Predicting future state for adaptive clinical pathway management

TL;DR

This paper presents weighted state transition logic, a novel approach that predicts future patient states and enables adaptive clinical pathway management by integrating semantic web technologies and reasoning.

Contribution

It introduces weighted state transition logic, extending linear logic with weights, for modeling and predicting patient state changes to support adaptive clinical pathways.

Findings

Enables prediction of future patient states.

Supports generation of adaptive clinical pathways.

Detects conflicts among coexisting pathways.

Abstract

Clinical decision support systems are assisting physicians in providing care to patients. However, in the context of clinical pathway management such systems are rather limited as they only take the current state of the patient into account and ignore the possible evolvement of that state in the future. In the past decade, the availability of big data in the healthcare domain did open a new era for clinical decision support. Machine learning technologies are now widely used in the clinical domain, nevertheless, mostly as a tool for disease prediction. A tool that not only predicts future states, but also enables adaptive clinical pathway management based on these predictions is still in need. This paper introduces weighted state transition logic, a logic to model state changes based on actions planned in clinical pathways. Weighted state transition logic extends linear logic by taking weights -- numerical values indicating the quality of an action or an entire clinical pathway -- into account. It allows us to predict the future states of a patient and it enables adaptive clinical pathway management based on these predictions. We provide an implementation of weighted state transition logic using semantic web technologies, which makes it easy to integrate semantic data and rules as background knowledge. Executed by a semantic reasoner, it is possible to generate a clinical pathway towards a target state, as well as to detect potential conflicts in the future when multiple pathways are coexisting. The transitions from the current state to the predicted future state are traceable, which builds trust from human users on the generated pathway.