Natural Language Processing for Unlocking Insights from Unstructured Big Data in The Healthcare Industry

Abstract

Healthcare's vast data volume is rapidly growing, of which over 80% is unstructured clinical notes, medical images, literature publications and social conversations. This big text data hides invaluable insights to enhance decisions, outcomes and discoveries. Natural language processing (NLP) enables extracting value from narratives using linguistics understanding to automatically convert free-text to structured data. This paper discusses NLP techniques applied in healthcare and practical benefits achieved. For electronic health records, dictionary and machine learning entity extraction accurately identifies clinical concepts like symptoms and treatments in notes for decision support, while relation extraction reveals links between medical problems and medications improving clinical modeling. Summarization of lengthy records also assists physicians. On social platforms, NLP helps accelerating discoveries by uncovering public health insights around outbreak forecasting, adverse drug events monitoring, and mental health conditions absent in curated medical datasets. For biomedicine's unstructured knowledge in publications, machine reading comprehension enables hypothesis generation and validation by answering complex questions with over 90% accuracy. While accuracy, security and interoperability challenges persist, innovations in transfer learning from language models like BERT, graph-based contextual representation, user-centered design, and federated learning are overcoming adoption barriers. As ethical implications are addressed responsibly, NLP adoption is expected to rise steeply. Overall, NLP unlocks healthcare's big unstructured data, unlocking evidence and insights supporting improved clinical and operational outcomes, patient-centric care and transformative medical discoveries using AI techniques that perceive both the content and contexts encoded in natural language.