Machine Learning, NLP and Network Analysis-Guided Medical Research : A Case Study

Can Machine Learning help us in identifying the origin of several Medical Syndromes? In previous posts we have seen how approximately 8 Million PubMed abstracts were collected and analyzed using Natural Language Processing (NLP) techniques. This NLP Processing is the basis for generating Data that may then be used as Input to several Machine Learning algorithms. In this Case Study our Goal is to identify relevant Medical Topics (Topics include Genes, Biological Pathways, etc) that are most likely to direct Medical Researchers towards the origin(s) of the following Syndromes: -Post-Finasteride Syndrome -Post-Accutane Syndrome -Chronic Fatigue Syndrome -Fibromyalgia -Gulf-War Syndrome -Post-Treatment Lyme disease Syndrome Before continuing, please read the following post for important disclaimers here Note that the results shown below originate strictly from output of Machine Learning Algorithms / Network Analysis. No Human intervention has been made apart from the fact that Candidate Topics were constantly being added for evaluation by a software system that combines ML Algorithms, NLP and Network Analysis to identify most promising Medical Topics.