Text Mining for Precision Medicine : Natural Language Processing, Machine Learning and Information Extraction for Knowledge Discovery in the Health Domain

Abstract

In recent years, the Precision Medicine paradigm has emerged to compensate for the shortcomings of the traditional 'one-size-fits-all' approach that has dominated the medical practice for so long. It aims at developing more precise and tailored plans for patients starting from screening and diagnosing to treatment and interventions. Turning the Precision Medicine dream into a clinical reality requires the integration of different sources of data such as scientific literature, electronic health records, and structured databases through the means of Text Mining, Information Extraction, and Machine learning.

This dissertation investigates, in specific, the role of Biomedical Natural Language Processing in the Precision Medicine revolution. The potential benefit of extracting valuable information from existing unstructured data improves the understanding of the field and contributes to better health care. This research focuses on two main aspects: proving the efficacy of Precision Medicine and advancing its integration into practice. To explore both aspects, this dissertation employs and extends methods and techniques such as automatic curation, ontology reuse ,and natural language inference.