arXiv subjects

George Hripcsak

Publications and source records attributed to George Hripcsak.

23 records · Page 2Linked to original sources

arXiv2018

Methodological variations in lagged regression for detecting physiologic drug effects in EHR data

We studied how lagged linear regression can be used to detect the physiologic effects of drugs from data in the electronic health record (EHR). We systematically examined the effect of methodological variations ((i) time series construction, (ii) temporal parameterization, (iii) intra-subject normalization, (iv) differencing (lagged rates of change achieved by taking differences between consecutive measurements), (v) explanatory variables, and (vi) regression models) on performance of lagged linear methods in this context. We generated two gold standards (one knowledge-base derived, one expert-curated) for expected pairwise relationships between 7 drugs and 4 labs, and evaluated how the 64 unique combinations of methodological perturbations reproduce gold standards. Our 28 cohorts included patients in Columbia University Medical Center/NewYork-Presbyterian Hospital clinical database. The most accurate methods achieved AUROC of 0.794 for knowledge-base derived gold standard (95%CI [0.741, 0.847]) and 0.705 for expert-curated gold standard (95% CI [0.629, 0.781]). We observed a 0.633 mean AUROC (95%CI [0.610, 0.657], expert-curated gold standard) across all methods that re-parameterize time according to sequence and use either a joint autoregressive model with differencing or an independent lag model without differencing. The complement of this set of methods achieved a mean AUROC close to 0.5, indicating the importance of these choices. We conclude that time- series analysis of EHR data will likely rely on some of the beneficial pre-processing and modeling methodologies identified, and will certainly benefit from continued careful analysis of methodological perturbations. This study found that methodological variations, such as pre-processing and representations, significantly affect results, exposing the importance of evaluating these components when comparing machine-learning methods.

stat.ME↗

arXiv2017

Offline and online data assimilation for real-time blood glucose forecasting in type 2 diabetes

We evaluate the benefits of combining different offline and online data assimilation methodologies to improve personalized blood glucose prediction with type 2 diabetes self-monitoring data. We collect self-monitoring data (nutritional reports and pre- and post-prandial glucose measurements) from 4 individuals with diabetes and 2 individuals without diabetes. We write online to refer to methods that update state and parameters sequentially as nutrition and glucose data are received, and offline to refer to methods that estimate parameters over a fixed data set, distributed over a time window containing multiple nutrition and glucose measurements. We fit a model of ultradian glucose dynamics to the first half of each data set using offline (MCMC and nonlinear optimization) and online (unscented Kalman filter and an unfiltered model---a dynamical model driven by nutrition data that does not update states) data assimilation methods. Model parameters estimated over the first half of the data are used within online forecasting methods to issue forecasts over the second half of each data set. Offline data assimilation methods provided consistent advantages in predictive performance and practical usability in 4 of 6 patient data sets compared to online data assimilation methods alone; yet 2 of 6 patients were best predicted with a strictly online approach. Interestingly, parameter estimates generated offline led to worse predictions when fed to a stochastic filter than when used in a simple, unfiltered model that incorporates new nutritional information, but does not update model states based on glucose measurements. The relative improvements seen from the unfiltered model, when carefully trained offline, exposes challenges in model sensitivity and filtering applications, but also opens possibilities for improved glucose forecasting and relaxed patient self-monitoring requirements.

q-bio.QM↗

arXiv2011

Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populations

This paper addresses how to calculate and interpret the time-delayed mutual information for a complex, diversely and sparsely measured, possibly non-stationary population of time-series of unknown composition and origin. The primary vehicle used for this analysis is a comparison between the time-delayed mutual information averaged over the population and the time-delayed mutual information of an aggregated population (here aggregation implies the population is conjoined before any statistical estimates are implemented). Through the use of information theoretic tools, a sequence of practically implementable calculations are detailed that allow for the average and aggregate time-delayed mutual information to be interpreted. Moreover, these calculations can be also be used to understand the degree of homo- or heterogeneity present in the population. To demonstrate that the proposed methods can be used in nearly any situation, the methods are applied and demonstrated on the time series of glucose measurements from two different subpopulations of individuals from the Columbia University Medical Center electronic health record repository, revealing a picture of the composition of the population as well as physiological features.

nlin.CD↗

arXiv2011

Population physiology: leveraging population scale (EHR) data to understand human endocrine dynamics

Studying physiology over a broad population for long periods of time is difficult primarily because collecting human physiologic data is intrusive, dangerous, and expensive. Electronic health record (EHR) data promise to support the development and testing of mechanistic physiologic models on diverse population, but limitations in the data have thus far thwarted such use. For instance, using uncontrolled population-scale EHR data to verify the outcome of time dependent behavior of mechanistic, constructive models can be difficult because: (i) aggregation of the population can obscure or generate a signal, (ii) there is often no control population, and (iii) diversity in how the population is measured can make the data difficult to fit into conventional analysis techniques. This paper shows that it is possible to use EHR data to test a physiological model for a population and over long time scales. Specifically, a methodology is developed and demonstrated for testing a mechanistic, time-dependent, physiological model of serum glucose dynamics with uncontrolled, population-scale, physiological patient data extracted from an EHR repository. It is shown that there is no observable daily variation the normalized mean glucose for any EHR subpopulations. In contrast, a derived value, daily variation in nonlinear correlation quantified by the time-delayed mutual information (TDMI), did reveal the intuitively expected diurnal variation in glucose levels amongst a wild population of humans. Moreover, in a population of intravenously fed patients, there was no observable TDMI-based diurnal signal. These TDMI-based signals, via a glucose insulin model, were then connected with human feeding patterns. In particular, a constructive physiological model was shown to correctly predict the difference between the general uncontrolled population and a subpopulation whose feeding was controlled.

q-bio.QM↗

arXiv2011

Estimation of time-delayed mutual information and bias for irregularly and sparsely sampled time-series

A method to estimate the time-dependent correlation via an empirical bias estimate of the time-delayed mutual information for a time-series is proposed. In particular, the bias of the time-delayed mutual information is shown to often be equivalent to the mutual information between two distributions of points from the same system separated by infinite time. Thus intuitively, estimation of the bias is reduced to estimation of the mutual information between distributions of data points separated by large time intervals. The proposed bias estimation techniques are shown to work for Lorenz equations data and glucose time series data of three patients from the Columbia University Medical Center database.

nlin.CD↗