Understanding Heterogeneous Health Outcomes with SciML and Pumas | Niklas Korsbo | SciMLCon 2022 скачать в хорошем качестве

Understanding Heterogeneous Health Outcomes with SciML and Pumas | Niklas Korsbo | SciMLCon 2022

This talk was part of SciMLCon 2022! For more information, check out https://scimlcon.org/2022/. For more information on the SciML Open Source Software Organization for Scientific Machine Learning, check out https://sciml.ai/. Abstract: What is driving disease progression, what effect does a drug have, and why do different patients have different outcomes? At Pumas-AI, we are mixing SciML with statistical and dynamical modelling to predict and optimise patient outcomes. Here, we explore the ideas of SciML within healthcare and demonstrate how it can be used to not only help develop dynamical models of disease progression and drug effect but also to predict variations in outcomes based on patient information. Description: Mathematical modelling is used throughout much of pharmacology - from identifying potential drug targets and evaluating drug efficacy to making individualised treatment and dosing recommendations for patients in hospital. Building these models is, however, not trivial. The biology that drives disease progression is often both complicated and at least partially unknown. Furthermore, despite having the same disease and receiving the same treatment there is still large heterogeneity in patient outcomes. For more info on the Julia Programming Language, follow us on Twitter:   / julialanguage   and consider sponsoring us on GitHub: https://github.com/sponsors/JuliaLang 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/JuliaCommunity/You... Interested in improving the auto generated captions? Get involved here: https://github.com/JuliaCommunity/You...