Zobrazeno 1 - 10
of 521
pro vyhledávání: '"Szolovits Peter"'
Autor:
Sergeeva, Elena, Sergeeva, Anastasia, Tang, Huiyun, Bongard-Blanchy, Kerstin, Szolovits, Peter
Previous research on expert advice-taking shows that humans exhibit two contradictory behaviors: on the one hand, people tend to overvalue their own opinions undervaluing the expert opinion, and on the other, people often defer to other people's advi
Externí odkaz:
http://arxiv.org/abs/2310.14358
Autor:
Lehman, Eric, Hernandez, Evan, Mahajan, Diwakar, Wulff, Jonas, Smith, Micah J., Ziegler, Zachary, Nadler, Daniel, Szolovits, Peter, Johnson, Alistair, Alsentzer, Emily
Although recent advances in scaling large language models (LLMs) have resulted in improvements on many NLP tasks, it remains unclear whether these models trained primarily with general web text are the right tool in highly specialized, safety critica
Externí odkaz:
http://arxiv.org/abs/2302.08091
Objective: Reflex testing protocols allow clinical laboratories to perform second line diagnostic tests on existing specimens based on the results of initially ordered tests. Reflex testing can support optimal clinical laboratory test ordering and di
Externí odkaz:
http://arxiv.org/abs/2302.00794
Autor:
Lehman, Eric, Lialin, Vladislav, Legaspi, Katelyn Y., Sy, Anne Janelle R., Pile, Patricia Therese S., Alberto, Nicole Rose I., Ragasa, Richard Raymund R., Puyat, Corinna Victoria M., Alberto, Isabelle Rose I., Alfonso, Pia Gabrielle I., Taliño, Marianne, Moukheiber, Dana, Wallace, Byron C., Rumshisky, Anna, Liang, Jenifer J., Raghavan, Preethi, Celi, Leo Anthony, Szolovits, Peter
Existing question answering (QA) datasets derived from electronic health records (EHR) are artificially generated and consequently fail to capture realistic physician information needs. We present Discharge Summary Clinical Questions (DiSCQ), a newly
Externí odkaz:
http://arxiv.org/abs/2206.02696
The increasing availability of large collections of electronic health record (EHR) data and unprecedented technical advances in deep learning (DL) have sparked a surge of research interest in developing DL based clinical decision support systems for
Externí odkaz:
http://arxiv.org/abs/2112.02625
Autor:
Liu, Sijia, Wen, Andrew, Wang, Liwei, He, Huan, Fu, Sunyang, Miller, Robert, Williams, Andrew, Harris, Daniel, Kavuluru, Ramakanth, Liu, Mei, Abu-el-rub, Noor, Schutte, Dalton, Zhang, Rui, Rouhizadeh, Masoud, Osborne, John D., He, Yongqun, Topaloglu, Umit, Hong, Stephanie S, Saltz, Joel H, Schaffter, Thomas, Pfaff, Emily, Chute, Christopher G., Duong, Tim, Haendel, Melissa A., Fuentes, Rafael, Szolovits, Peter, Xu, Hua, Liu, Hongfang, Collaborative, National COVID Cohort, Processing, Natural Language, Subgroup
While we pay attention to the latest advances in clinical natural language processing (NLP), we can notice some resistance in the clinical and translational research community to adopt NLP models due to limited transparency, interpretability, and usa
Externí odkaz:
http://arxiv.org/abs/2110.10780
Language model pre-training and derived methods are incredibly impactful in machine learning. However, there remains considerable uncertainty on exactly why pre-training helps improve performance for fine-tuning tasks. This is especially true when at
Externí odkaz:
http://arxiv.org/abs/2103.10334
Recent developments in Natural Language Processing (NLP) demonstrate that large-scale, self-supervised pre-training can be extremely beneficial for downstream tasks. These ideas have been adapted to other domains, including the analysis of the amino
Externí odkaz:
http://arxiv.org/abs/2102.00466
Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical
Externí odkaz:
http://arxiv.org/abs/2009.13081