Interpretable predictive models in the healthcare domain

"Predictive models are often used to identify individuals that will likely have escalating health severity in the future and accordingly deliver appropriate interventions. However, for the clinicians and care managers, these predictive models often act as a black-box at an individual level. The...

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Bibliographic Details
Other Authors: Kamalakannan, Sridharan, on-screen presenter (onscreen presenter)
Format: Online Video
Language:Inglés
Published: [Place of publication not identified] : Data Science Salon 2019.
Subjects:
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009822834306719
Description
Summary:"Predictive models are often used to identify individuals that will likely have escalating health severity in the future and accordingly deliver appropriate interventions. However, for the clinicians and care managers, these predictive models often act as a black-box at an individual level. The reason for this being, typically predictive models use combinations of complicated algorithms that makes it hard to explain the reason behind a predictive model score at an individual level. This talk will focus on model and feature agnostic methodologies and techniques that help uncover the drivers behind a prediction at a personal level in a healthcare setting."--Resource description page.
Item Description:Title from resource description page (Safari, viewed November 3, 2020).
Physical Description:1 online resource (1 streaming video file (31 min., 35 sec.)) : digital, sound, color