Improving the accuracy of medical diagnosis with causal machine learning
In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them, while existing diagnostic algorithms are purely associative. Here, the authors reformulate diagnosis as a counterfactual inference task and derive new counterfactual diagnostic algorithms.
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2020-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-17419-7 |
| Summary: | In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them, while existing diagnostic algorithms are purely associative. Here, the authors reformulate diagnosis as a counterfactual inference task and derive new counterfactual diagnostic algorithms. |
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| ISSN: | 2041-1723 |