AI reads doctors’ notes to find hidden links in cancer cases
Originally shared by Ward Plunet
AI reads doctors’ notes to find hidden links in cancer cases
Rätsch’s team built a machine learning algorithm to crunch through anonymised clinical notes from 200,000 people with cancer. Their program sorted millions of sentences – including patients’ symptoms, medical histories and doctors’ observations – into 10,000 related clusters.
Each cluster represented a common observation found across several medical records. For example, a doctor’s note recommending a particular course of treatment, or picking up on a noteworthy symptom. Connections between clusters were then mapped, showing the relationships between different comments or courses of treatment.
In a second study building on Rätsch’s work, the clusters are now being compared against the records of about 2000 people with different types of cancer. The researchers are looking for hidden associations between written notes and patients’ gene and blood sequencing. For example, patients with similar genetic results might have the same kind of note in their files. These connections can reveal similarities doctors might not have noticed before.
https://www.newscientist.com/article/2078159-ai-reads-doctors-notes-to-find-hidden-links-in-cancer-cases/
AI reads doctors’ notes to find hidden links in cancer cases
Rätsch’s team built a machine learning algorithm to crunch through anonymised clinical notes from 200,000 people with cancer. Their program sorted millions of sentences – including patients’ symptoms, medical histories and doctors’ observations – into 10,000 related clusters.
Each cluster represented a common observation found across several medical records. For example, a doctor’s note recommending a particular course of treatment, or picking up on a noteworthy symptom. Connections between clusters were then mapped, showing the relationships between different comments or courses of treatment.
In a second study building on Rätsch’s work, the clusters are now being compared against the records of about 2000 people with different types of cancer. The researchers are looking for hidden associations between written notes and patients’ gene and blood sequencing. For example, patients with similar genetic results might have the same kind of note in their files. These connections can reveal similarities doctors might not have noticed before.
https://www.newscientist.com/article/2078159-ai-reads-doctors-notes-to-find-hidden-links-in-cancer-cases/
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