Interpretable Predictive Models in the Healthcare Domain

• Verfasst von: Salon, Data [VerfasserIn]
• Titel: Interpretable Predictive Models in the Healthcare Domain
• Institutionen: Safari, an O’Reilly Media Company. [MitwirkendeR]
• Verf.angabe: Salon, Data
• Ausgabe: 1st edition
• Verlagsort: [Erscheinungsort nicht ermittelbar]
• Verlag: Data Science Salon
• Jahr: 2019
• Umfang: 1 online resource (1 video file, approximately 32 min.)
• Fussnoten: Online resource; Title from title screen (viewed February 21, 2019)
• ComputerInfo: Mode of access: World Wide Web.
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Electronic videos ; local
• K10plus-PPN: 1733129979
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m3755947307
• Lokale URL Inst.: Zum Volltext

Abstract

Presented by Sridharan Kamalakannan, Head of Data Science at Humana 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.