Indium: Using Novel Machine Learning Algorithms to Develop a Nondisease-specific Personalized Medicine Engine

Overview

In medicine today, treatments for patients are based on the diagnosis, not the patient individually. It has been shown that this is only effective for about 60% of people. To help treat more patients I developed Indium, a nondisease-specific personalized medicine using novel machine learning algorithms. I divide this problem into 3 distinct steps: diagnosis, prognosis and individualized treatment creation. To truly create a completely generalized diagnostic system I developed a powerful natural language processing (NLP) engine that analyzes how language acts on itself. I then connect this NLP to PubMed, a database of medical research papers, to extract features and biomarkers that are indicative of certain diseases. Analyzing the effectiveness of my system, I have found that my generalized algorithm is more effective, by about 3 standard deviations, than the state of the art techniques and physicians. My prognostic software works by utilizing fuzzy lagged data co-clustering, an NP-complete problem. To circumvent this problem I developed a Monte-Carlo approximation that runs in polynomial time. Lastly, dealing with treatment creation I developed a Q-learning algorithm that dynamically adjusts for the specific patient parameters. To deal with the problem of censored data, I created an SVM system to maintain a constant belief state of the subject. I demonstrated the performance of the proposed algorithmic framework through the analysis of real clinical trials. The personalized medicine system I developed not only operates in non-optimal environments, but it is more effective than the state of the art techniques. Since my algorithm is offloaded into the cloud I am able to help patients around the world regardless of their socioeconomic status.