Precision medicine offers the promise of individualized care tailored to the specific (usually molecular) characteristics of the patient. Finding hyper-individualized treatments requires collection and analysis of large amounts of data against the backdrop of large amounts of biomedical knowledge. Moreover, adoption of any recommendation by clinicians requires a justification of the recommendation that clearly explains why a given treatment is likely to be effective. Synthesis of physician-understandable proofs turns out to be a task well-suited to relational programming. This talk explores the use of miniKanren for precision medicine as embodied by the drug-repurposing tool mediKanren.
Professor of Internal Medicine and Computer Science, UAB