Zobrazeno 1 - 10
of 314
pro vyhledávání: '"A., Dligach"'
Autor:
Gao, Mingye, Varshney, Aman, Chen, Shan, Goddla, Vikram, Gallifant, Jack, Doyle, Patrick, Novack, Claire, Dillon-Martin, Maeve, Perkins, Teresia, Correia, Xinrong, Duhaime, Erik, Isenstein, Howard, Sharon, Elad, Lehmann, Lisa Soleymani, Kozono, David, Anthony, Brian, Dligach, Dmitriy, Bitterman, Danielle S.
Cancer clinical trials often face challenges in recruitment and engagement due to a lack of participant-facing informational and educational resources. This study investigated the potential of Large Language Models (LLMs), specifically GPT4, in gener
Externí odkaz:
http://arxiv.org/abs/2412.01955
Autor:
Gao, Yanjun, Myers, Skatje, Chen, Shan, Dligach, Dmitriy, Miller, Timothy A, Bitterman, Danielle, Chen, Guanhua, Mayampurath, Anoop, Churpek, Matthew, Afshar, Majid
Large language models (LLMs) are being explored for diagnostic decision support, yet their ability to estimate pre-test probabilities, vital for clinical decision-making, remains limited. This study evaluates two LLMs, Mistral-7B and Llama3-70B, usin
Externí odkaz:
http://arxiv.org/abs/2411.04962
Autor:
Myers, Skatje, Miller, Timothy A., Gao, Yanjun, Churpek, Matthew M., Mayampurath, Anoop, Dligach, Dmitriy, Afshar, Majid
Objective: Applying large language models (LLMs) to the clinical domain is challenging due to the context-heavy nature of processing medical records. Retrieval-augmented generation (RAG) offers a solution by facilitating reasoning over large text sou
Externí odkaz:
http://arxiv.org/abs/2409.15163
Autor:
Gao, Yanjun, Myers, Skatje, Chen, Shan, Dligach, Dmitriy, Miller, Timothy A, Bitterman, Danielle, Churpek, Matthew, Afshar, Majid
The introduction of Large Language Models (LLMs) has advanced data representation and analysis, bringing significant progress in their use for medical questions and answering. Despite these advancements, integrating tabular data, especially numerical
Externí odkaz:
http://arxiv.org/abs/2408.11854
Autor:
Chen, Shan, Gallifant, Jack, Guevara, Marco, Gao, Yanjun, Afshar, Majid, Miller, Timothy, Dligach, Dmitriy, Bitterman, Danielle S.
Generative models have been showing potential for producing data in mass. This study explores the enhancement of clinical natural language processing performance by utilizing synthetic data generated from advanced language models. Promising results s
Externí odkaz:
http://arxiv.org/abs/2403.19511
Autor:
Gao, Yanjun, Li, Ruizhe, Caskey, John, Dligach, Dmitriy, Miller, Timothy, Churpek, Matthew M., Afshar, Majid
Electronic Health Records (EHRs) and routine documentation practices play a vital role in patients' daily care, providing a holistic record of health, diagnoses, and treatment. However, complex and verbose EHR narratives overload healthcare providers
Externí odkaz:
http://arxiv.org/abs/2308.14321
The BioNLP Workshop 2023 initiated the launch of a shared task on Problem List Summarization (ProbSum) in January 2023. The aim of this shared task is to attract future research efforts in building NLP models for real-world diagnostic decision suppor
Externí odkaz:
http://arxiv.org/abs/2306.05270
Autor:
Sharma, Brihat, Gao, Yanjun, Miller, Timothy, Churpek, Matthew M., Afshar, Majid, Dligach, Dmitriy
Generative artificial intelligence (AI) is a promising direction for augmenting clinical diagnostic decision support and reducing diagnostic errors, a leading contributor to medical errors. To further the development of clinical AI systems, the Diagn
Externí odkaz:
http://arxiv.org/abs/2306.04551
Autor:
Gao, Yanjun, Dligach, Dmitriy, Miller, Timothy, Churpek, Matthew M, Uzuner, Ozlem, Afshar, Majid
Daily progress notes are common types in the electronic health record (EHR) where healthcare providers document the patient's daily progress and treatment plans. The EHR is designed to document all the care provided to patients, but it also enables n
Externí odkaz:
http://arxiv.org/abs/2303.08038
Autor:
Gao, Yanjun, Dligach, Dmitriy, Miller, Timothy, Caskey, John, Sharma, Brihat, Churpek, Matthew M, Afshar, Majid
The meaningful use of electronic health records (EHR) continues to progress in the digital era with clinical decision support systems augmented by artificial intelligence. A priority in improving provider experience is to overcome information overloa
Externí odkaz:
http://arxiv.org/abs/2209.14901