Jinsung Yoon

Adaptive Ensemble Learning With Confidence Bounds

Extracting actionable intelligence from distributed, heterogeneous, correlated, and high-dimensional data sources requires run-time processing and learning both locally and globally. In the last decade, a large number of meta-learning techniques have been proposed in which local learners make online predictions based on their locally collected data instances, and feed these predictions to an ensemble learner, which fuses them and issues a global prediction. However, most of these works do not provide performance guarantees or, when they do, these guarantees are asymptotic. None of these existing works provide confidence estimates about the issued predictions or rate of learning guarantees for the ensemble learner. In this paper, we provide a systematic ensemble learning method called Hedged Bandits, which comes with both long-run (asymptotic) and short-run (rate of learning) performance guarantees. Moreover, our approach yields performance guarantees with respect to the optimal local prediction strategy, and is also able to adapt its predictions in a data-driven manner. We illustrate the performance of Hedged Bandits in the context of medical informatics and show that it outperforms numerous online and offline ensemble learning methods.

Discovery and Clinical Decision Support for Personalized Healthcare

With the advent of electronic health records, more data are continuously collected for individual patients, and more data are available for review from past patients. Despite this, it has not yet been possible to successfully use this data to systematically build clinical decision support systems that can produce personalized clinical recommendations to assist clinicians in providing individualized healthcare. In this paper, we present a novel approach, discovery engine (DE), that discovers which patient characteristics are most relevant for predicting the correct diagnosis and/or recommending the best treatment regimen for each patient. We demonstrate the performance of DE in two clinical settings: diagnosis of breast cancer as well as a personalized recommendation for a specific chemotherapy regimen for breast cancer patients. For each distinct clinical recommendation, different patient features are relevant; DE can discover these different relevant features and use them to recommend personalized clinical decisions. The DE approach achieves a 16.6% improvement over existing state-of-the-art recommendation algorithms regarding kappa coefficients for recommending the personalized chemotherapy regimens. For diagnostic predictions, the DE approach achieves a 2.18% and 4.20% improvement over existing state-of-the-art prediction algorithms regarding prediction error rate and false positive rate, respectively. We also demonstrate that the performance of our approach is robust against missing information and that the relevant features discovered by DE are confirmed by clinical references.

Personalized Risk Scoring for Critical Care Prognosis Using Mixtures of Gaussian Processes

Objective: In this paper, we develop a personalized real-time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs; the proposed risk scoring system ensures timely intensive care unit admissions for clinically deteriorating patients. Methods: The risk scoring system is based on the idea of sequential hypothesis testing under an uncertain time horizon. The system learns a set of latent patient subtypes from the offline electronic health record data, and trains a mixture of Gaussian Process experts, where each expert models the physiological data streams associated with a specific patient subtype. Transfer learning techniques are used to learn the relationship between a patients latent subtype and her static admission information (e.g., age, gender, transfer status, ICD-9 codes, etc). Results: Experiments conducted on data from a heterogeneous cohort of 6321 patients admitted to Ronald Reagan UCLA medical center show that our score significantly outperforms the currently deployed risk scores, such as the Rothman index, MEWS, APACHE, and SOFA scores, in terms of timeliness, true positive rate, and positive predictive value. Conclusion: Our results reflect the importance of adopting the concepts of personalized medicine in critical care settings; significant accuracy and timeliness gains can be achieved by accounting for the patients’ heterogeneity. Significance: The proposed risk scoring methodology can confer huge clinical and social benefits on a massive number of critically ill inpatients who exhibit adverse outcomes including, but not limited to, cardiac arrests, respiratory arrests, and septic shocks.

Sex Differences in Outcomes After STEMI: Effect Modification by Treatment Strategy and Age

Importance Previous works have shown that women hospitalized with ST-segment elevation myocardial infarction (STEMI) have higher short-term mortality rates than men. However, it is unclear if these differences persist among patients undergoing contemporary primary percutaneous coronary intervention (PCI). Objective To investigate whether the risk of 30-day mortality after STEMI is higher in women than men and, if so, to assess the role of age, medications, and primary PCI in this excess of risk. Design, Setting, and Participants From January 2010 to January 2016, a total of 8834 patients were hospitalized and received medical treatment for STEMI in 41 hospitals referring data to the International Survey of Acute Coronary Syndromes in Transitional Countries (ISACS-TC) registry (NCT01218776). Exposures Demographics, baseline characteristics, clinical profile, and pharmacological treatment within 24 hours and primary PCI. Main Outcomes and Measures Adjusted 30-day mortality rates estimated using inverse probability of treatment weighted (IPTW) logistic regression models. Results There were 2657 women with a mean (SD) age of 66.1 (11.6) years and 6177 men with a mean (SD) age of 59.9 (11.7) years included in the study. Thirty-day mortality was significantly higher for women than for men (11.6% vs 6.0%, P < .001). The gap in sex-specific mortality narrowed if restricting the analysis to men and women undergoing primary PCI (7.1% vs 3.3%, P < .001). After multivariable adjustment for comorbidities and treatment covariates, women under 60 had higher early mortality risk than men of the same age category (OR, 1.88; 95% CI, 1.04-3.26; P = .02). The risk in the subgroups aged 60 to 74 years and over 75 years was not significantly different between sexes (OR, 1.28; 95% CI, 0.88-1.88; P = .19 and OR, 1.17; 95% CI, 0.80-1.73; P = .40; respectively). After IPTW adjustment for baseline clinical covariates, the relationship among sex, age category, and 30-day mortality was similar (OR, 1.56 [95% CI, 1.05-2.3]; OR, 1.49 [95% CI, 1.15-1.92]; and OR, 1.21 [95% CI, 0.93-1.57]; respectively). Conclusions and Relevance Younger age was associated with higher 30-day mortality rates in women with STEMI even after adjustment for medications, primary PCI, and other coexisting comorbidities. This difference declines after age 60 and is no longer observed in oldest women.

