MIT Developing AI To Better Diagnose Cancer
stowie writes: Working with Massachusetts General Hospital, MIT has developed a computational model that aims to automatically suggest cancer diagnoses by learning from thousands of data points from past pathology reports. The core idea is a technique called Subgraph Augmented Non-negative Tensor Factorization (SANTF). In SANTF, data from 800-plus medical cases are organized as a 3D table where the dimensions correspond to the set of patients, the set of frequent subgraphs, and the collection of words appearing in and near each data element mentioned in the reports. This scheme clusters each of these dimensions simultaneously, using the relationships in each dimension to constrain those in the others. Researchers can then link test results to lymphoma subtypes.
I think this may contribute to an already existing problem with the high cancer rate: high detection. How many people who are diagnosed with cancer would have lived just fine and ended up dying from something else? We do find cancer in autopsies where the person didn't know they had cancer. Is the current trend of high cancer rates partly due to better means of detection, and it's just that lots of people have had asymptomatic cancer all this time? Does every form of cancer require massive amounts of chemo? My wife passed away from stage 4 colon cancer last year. It had spread to her lungs, adrenal gland, and liver. She had surgery for the original tumor, and underwent 3 years of aggressive chemo to remove the very tiny filaments elsewhere in her body. I can only wonder if, without the chemo, she would have had the same fate. There are people who forego chemo and survive. And obviously chemo is necessary for many people to beat cancer. But I have to wonder if getting better at detecting cancer will bring more good than harm.
Tic-Tac-Toe, Global Thermonuclear War, and relationships all have the same winning move.