11-06-2014, 12:56 PM
Artificial Intelligence Framework for Simulating Clinical Decision-Making: A Markov Decision Process Approach
Artificial Intelligence Framework.pdf (Size: 815.75 KB / Downloads: 37)
Abstract
Objective: In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This framework serves two potential functions: 1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and 2) the basis for clinical artificial intelligence – an AI that can “think like a doctor.”
Centerstone Research Institute
365 South Park Ridge Road
Bloomington, IN 47401
1.812.336.1156
Casey.Bennett[at]CenterstoneResearch.org
cabennet[at]indiana.edu1.
Introduction
1.1 Problem
There are multiple major problems in the functioning and delivery of the modern healthcare system – rapidly expanding costs and complexity, the growing myriad of treatment options, and exploding information streams that often do not, or at most ineffectively, reach the front lines. Even the answer to the basic healthcare question of “What's wrong with this person” often remains elusive in the modern era – let alone clear answers on the most effective treatment for an individual or how we achieve lower costs and greater efficiency. With the expanding use of electronic health records (EHRs) and growth of large public biomedical datasets (e.g. GenBank, caBig), the area is ripe for applications of computational and artificial intelligence (AI) techniques in order to uncover fundamental patterns that can be used to predict optimal treatments, minimize side effects, reduce medical errors/costs, and better integrate research and practice [1].1.3 Previous work
In previous work, we have detailed computational approaches for determining optimal treatment decisions at single timepoints via the use of data mining/machine learning techniques. Initial results of such approaches have achieved success rates of near 80% in predicting optimal treatment for individual patients with complex, chronic illness, and hold promise for further improvement [7,9]. Predictive algorithms based on such data-driven models are essentially an individualized form of practice-based evidence drawn from the live population. Another term for this is “personalized medicine.”
The ability to adapt specific treatments to fit the characteristics of an individual’s disorder transcends the traditional disease model. Prior work in this area has primarily addressed the utility of genetic data to inform individualized care. However, it is likely that the next decade will see the integration of multiple sources of data - genetic, clinical, socio-demographic – to build a more complete profile of the individual, their inherited risks, and the environmental/behavioral factors associated with disorder and the effective treatment thereof [10]. Indeed, we already see the trend of combining clinical and genetic indicators in prediction of cancer prognosis as a way of developing cheaper, more effective prognostic tools [11-13].1.4 Computational approaches to sequential decision making
The focus of the current study is to extend the prior work beyond optimizing treatments at single decision points in clinical settings. This paper considers sequential decision processes,
in which a sequence of interrelated decisions must be made over time, such as those encountered in the treatment of chronic disorders.
Conclusion
At the end of the day, if we can predict the likely result of a sequence of actions/treatment
for some time out into the future, we can use that to determine the optimal action right now. As
recently pointed out by the Institute of Medicine, does it make sense to continue to have human
clinicians attempt to estimate the probabilistic effects of multiple actions over time, across
multitudes of treatment options and variable patient characteristics, in order to derive some
intuition of the optimal course of action? Or would we be better served to free them to focus on
delivery of actual patient care [39]? The work presented here adds to a growing body of
evidence that such complex treatment decisions may be better handled through modeling than
intuition alone [18,21]. Furthermore, the potential exists to extend this framework as a technical
infrastructure for delivering personalized medicine. Such an approach presents real opportunities
to address the fundamental healthcare challenges of our time, and may serve a critical role in
advancing human performance as well.