all AI news
From algorithms to action: improving patient care requires causality
April 3, 2024, 4:43 a.m. | Wouter A. C. van Amsterdam, Pim A. de Jong, Joost J. C. Verhoeff, Tim Leiner, Rajesh Ranganath
cs.LG updates on arXiv.org arxiv.org
Abstract: In cancer research there is much interest in building and validating outcome predicting outcomes to support treatment decisions. However, because most outcome prediction models are developed and validated without regard to the causal aspects of treatment decision making, many published outcome prediction models may cause harm when used for decision making, despite being found accurate in validation studies. Guidelines on prediction model validation and the checklist for risk model endorsement by the American Joint Committee …
abstract algorithms arxiv building cancer causal causality cs.cy cs.lg decision decision making decisions harm however improving making patient patient care prediction prediction models regard research stat.ml support treatment type
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Lead Data Scientist, Commercial Analytics
@ Checkout.com | London, United Kingdom
Data Engineer I
@ Love's Travel Stops | Oklahoma City, OK, US, 73120