A decision support system based on artificial intelligence and systems biology for the simulation of pancreatic cancer patient status

Abstract

Oncology treatments require continuous individual adjustment based on the measurement of multiple clinical parameters. Prediction tools exploiting the patterns present in the clinical data could be used to assist decision making and ease the burden associated to the interpretation of all these parameters. The goal of this study was to predict the evolution of patients with pancreatic cancer at their next visit using information routinely recorded in health records, providing a decision-support system for clinicians. We selected hematological variables as the visit's clinical outcomes, under the assumption that they can be predictive of the evolution of the patient.Multivariate models based on regression trees were generated to predict next-visit values for each of the clinical outcomes selected, based on the longitudinal clinical data as well as on molecular data sets streaming from in silico simulations of individual patient status at each visit. The models predict, with a mean prediction score (balanced accuracy) of 0.79, the evolution trends of eosinophils, leukocytes, monocytes, and platelets. Time span between visits and neutropenia were among the most common factors contributing to the predicted evolution. The inclusion of molecular variables from the systems-biology in silico simulations provided a molecular background for the observed variations in the selected outcome variables, mostly in relation to the regulation of hematopoiesis. In spite of its limitations, this study serves as a proof of concept for the application of next-visit prediction tools in real-world settings, even when available data sets are small. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?Artificial intelligence (AI) and related technologies are increasingly prevalent in health care, as they can manage heterogeneous sources of data, identifying underlaying patterns, and predicting complex outcomes.

Multiorgan locked-state model of chronic diseases and systems pharmacology opportunities

“…In past decades, the development of systems biology algorithms that seek to uncover how a complex living system, such as a cell, works as an integrated whole, 36 , 37 , 38 , 39 including hybrid network biology algorithms powered by AI, 40 , 41 have advanced our understanding of how gene–gene interplay sustains the development of chronic diseases. However, endeavors to develop systems biology and AI tools, and simultaneously generate omics data, to decipher multiorgan communication are lagging behind.…”