Explainable statistical learning in public health for policy development: the case of real-world suicide data

Explainable statistical learning in public health for policy development: the case of real-world suicide data

Abstract Background In recent years, the availability of publicly available data related to public health has significantly increased. These data have substantial potential to develop public health policy; however, this requires meaningful and insightful analysis. Our aim is to demonstrate how data...

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Bibliographic Details
Main Authors: Paul van Schaik, Yonghong Peng, Adedokun Ojelabi, Jonathan Ling
Format: Article
Language:English
Published: BMC 2019-07-01
Series:BMC Medical Research Methodology
Subjects:
Public health data
Mental health
Suicide
Statistical learning
Machine learning
Explainability
Online Access:http://link.springer.com/article/10.1186/s12874-019-0796-7

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http://link.springer.com/article/10.1186/s12874-019-0796-7

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