Artificial Intelligence Pipeline to Bridge the Gap between Bench Researchers and Clinical Researchers in Precision Medicine

Abstract

Precision medicine informatics is a field of research that incorporates learning systems that generate new knowledge to improve individualized treatments using integrated data sets and models. Given the everincreasing volumes of data that are relevant to patient care, artificial intelligence (AI) pipelines need to be a central component of such research to speed discovery. Applying AI methodology to complex multidisciplinary information retrieval can support efforts to discover bridging concepts within collaborating communities. This dovetails with precision medicine research, given the information rich multi-omic data that are used in precision medicine analysis pipelines. In this perspective article we define a prototype AI pipeline to facilitate discovering research connections between bioinformatics and clinical researchers. We propose building knowledge representations that are iteratively improved through AI and human-informed learning feedback loops supported through crowdsourcing. To illustrate this, we will explore the specific use case of nonalcoholic fatty liver disease, a growing health care problem. We will examine AI pipeline construction and utilization in relation to bench-tobedside bridging concepts with interconnecting knowledge representations applicable to bioinformatics researchers and clinicians.

Artificial intelligence methods for a Bayesian epistemology‐powered evidence evaluation

“…49 On the medical side, knowledge extraction may prove fundamental for accelerating the bench to bedside passage in pharmacological research. 52 With respect to E-Synthesis, evidence retrieval may boost its performances, by querying databases for all known names for a drug (alike what is done in databases like VigiBase ‡ ‡ ), for similar drugs (similarity in terms of active ingredient, drug carrier, chemical structure) and similar reactions, as well as disentangling mechanisms of putative causal connections with respect to different drugs causing the same ADR. § §…”