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 risk factors enables more informed decisions on interventions to mitigate or ameliorate modifiable factors. For these reasons, risk prediction models must be explainable and grounded on medical knowledge. Current machine learning-based risk prediction models are frequently 'black-box' models whose inner workings cannot be understood easily, making it difficult to define risk drivers. Since machine learning models follow patterns in the data rather than looking for medically relevant relationships, possi

SUBMITTER: Twick I 

PROVIDER: S-EPMC9200841 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature