Towards Hypothesis Generation for mHealth Applications: A Data Driven Approach

The rapid expansion of digital health research has led to the integration of Natural Language Processing (NLP) with traditional qualitative methodologies. While this convergence, remains promising in an exploratory phase, often demanding substantial resources due to a lack of standardized techniques. The mHealth domain, which encompasses a broad spectrum of healthcare applications, has undergone a transformation that now includes virtual consultations, remote monitoring, medication management, and health education. These advancements not only empower healthcare consumers to proactively improve their health outcomes but also facilitate healthcare providers in delivering immediate and responsive services. Overcoming challenges in this realm and harnessing these opportunities can potentially accelerate the integration of NLP methods into mHealth applications. The proposed data driven hypothesis generation analyze the existing literature of mHealth applications based in India and aims to identify patterns and gaps in knowledge that can form the basis for generating a novel hypotheses using computational techniques. The work mainly makes use of scientific literature corpus and embedded topic model to capture the importance of words in the document and find their semantic relations. Thus the generated hypothesis can guide further research, experimental design, or empirical investigations, driving data-driven discoveries in the field.