Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol comprises five main stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty, and generalizability evaluation. We have applied the workflow to four endpoints (acute kidney injury, mortality, length of stay and 30-day hospital readmission). The workflow can enable continuous (e.g., triggered every 6 h) and static (e.g., triggered at 24 h after admission) predictions. We also provide an open-source codebase that illustrates some key principles in EHR modeling. This protocol can be used by interdisciplinary teams with programming and clinical expertise to build deep-learning prediction models with alternate data sources and prediction tasks

Early hospital mortality prediction using vital signals

Early hospital mortality prediction is critical as intensivists strive to make efficient medical decisions about the severely ill patients staying in intensive care units. As a result, various methods have been developed to address this problem based on clinical records. However, some of the laboratory test results are time-consuming and need to be processed. In this paper, we propose a novel method to predict mortality using features extracted from the heart signals of patients within the first hour of ICU admission. In order to predict the risk, quantitative features have been computed based on the heart rate signals of ICU patients. Each signal is described in terms of 12 statistical and signal-based features. The extracted features are fed into eight classifiers: decision tree, linear discriminant, logistic regression, support vector machine (SVM), random forest, boosted trees, Gaussian SVM, and K-nearest neighborhood (K-NN). To derive insight into the performance of the proposed method, several experiments have been conducted using the well-known clinical dataset named Medical Information Mart for Intensive Care III (MIMIC-III). The experimental results demonstrate the capability of the proposed method in terms of precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The decision tree classifier satisfies both accuracy and interpretability better than the other classifiers, producing an F1-score and AUC equal to 0.91 and 0.93, respectively. It indicates that heart rate signals can be used for predicting mortality in patients in the ICU, achieving a comparable performance with existing predictions that rely on high dimensional features from clinical records which need to be processed and may contain missing information.Comment: 11 pages, 5 figures, preprint of accepted paper in IEEE&ACM CHASE 2018 and published in Smart Health journa

Learning deep patient representations for the teleICU

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 89-93).This thesis presents a method of extracting deep robust representations of teleICU clinical data using Transformer networks, inspired by recent machine learning literature in language modeling. The utility of these representations is evaluated in various prediction outcome tasks, in which they were able to outperform linear and neural baselines. Also examined are the probability distributions of various patient characteristics across the learned patient representation space; where corresponding high-level spatial structure suggests potential for use as a similarity metric or in combination with other patient similarity metrics. Finally, the code for the models developed is publicly provided as a starting point for further research.by Ini Oguntola.M. Eng.M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc

Development, Implementation and Evaluation of Medical Decision Support Systems Based on Mortality Prediction Algorithms from an Operations Research Perspective.

Wide implementation of electronic health record systems provides rich data for personalized medicine. One topic of great interest is to develop methods to assist physicians in prognosis for example mortality. While many studies have reported on various new prediction models and algorithms there is relatively little literature on if and how these new prediction methods translate into actual benefits. My dissertation consists of three theses that aims at filling this gap between prognostic predictions and clinical decisions in end-of-life care and intensive care settings. In the first thesis, we develop an approach to using temporal trends in physiologic data as an input into mortality prediction models. The approach uses penalized b-spline smoothing and functional PCA to summarize time series of patient data. we apply the methodology in two settings to demonstrate the value of using the shapes of health data time series as a predictor of patient prognosis. The first application a mortality predictor for advanced cancer patients that can help oncologists decide which patients should stop aggressive treatments and switch to palliative care such as that provided in hospice. The second one is a real-time near term mortality predictor for MICU patients that can work as an early alarm system to guide timely interventions. In the second thesis, we investigate the integration of a prediction algorithm with physician decision making, focusing on the advanced cancer patient setting. We design a retrospective study to compare prognoses made by doctors and those that would be recommended by the IMPAC algorithm developed in Chapter 1. We used the doctor\u27s discharge decision as a proxy of what they predict the patient as dying in 90 days and show that doctor\u27s predictions tend to very conservative. Although IMPAC on its own does not perform better than doctors in terms of precision and recall, we find that IMPAC and doctors identify significantly different group of positive cases. IMPAC and doctors are also good at identifying very different groups of patients in terms of survival time. We propose a new way to augment decisions of doctors with IMPAC. At the same recall, the augment method identifies 43\% more patients close to death than the doctors do. We also estimate potential hospitalizations and hospital length of stays avoided if the doctors use augmented procedure instead of acting on their own beliefs. In the third thesis, we look at the integration of a prediction algorithm with physician decision making, focusing on the ICU setting. We use a POMDP framework to evaluate how decision support systems based on ICU mortality predictions can help physicians allocate time to inspect the patients at highest risk of death. We assume physicians have limited time and seek to optimally allocate it to patients in order to minimize their mortality rate. Physicians can do Bayesian updates on observations of patient health state. A prediction algorithm can augment this process by sending alerts to physicians. We represent the algorithm by an arbitrary point on an ROC curve representing a particular alert threshold. We study two approaches to using the algorithm input: (1) Belief based policy (BBP) that integrates algorithm outputs using Bayesian updating; (2) Alarm triggered policy (ATP) where the physician responds only to the algorithm without updating, and compare them to benchmarks that do not rely on the algorithm at all. By running simulations, we explore how the accuracy of predictions can translate into lower mortality rates

Analyzing Patient Trajectories With Artificial Intelligence

In digital medicine, patient data typically record health events over time (eg, through electronic health records, wearables, or other sensing technologies) and thus form unique patient trajectories. Patient trajectories are highly predictive of the future course of diseases and therefore facilitate effective care. However, digital medicine often uses only limited patient data, consisting of health events from only a single or small number of time points while ignoring additional information encoded in patient trajectories. To analyze such rich longitudinal data, new artificial intelligence (AI) solutions are needed. In this paper, we provide an overview of the recent efforts to develop trajectory-aware AI solutions and provide suggestions for future directions. Specifically, we examine the implications for developing disease models from patient trajectories along the typical workflow in AI: problem definition, data processing, modeling, evaluation, and interpretation. We conclude with a discussion of how such AI solutions will allow the field to build robust models for personalized risk scoring, subtyping, and disease pathway discovery