Zobrazeno 1 - 10
of 686
pro vyhledávání: '"Afshar, Majid"'
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:
Chen, Shan, Guevara, Marco, Moningi, Shalini, Hoebers, Frank, Elhalawani, Hesham, Kann, Benjamin H., Chipidza, Fallon E., Leeman, Jonathan, Aerts, Hugo J. W. L., Miller, Timothy, Savova, Guergana K., Mak, Raymond H., Lustberg, Maryam, Afshar, Majid, Bitterman, Danielle S.
Documentation burden is a major contributor to clinician burnout, which is rising nationally and is an urgent threat to our ability to care for patients. Artificial intelligence (AI) chatbots, such as ChatGPT, could reduce clinician burden by assisti
Externí odkaz:
http://arxiv.org/abs/2310.17703
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
Large language models (LLMs) like ChatGPT have excited scientists across fields; in medicine, one source of excitement is the potential applications of LLMs trained on electronic health record (EHR) data. But there are tough questions we must first a
Externí odkaz:
http://arxiv.org/abs/2309.12339
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
Autor:
Gao, Yanjun, Dligach, Dmitriy, Miller, Timothy, Xu, Dongfang, Churpek, Matthew M., Afshar, Majid
Automatically summarizing patients' main problems from daily progress notes using natural language processing methods helps to battle against information and cognitive overload in hospital settings and potentially assists providers with computerized
Externí odkaz:
http://arxiv.org/abs/2208.08408
Autor:
Gao, Yanjun, Dligach, Dmitriy, Miller, Timothy, Tesch, Samuel, Laffin, Ryan, Churpek, Matthew M., Afshar, Majid
Applying methods in natural language processing on electronic health records (EHR) data is a growing field. Existing corpus and annotation focus on modeling textual features and relation prediction. However, there is a paucity of annotated corpus bui
Externí odkaz:
http://arxiv.org/abs/2204.03035