no code implementations • 28 Aug 2023 • Yanjun Gao, Ruizhe Li, John Caskey, Dmitriy Dligach, Timothy Miller, Matthew M. Churpek, Majid Afshar
In this paper, we outline an innovative approach for augmenting the proficiency of LLMs in the realm of automated diagnosis generation, achieved through the incorporation of a medical knowledge graph (KG) and a novel graph model: Dr. Knows, inspired by the clinical diagnostic reasoning process.
no code implementations • 8 Jun 2023 • Yanjun Gao, Dmitriy Dligach, Timothy Miller, Matthew M. Churpek, Majid Afshar
The BioNLP Workshop 2023 initiated the launch of a shared task on Problem List Summarization (ProbSum) in January 2023.
no code implementations • 7 Jun 2023 • Brihat Sharma, Yanjun Gao, Timothy Miller, Matthew M. Churpek, Majid Afshar, Dmitriy Dligach
Generative artificial intelligence (AI) is a promising direction for augmenting clinical diagnostic decision support and reducing diagnostic errors, a leading contributor to medical errors.
no code implementations • 17 Aug 2022 • Yanjun Gao, Dmitriy Dligach, Timothy Miller, Dongfang Xu, Matthew M. Churpek, Majid Afshar
In this work, we propose a new NLP task that aims to generate a list of problems in a patient's daily care plan using input from the provider's progress notes during hospitalization.
no code implementations • LREC 2022 • Yanjun Gao, Dmitriy Dligach, Timothy Miller, Samuel Tesch, Ryan Laffin, Matthew M. Churpek, Majid Afshar
This work introduces a hierarchical annotation schema with three stages to address clinical text understanding, clinical reasoning, and summarization.