Organized by Dr. Aviv Gibali & Dr. Hussein Naseraldin
Modeling a Chronic Disease – a Multi-Disciplinary Challenge
Dr. MD. Barkan and Dr. Harel are two medical data researchers in the Israeli academia, who cooperate with the health industry (HMOs, hospitals and medical research institutions) to help solve complex problems in face of Big Data.
Background
Big data in medicine is not only an expression representing the medical information collected in relation to the population, but it is also pertaining to for a single patient, especially a chronically/multi-morbid patient (who suffers from several chronic diseases simultaneously).
Chronic disease entails a medical information system to collect a lot of “raw” information, including the patient's history in the context of the disease, from numerous interfaces and in many dimensions: visits by family physicians, physical measures (e.g., blood pressure, heart rate, BMI), laboratory tests, diagnostic tests, and more. Moreover, measuring proximity amid individuals is even more challenging as the dynamics of one chronic disease may be related to another.
Objective
As a first step, the goal is to mathematically model a chronic disease of a single patient. Once completed, the next challenge is to model a patient with several chronic diseases at the same time.
With the resulting model for an individual, the ultimate challenge is to define a metric distance function to measure how one patient is “close” to another, with the same disease or group of diseases. This should also enable to cluster chronic patients.
