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.
We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data. To this end, we propose a two-stage framework for ...building anomaly detectors using normal training data only. We first learn self-supervised deep representations and then build a generative one-class classifier on learned representations. We learn representations by classifying normal data from the CutPaste, a simple data augmentation strategy that cuts an image patch and pastes at a random location of a large image. Our empirical study on MVTec anomaly detection dataset demonstrates the proposed algorithm is general to be able to detect various types of real-world defects. We bring the improvement upon previous arts by 3.1 AUCs when learning representations from scratch. By transfer learning on pretrained representations on ImageNet, we achieve a new state-of-the-art 96.6 AUC. Lastly, we extend the framework to learn and extract representations from patches to allow localizing defective areas without annotations during training.
Adaptive Ensemble Learning With Confidence Bounds Tekin, Cem; Jinsung Yoon; van der Schaar, Mihaela
IEEE transactions on signal processing,
2017-Feb.15,-15, 2017-2-15, Volume:
65, Issue:
4
Journal Article
Peer reviewed
Open access
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.
This study presents the filter design of GNSS/IMU integration for wearable EPTS (Electronic Performance and Tracking System) of football players. EPTS has been widely used in sports fields recently, ...and GNSS (Global Navigation Satellite System) and IMU (Inertial Measurement Unit) in wearable EPTS have been used to measure and provide players' athletic performance data. A sensor fusion technique can be used to provide high-quality analysis data of athletic performance. For this reason, the integration filter of GNSS data and IMU data is designed in this study. The loosely-coupled strategy is considered to integrate GNSS and IMU data considering the specification of the wearable EPTS product. Quaternion is used to estimate a player's attitude to avoid the gimbal lock singularity in this study. Experiment results validate the performance of the proposed GNSS/IMU loosely-coupled integration filter for wearable EPTS of football players.
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 patient's 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.
ST-segment elevation myocardial infarction (STEMI) complicated by symptoms of acute de novo heart failure is associated with excess mortality. Whether development of heart failure and its outcomes ...differ by sex is unknown.
This study sought to examine the relationships among sex, acute heart failure, and related outcomes after STEMI in patients with no prior history of heart failure recorded at baseline.
Patients were recruited from a network of hospitals in the ISACS-TC (International Survey of Acute Coronary Syndromes in Transitional Countries) registry (NCT01218776). Main outcome measures were incidence of Killip class ≥II at hospital presentation and risk-adjusted 30-day mortality rates were estimated using inverse probability of weighting and logistic regression models.
This study included 10,443 patients (3,112 women). After covariate adjustment and matching for age, cardiovascular risk factors, comorbidities, disease severity, and delay to hospital presentation, the incidence of de novo heart failure at hospital presentation was significantly higher for women than for men (25.1% vs. 20.0%, odds ratio OR: 1.34; 95% confidence interval CI: 1.21 to 1.48). Women with de novo heart failure had higher 30-day mortality than did their male counterparts (25.1% vs. 20.6%; OR: 1.29; 95% CI: 1.05 to 1.58). The sex-related difference in mortality rates was still apparent in patients with de novo heart failure undergoing reperfusion therapy after hospital presentation (21.3% vs. 15.7%; OR: 1.45; 95% CI: 1.07 to 1.96).
Women are at higher risk to develop de novo heart failure after STEMI and women with de novo heart failure have worse survival than do their male counterparts. Therefore, de novo heart failure is a key feature to explain mortality gap after STEMI among women and men.
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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.
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.
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.
The inertial measurement unit (IMU) and magnetic, angular rate, and gravity (MARG) sensor orientation and position are widely used in the medical, robotics, and other fields. In general, the ...orientations can be defined by the integration of angular velocity data, and the positions are also computed from the double integration of acceleration data. However, the acceleration and angular velocity data are often inaccurate due to measurement errors which arise when the sensor moves quickly. Therefore, the orientations and positions significantly differ from the actual values. To address these issues, several techniques are proposed for the accurate measurement of IMU and MARG sensor orientations and positions. The proposed optimization method is applied to raw sensor data to compute faithful orientations by stabilizing and accelerating the convergence of the optimization process. Furthermore, a deep neural network based on 1D convolutional neural network (CNN) layers is proposed to predict the desired velocity from raw acceleration data. The method is validated qualitatively and quantitatively with an optical motion capture (mocap) system. The experimental results show that the proposed method significantly improves orientation and position estimations compared to those of other approaches.
