“We are made to persist. That’s how we find out who we are.”
― Tobias Wolff
Two years in the making, with a database of 6000+ individual lab markers and their values in specific pathologies, I’ve finally cracked an algorithm that has eluded me for over a year and a half: How to use machine learning AI to predict the probability of a certain diagnosis based on an individual’s collection of laboratory results. Because the pathologies link to the NCBI MeSH (medical subject headings) they can also be used to weigh specific clinical signs and symptoms to further establish an accurate diagnosis.
More work on my machine learning diagnosis engine. Underneath the likely diagnoses (from the user-supplied lab results) I’ve added a heat map that correlates the known symptoms associated with each pathology resulting from the reported lab data. The meaner the red color the more the symptom is associated with that disorder. The practitioner can also tick off the ones that apply to the client, press a button, and these probabilities will be added to the results from the laboratory data to further refine the guesswork.