Big Data Analytics Advances in Health Intelligence, Public Health, and Evidence-Based Precision Medicine

Abstract

Large amount of patient data is available in hospitals. Moreover, a huge body of medical knowledge is available in digital form in the public domain, like NLM (National Library of Medicine), PubMed, NCBI (National Center for Biotechnology Information), MeSH (Medical Subject Heading), OMIM (Online Mendelian Inheritance in Man). There are also public biomedical databases like PDB (Protein Data Bank), GO (Gene Ontology), Chemical Entities of Biological Interest (ChEBI), KEGG (Kyoto Encyclopedia of Genes and Genomes), Drug databases (DrugBank), Recon (Reconstruction of Human Metabolism), dbSNP (DNA Mutation Database), COSMIC (Catalogue of Somatic Mutations in Cancer), etc. The list goes on and on and on. In this paper, we are addressing the challenge – how does our analytic solution combine these data and knowledge bodies through the technology of big-data combined with artificial intelligence, mathematical models, and translational medicine into “Evidence Based Precision Medicine – the perfect decision outcome with perfect knowledge backing.” The benefits are immense for many stakeholders. Payer costs are reduced significantly – be it an insurance company or employer or an uninsured individual. The accuracy of medical decisions including the hospital productivity are increased significantly, with reduced medical errors, reduced disease burden, reduced fraud and wastage. Evidence based precision medicine will benefit patients, patients’ families, doctors, hospitals, insurance companies, payers, Government regulators, healthcare professionals, public exchequers and finally, improve the overall general health of the population as a whole.