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Rediscovery of CNN's Versatility for Text-based Encoding of Raw Electronic Health Records

Autor: Cho, Eunbyeol, Lee, Min Jae, Hur, Kyunghoon, Kim, Jiyoun, Yoon, Jinsung, Choi, Edward

Making the most use of abundant information in electronic health records (EHR) is rapidly becoming an important topic in the medical domain. Recent work presented a promising framework that embeds entire features in raw EHR data regardless of its for

Externí odkaz: http://arxiv.org/abs/2303.08290

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UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledge

Autor: Hur, Kyunghoon, Oh, Jungwoo, Kim, Junu, Kim, Jiyoun, Lee, Min Jae, Cho, Eunbyeol, Moon, Seong-Eun, Kim, Young-Hak, Choi, Edward

Despite the abundance of Electronic Healthcare Records (EHR), its heterogeneity restricts the utilization of medical data in building predictive models. To address this challenge, we propose Universal Healthcare Predictive Framework (UniHPF), which r

Externí odkaz: http://arxiv.org/abs/2211.08082

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GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning

Autor: Hur, Kyunghoon, Oh, Jungwoo, Kim, Junu, Kim, Jiyoun, Lee, Min Jae, Cho, Eunbyeol, Moon, Seong-Eun, Kim, Young-Hak, Atallah, Louis, Choi, Edward

Publikováno v: IEEE Journal of Biomedical and Health Informatics 2024

Despite the remarkable progress in the development of predictive models for healthcare, applying these algorithms on a large scale has been challenging. Algorithms trained on a particular task, based on specific data formats available in a set of med

Externí odkaz: http://arxiv.org/abs/2207.09858

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