Towards interpretable, medically grounded, EMR-based risk prediction models
Abstract Machine-learning based risk prediction models have the potential to improve patient outcomes by assessing risk more accurately than clinicians. Significant additional value lies in these models providing feedback about the factors that amplify an individual patient’s risk. Identification of...
Main Authors: | Isabell Twick, Guy Zahavi, Haggai Benvenisti, Ronya Rubinstein, Michael S. Woods, Haim Berkenstadt, Aviram Nissan, Enes Hosgor, Dan Assaf |
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Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2022-06-01
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Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-022-13504-7 |
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