Machine learning analysis of doctor notes predicts cancer progression


Gunnar Rätsch and Memorial Sloan Kettering colleagues are using AI to find similarities between cancer cases.  Ratsch’s algorithm has analyzed 100 million sentences taken from clinical notes of about 200,000 cancer patients to predict disease progression.

In a recent study, machine learning was used to classify  patient symptoms, medical histories and doctors’ observations into 10,000 clusters. Each cluster represented a common observation in medical records, including recommended treatments and typical symptoms. Connections between clusters were mapped to  show inter-relationships. In another study, algorithms were used to  find hidden associations between written notes and patients’ gene and blood sequencing.

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