A Modeling and Control Framework for Early Detection of Adverse Clinical States

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Critical care units are the highest mortality units in any hospital. These severely ill patients undergo multiple complex interventions at the same time, and care is so complex that they are extremely vulnerable to medical errors and adverse outcomes. Critical care patients are the most heavily instrumented patients in the hospital. Physiological signals are collected using many different types of sensors. These sensor signals reflect the underlying dynamic, integrated physiological state of the patient and are thus highly complex and inter-related. The biggest challenge faced by critical care physicians is that the amount and complexity of these data push the limits of what they can cognitively assimilate and relate to the overall physiological status of their patients. They are confronted by Big Data at every moment, yet they must interpret and act on them quickly. They lack the tools to do this. In this project, a goal is to develop and apply a novel class of algorithms, known as optimal change-point detection algorithms, to the problem of detecting when a patients state changes from one clinical condition to another. The application of sophisticated algorithms to healthcare will not only transform treatment in critical care units, but will be brought to the classroom to undergraduate students minoring in Computational Medicine.The research team plans to develop automated computational methods for processing physiological time series data from critical care patient sensors to quickly detect changes in clinical state. These methods will include; (i) improved selection of features that characterize patient state; (ii) optimal control algorithms to detect transitions of patient state based on these features. One innovation in the proposed approach is a re-formulated the state transition detection problem as an optimal change-point problem from the fields of controls-theory and Bayesian optimal sequential decision making. This in turn has enabled us to derive an optimal detector by first defining a cost function that reflects performance goals (e.g. maximize sensitivity, minimize false positives), and then developing the detection rule that minimizes this cost. The researchers have demonstrated that this approach decreases time to detection of epileptic seizure onset by 50% relative to other state of the art methods. Another innovation lies in how to compute features from physiological waveforms. The goal of proposed research is to determine if optimal-change point algorithms in conjunction with these new features applied to the analysis of multiple types of physiological time series data can support earlier detection of changes in the clinical conditions of patients in the critical care unit. A new course or course module that introduces statistical and mechanistic model estimation and simple early detection algorithms will be developed. This course will expose biomedical engineering students to experimental data, biophysical-based models, and statistical models of biological systems that have clinical relevance.