented intelligence. Together, this multidisciplinary team brings nationally renowned expertise in clinical informatics with a track record of successful mentorship. My 4-year proposal with intensive mentorship, clinical research training, formal coursework in health systems engineering and informatics, and computing resources at the University of Wisconsin-Madison will ensure my success as I grow into an independent scientist.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $484,300 )
2025	2025	THE REGENTS OF THE UNIV. OF COLORADO	13001 E 17TH PL STE F428	AURORA	CO	80045	ADAMS	USA	Medical Library Assistance	000	2	3/18/2025	EXTENSION WITH OR WITHOUT FUNDS	$242,128
2025	2025	THE REGENTS OF THE UNIV. OF COLORADO	13001 E 17TH PL STE F428	AURORA	CO	80045	ADAMS	USA	Medical Library Assistance	001	3	8/22/2025	NON-COMPETING CONTINUATION	$242,128
2025	2024	THE REGENTS OF THE UNIV. OF COLORADO	13001 E 17TH PL STE F428	AURORA	CO	80045	ADAMS	USA	Medical Library Assistance	000	2	3/18/2025	EXTENSION WITH OR WITHOUT FUNDS	$44
														Subtotal = $484,300

Issue Date FY: 2024 ( Subtotal = $248,999 )
2024	2024	THE REGENTS OF THE UNIVERSITY OF COLORADO	13001 E 17TH PL STE F428	AURORA	CO	80045	ADAMS	USA	Medical Library Assistance	000	2	8/30/2024	EXTENSION WITH OR WITHOUT FUNDS	$248,999
														Subtotal = $248,999

Grand Total All Awards = $733,299

Top

All Categories

About

Search

Reports

Data Submission

Award Information

Developing and Evaluating Multi-Modal Clinical Diagnostic Reasoning Models for Automated Diagnosis Generation

Award Number: R00LM014308

ORGANIZATION: NATIONAL LIBRARY OF MEDICINE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/01/2024

PERIOD OF PERFORMANCE END DATE: 08/31/2027

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer