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Interpretable Predictive Models in the Healthcare Domain /
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 predicti...
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| Formato: | Video |
| Idioma: | Inglés |
| Publicado: |
[Erscheinungsort nicht ermittelbar] :
Data Science Salon,
2019
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| Edición: | 1st edition. |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | 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. |
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| Notas: | Online resource; Title from title screen (viewed February 21, 2019). |
| Descripción Física: | 1 online resource (1 video file, circa 32 min.) |