Dynamic, data-driven processing of multispectral video streams

Video analytics plays an important role in a wide variety of defense-, monitoring- and surveillance-related systems for air and ground environments. In this context, multispectral video processing is attracting increased interest in recent years, due in part to technological advances in video capture. Compared with monochromatic video, multispectral video offers better spectral resolution, and different bands of multispectral video streams can enhance video analytics capabilities in different ways. For example, the infrared bands can provide better separation of shadows from objects, and improved spatial resolution in scenes that are impaired by fog or haze [16].

Personalized survival predictions via Trees of Predictors: An application to cardiac transplantation

Background Risk prediction is crucial in many areas of medical practice, such as cardiac transplantation, but existing clinical risk-scoring methods have suboptimal performance. We develop a novel risk prediction algorithm and test its performance on the database of all patients who were registered for cardiac transplantation in the United States during 1985-2015. Methods and findings We develop a new, interpretable, methodology (ToPs: Trees of Predictors) built on the principle that specific predictive (survival) models should be used for specific clusters within the patient population. ToPs discovers these specific clusters and the specific predictive model that performs best for each cluster. In comparison with existing clinical risk scoring methods and state-of-the-art machine learning methods, our method provides significant improvements in survival predictions, both post- and pre-cardiac transplantation. For instance: in terms of 3-month survival post-transplantation, our method achieves AUC of 0.660; the best clinical risk scoring method (RSS) achieves 0.587. In terms of 3-year survival/mortality predictions post-transplantation (in comparison to RSS), holding specificity at 80.0%, our algorithm correctly predicts survival for 2,442 (14.0%) more patients (of 17,441 who actually survived); holding sensitivity at 80.0%, our algorithm correctly predicts mortality for 694 (13.0%) more patients (of 5,339 who did not survive). ToPs achieves similar improvements for other time horizons and for predictions pre-transplantation. ToPs discovers the most relevant features (covariates), uses available features to best advantage, and can adapt to changes in clinical practice. Conclusions We show that, in comparison with existing clinical risk-scoring methods and other machine learning methods, ToPs significantly improves survival predictions both post- and pre-cardiac transplantation. ToPs provides a more accurate, personalized approach to survival prediction that can benefit patients, clinicians, and policymakers in making clinical decisions and setting clinical policy. Because survival prediction is widely used in clinical decision-making across diseases and clinical specialties, the implications of our methods are far-reaching.

Discovering Pediatric Asthma Phenotypes on the Basis of Response to Controller Medication Using Machine Learning

Rationale: Pediatric asthma has variable underlying inflammation and symptom control. Approaches to addressing this heterogeneity, such as clustering methods to find phenotypes and predict outcomes, have been investigated. However, clustering based on the relationship between treatment and clinical outcome has not been performed, and machine learning approaches for long‐term outcome prediction in pediatric asthma have not been studied in depth. Objectives: Our objectives were to use our novel machine learning algorithm, predictor pursuit (PP), to discover pediatric asthma phenotypes on the basis of asthma control in response to controller medications, to predict longitudinal asthma control among children with asthma, and to identify features associated with asthma control within each discovered pediatric phenotype. Methods: We applied PP to the Childhood Asthma Management Program study data (n = 1,019) to discover phenotypes on the basis of asthma control between assigned controller therapy groups (budesonide vs. nedocromil). We confirmed PPs ability to discover phenotypes using the Asthma Clinical Research Network/Childhood Asthma Research and Education network data. We next predicted childrens asthma control over time and compared PPs performance with that of traditional prediction methods. Last, we identified clinical features most correlated with asthma control in the discovered phenotypes. Results: Four phenotypes were discovered in both datasets: allergic not obese (A+/O−), obese not allergic (A−/O+), allergic and obese (A+/O+), and not allergic not obese (A−/O−). Of the children with well‐controlled asthma in the Childhood Asthma Management Program dataset, we found more nonobese children treated with budesonide than with nedocromil (P = 0.015) and more obese children treated with nedocromil than with budesonide (P = 0.008). Within the obese group, more A+/O+ childrens asthma was well controlled with nedocromil than with budesonide (P = 0.022) or with placebo (P = 0.011). The PP algorithm performed significantly better (P < 0.001) than traditional machine learning algorithms for both short‐ and long‐term asthma control prediction. Asthma control and bronchodilator response were the features most predictive of short‐term asthma control, regardless of type of controller medication or phenotype. Bronchodilator response and serum eosinophils were the most predictive features of asthma control, regardless of type of controller medication or phenotype. Conclusions: Advanced statistical machine learning approaches can be powerful tools for discovery of phenotypes based on treatment response and can aid in asthma control prediction in complex medical conditions such as asthma.

LATE PCI IN STEMI: A COMPLEX INTERACTION BETWEEN DELAY AND AGE

Data supporting the use of percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) beyond a 12-hour cut-off are sparse and contradictory. We aimed to investigate whether different delays to hospital presentation and patient clinical characteristics may