Identifying evolving multivariate dynamics in individual and cohort time series, with application to physiological control systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 117-125).Physiological control systems involve multiple interacting variables operating in feedback loops that enhance an organism's ability to self-regulate and respond to internal and external disturbances. The resulting multivariate time-series often exhibit rich dynamical patterns, which are altered under pathological conditions. However, model identification for physiological systems is complicated by measurement artifacts and changes between operating regimes. The overall aim of this thesis is to develop and validate computational tools for identification and analysis of structured multivariate models of physiological dynamics in individual and cohort time-series. We first address the identification and stability of the respiratory chemoreflex system, which is key to the pathogenesis of sleep-induced periodic breathing and apnea. Using data from both an animal model of periodic breathing, as well as human recordings from clinical sleep studies, we demonstrate that model-based analysis of the interactions involved in spontaneous breathing can characterize the dynamics of the respiratory control system, and provide a useful tool for quantifying the contribution of various dynamic factors to ventilatory instability. The techniques have suggested novel approaches to titration of combination therapies, and clinical evaluations are now underway. We then study shared multivariate dynamics in physiological cohort time-series, assuming that the time-series are generated by switching among a finite collection of physiologically constrained dynamical models. Patients whose time-series exhibit similar dynamics may be grouped for monitoring and outcome prediction. We develop a novel parallelizable machine-learning algorithm for outcome-discriminative identification of the switching dynamics, using a probabilistic dynamic Bayesian network to initialize a deterministic neural network classifier. In validation studies involving simulated data and human laboratory recordings, the new technique significantly outperforms the standard expectation-maximization approach for identification of switching dynamics. In a clinical application, we show the prognostic value of assessing evolving dynamics in blood pressure time-series to predict mortality in a cohort of intensive care unit patients. A better understanding of the dynamics of physiological systems in both health and disease may enable clinicians to direct therapeutic interventions targeted to specific underlying mechanisms. The techniques developed in this thesis are general, and can be extended to other domains involving multi-dimensional cohort time-series.by Shamim Nemati.Ph.D

A Spatio-Temporal Bayesian Network Classifier for Understanding Visual Field Deterioration

Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma which is a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test data, retinal image data and patient demographic data. However, there has been relatively little work in modelling the spatial and temporal relationships common to such data. In this paper we introduce a novel method for classifying Visual Field (VF) data that explicitly models these spatial and temporal relationships. We carry out an analysis of this method and compare it to a number of classifiers from the machine learning and statistical communities. Results are very encouraging showing that our classifiers are comparable to existing statistical models whilst also facilitating the understanding of underlying spatial and temporal relationships within VF data. The results reveal the potential of using such models for knowledge discovery within ophthalmic databases, such as networks reflecting the ‘nasal step’, an early indicator of the onset of glaucoma. The results outlined in this paper pave the way for a substantial program of study involving many other spatial and temporal datasets, including retinal image and clinical data

Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk

Objective: To compare different deep learning architectures for predicting the risk of readmission within 30 days of discharge from the intensive care unit (ICU). The interpretability of attention-based models is leveraged to describe patients-at-risk. Methods: Several deep learning architectures making use of attention mechanisms, recurrent layers, neural ordinary differential equations (ODEs), and medical concept embeddings with time-aware attention were trained using publicly available electronic medical record data (MIMIC-III) associated with 45,298 ICU stays for 33,150 patients. Bayesian inference was used to compute the posterior over weights of an attention-based model. Odds ratios associated with an increased risk of readmission were computed for static variables. Diagnoses, procedures, medications, and vital signs were ranked according to the associated risk of readmission. Results: A recurrent neural network, with time dynamics of code embeddings computed by neural ODEs, achieved the highest average precision of 0.331 (AUROC: 0.739, F1-Score: 0.372). Predictive accuracy was comparable across neural network architectures. Groups of patients at risk included those suffering from infectious complications, with chronic or progressive conditions, and for whom standard medical care was not suitable. Conclusions: Attention-based networks may be preferable to recurrent networks if an interpretable model is required, at only marginal cost in predictive accuracy

A novel time series analysis approach for prediction of dialysis in critically ill patients using echo-state networks

Background: Echo-state networks (ESN) are part of a group of reservoir computing methods and are basically a form of recurrent artificial neural networks (ANN). These methods can perform classification tasks on time series data. The recurrent ANN of an echo-state network has an 'echo-state' characteristic. This 'echo-state' functions as a fading memory: samples that have been introduced into the network in a further past, are faded away. The echostate approach for the training of recurrent neural networks was first described by Jaeger H. et al. In clinical medicine, until this moment, no original research articles have been published to examine the use of echo-state networks. Methods: This study examines the possibility of using an echo-state network for prediction of dialysis in the ICU. Therefore, diuresis values and creatinine levels of the first three days after ICU admission were collected from 830 patients admitted to the intensive care unit (ICU) between May 31th 2003 and November 17th 2007. The outcome parameter was the performance by the echo-state network in predicting the need for dialysis between day 5 and day 10 of ICU admission. Patients with an ICU length of stay < 10 days or patients that received dialysis in the first five days of ICU admission were excluded. Performance by the echo-state network was then compared by means of the area under the receiver operating characteristic curve (AUC) with results obtained by two other time series analysis methods by means of a support vector machine (SVM) and a naive Bayes algorithm (NB). Results: The AUC's in the three developed echo-state networks were 0.822, 0.818, and 0.817. These results were comparable to the results obtained by the SVM and the NB algorithm. Conclusions: This proof of concept study is the first to evaluate the performance of echo-state networks in an ICU environment. This echo-state network predicted the need for dialysis in ICU patients. The AUC's of the echo-state networks were good and comparable to the performance of other classification algorithms. Moreover, the echostate network was more easily configured than other time series modeling technologies