Background It is still unknown whether traditional risk factors may have a sex-specific impact on coronary artery disease (CAD) burden. Methods and Results We identified 14 793 patients who underwent ...coronary angiography for acute coronary syndromes in the ISACS-TC (International Survey of Acute Coronary Syndromes in Transitional Countries; ClinicalTrials.gov, NCT01218776) registry from 2010 to 2019. The main outcome measure was the association between traditional risk factors and severity of CAD and its relationship with 30-day mortality. Relative risk (RR) ratios and 95% CIs were calculated from the ratio of the absolute risks of women versus men using inverse probability of weighting. Estimates were compared by test of interaction on the log scale. Severity of CAD was categorized as obstructive (≥50% stenosis) versus nonobstructive CAD. The RR ratio for obstructive CAD in women versus men among people without diabetes mellitus was 0.49 (95% CI, 0.41-0.60) and among those with diabetes mellitus was 0.89 (95% CI, 0.62-1.29), with an interaction by diabetes mellitus status of
=0.002. Exposure to smoking shifted the RR ratios from 0.50 (95% CI, 0.41-0.61) in nonsmokers to 0.75 (95% CI, 0.54-1.03) in current smokers, with an interaction by smoking status of
=0.018. There were no significant sex-related interactions with hypercholesterolemia and hypertension. Women with obstructive CAD had higher 30-day mortality rates than men (RR, 1.75; 95% CI, 1.48-2.07). No sex differences in mortality were observed in patients with nonobstructive CAD. Conclusions Obstructive CAD in women signifies a higher risk for mortality compared with men. Current smoking and diabetes mellitus disproportionally increase the risk of obstructive CAD in women. Achieving the goal of improving cardiovascular health in women still requires intensive efforts toward further implementation of lifestyle and treatment interventions. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT01218776.
Background We hypothesized that female sex is a treatment effect modifier of blood flow and related 30-day mortality after primary percutaneous coronary intervention ( PCI ) for ST -segment-elevation ...myocardial infarction and that the magnitude of the effect on outcomes differs depending on delay to hospital presentation. Methods and Results We identified 2596 patients enrolled in the ISACS - TC (International Survey of Acute Coronary Syndromes in Transitional Countries) registry from 2010 to 2016. Primary outcome was the occurrence of 30-day mortality. Key secondary outcome was the rate of suboptimal post- PCI Thrombolysis in Myocardial Infarction ( TIMI ; flow grade 0-2). Multivariate logistic regression and inverse probability of treatment weighted models were adjusted for baseline clinical covariates. We characterized patient outcomes associated with a delay from symptom onset to hospital presentation of ≤120 minutes. In multivariable regression models, female sex was associated with postprocedural TIMI flow grade 0 to 2 (odds ratio OR , 1.68; 95% CI , 1.15-2.44) and higher mortality ( OR, 1.72; 95% CI , 1.02-2.90). Using inverse probability of treatment weighting, 30-day mortality was higher in women compared with men (4.8% versus 2.5%; OR , 2.00; 95% CI , 1.27-3.15). Likewise, we found a significant sex difference in post- PCI TIMI flow grade 0 to 2 (8.8% versus 5.0%; OR , 1.83; 95% CI , 1.31-2.56). The sex gap in mortality was no longer significant for patients having hospital presentation of ≤120 minutes ( OR , 1.28; 95% CI , 0.35-4.69). Sex difference in post- PCI TIMI flow grade was consistent regardless of time to hospital presentation. Conclusions Delay to hospital presentation and suboptimal post- PCI TIMI flow grade are variables independently associated with excess mortality in women, suggesting complementary mechanisms of reduced survival. Clinical Trial Registration URL : http://www.clinicaltrials.gov . Unique identifier: NCT 01218776.
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