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Akademický článek

Towards proactive palliative care in oncology: developing an explainable EHR-based machine learning model for mortality risk prediction

Autor: Qingyuan Zhuang, Alwin Yaoxian Zhang, Ryan Shea Tan Ying Cong, Grace Meijuan Yang, Patricia Soek Hui Neo, Daniel SW Tan, Melvin LK Chua, Iain Beehuat Tan, Fuh Yong Wong, Marcus Eng Hock Ong, Sean Shao Wei Lam, Nan Liu

Publikováno v: BMC Palliative Care, Vol 23, Iss 1, Pp 1-10 (2024)

Abstract Background Ex-ante identification of the last year in life facilitates a proactive palliative approach. Machine learning models trained on electronic health records (EHR) demonstrate promising performance in cancer prognostication. However,

Externí odkaz: https://doaj.org/article/5408fd937f194a38b05c0bb53a3cb96b

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Akademický článek

The younger flagellum sets the beat for Chlamydomonas reinhardtii

Autor: Da Wei, Greta Quaranta, Marie-Eve Aubin-Tam, Daniel SW Tam

Publikováno v: eLife, Vol 13 (2024)

Eukaryotes swim with coordinated flagellar (ciliary) beating and steer by fine-tuning the coordination. The model organism for studying flagellate motility, Chlamydomonas reinhardtii, employs synchronous, breaststroke-like flagellar beating to swim,

Externí odkaz: https://doaj.org/article/1a1d7e26a88344989ad00721c41c2bd8

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Akademický článek

Genomic heterogeneity at baseline is associated with T790M resistance mutations in EGFR‐mutated lung cancer treated with the first‐/second‐generation tyrosine kinase inhibitors

Autor: Michael Menzel, Martina Kirchner, Klaus Kluck, Markus Ball, Susanne Beck, Michael Allgäuer, Christin Assmann, Johannes Schnorbach, Anna‐Lena Volckmar, Timothy Kwang Yong Tay, Hannah Goldschmid, Daniel SW Tan, Michael Thomas, Daniel Kazdal, Jan Budczies, Albrecht Stenzinger, Petros Christopoulos

Publikováno v: The Journal of Pathology: Clinical Research, Vol 10, Iss 2, Pp n/a-n/a (2024)

Abstract This study analyzed whether extended molecular profiling can predict the development of epidermal growth factor receptor (EGFR) gene T790M mutation, which is the most frequent resistance alteration in non‐small cell lung cancer (NSCLC) aft

Externí odkaz: https://doaj.org/article/592032d0cf624a62bd5f0cb57fe2198c

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Report

A Proposed S.C.O.R.E. Evaluation Framework for Large Language Models : Safety, Consensus, Objectivity, Reproducibility and Explainability

Autor: Tan, Ting Fang, Elangovan, Kabilan, Ong, Jasmine, Shah, Nigam, Sung, Joseph, Wong, Tien Yin, Xue, Lan, Liu, Nan, Wang, Haibo, Kuo, Chang Fu, Chesterman, Simon, Yeong, Zee Kin, Ting, Daniel SW

A comprehensive qualitative evaluation framework for large language models (LLM) in healthcare that expands beyond traditional accuracy and quantitative metrics needed. We propose 5 key aspects for evaluation of LLMs: Safety, Consensus, Objectivity,

Externí odkaz: http://arxiv.org/abs/2407.07666