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Background: Identifying people at risk of cardiovascular diseases (CVD) is a cornerstone of preventative cardiology. Risk prediction models currently recommended by clinical guidelines are typically based on a limited number of predictors with sub-optimal performance across all patient groups. Data-driven techniques based on machine learning (ML) might improve the performance of risk predictions by agnostically discovering novel risk predictors and learning the complex interactions between them. We tested (1) whether ML techniques based on a state-of-the-art automated ML framework (AutoPrognosis) could improve CVD risk prediction compared to traditional approaches, and (2) whether considering non-traditional variables could increase the accuracy of CVD risk predictions. Methods and findings: Using data on 423,604 participants without CVD at baseline in UK Biobank, we developed a ML-based model for predicting CVD risk based on 473 available variables. Our ML-based model was derived using AutoPrognosis, an algorithmic tool that automatically selects and tunes ensembles of ML modeling pipelines (comprising data imputation, feature processing, classification and calibration algorithms). We compared our model with a well-established risk prediction algorithm based on conventional CVD risk factors (Framingham score), a Cox proportional hazards (PH) model based on familiar risk factors (i.e, age, gender, smoking status, systolic blood pressure, history of diabetes, reception of treatments for hypertension and body mass index), and a Cox PH model based on all of the 473 available variables. Predictive performances were assessed using area under the receiver operating characteristic curve (AUC-ROC). Overall, our AutoPrognosis model improved risk prediction (AUC-ROC: 0.774, 95% CI: 0.768-0.780) compared to Framingham score (AUC-ROC: 0.724, 95% CI: 0.720-0.728, p < 0.001), Cox PH model with conventional risk factors (AUC-ROC: 0.734, 95% CI: 0.729-0.739, p < 0.001), and Cox PH model with all UK Biobank variables (AUC-ROC: 0.758, 95% CI: 0.753-0.763, p < 0.001). Out of 4,801 CVD cases recorded within 5 years of baseline, AutoPrognosis was able to correctly predict 368 more cases compared to the Framingham score. Our AutoPrognosis model included predictors that are not usually considered in existing risk prediction models, such as the individuals’ usual walking pace and their self-reported overall health rating. Furthermore, our model improved risk prediction in potentially relevant sub-populations, such as in individuals with history of diabetes. We also highlight the relative benefits accrued from including more information into a predictive model (information gain) as compared to the benefits of using more complex models (modeling gain). Conclusions: Our AutoPrognosis model improves the accuracy of CVD risk prediction in the UK Biobank population. This approach performs well in traditionally poorly served patient subgroups. Additionally, AutoPrognosis uncovered novel predictors for CVD disease that may now be tested in prospective studies. We found that the “information gain” achieved by considering more risk factors in the predictive model was significantly higher than the “modeling gain” achieved by adopting complex predictive models. [ABSTRACT FROM AUTHOR]

Modeling continuous-time physiological processes that manifest a patient's evolving clinical states is a key step in approaching many problems in healthcare. In this paper, we develop the Hidden Absorbing Semi-Markov Model (HASMM): a versatile probabilistic model that is capable of capturing the modern electronic health record (EHR) data. Unlike existing models, the HASMM accommodates irregularly sampled, temporally correlated, and informatively censored physiological data, and can describe non-stationary clinical state transitions. Learning the HASMM parameters from the EHR data is achieved via a novel forward-filtering backward-sampling Monte-Carlo EM algorithm that exploits the knowledge of the end-point clinical outcomes (informative censoring) in the EHR data, and implements the E-step by sequentially sampling the patients' clinical states in the reversetime direction while conditioning on the future states. Real-time inferences are drawn via a forward-filtering algorithm that operates on a virtually constructed discrete-time embedded Markov chain that mirrors the patient's continuous-time state trajectory. We demonstrate the prognostic utility of the HASMM in a critical care prognosis setting using a real-world dataset for patients admitted to the Ronald Reagan UCLA Medical Center. In particular, we show that using HASMMs, a patient's clinical deterioration can be predicted 8-9 hours prior to intensive care unit admission, with a 22% AUC gain compared to the Rothman index, which is the state-of-the-art critical care risk scoring technology. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

In this paper, we characterize the achievable (linear) degrees of freedom (DoFs) of the K-user single-input single-output (SISO) interference channel with blind interference alignment (BIA) using staggered antenna switching. In this scheme, each transmitter is equipped with one conventional antenna, and each receiver is equipped with one reconfigurable (multimode) antenna. Assuming that the channel is known to the receivers only, we show that by using linear BIA with staggered antenna switching at the receiver, a sum DoF of \text{2}K/(K+\text{2}) is achievable. This result implies that in using BIA, we can double the DoF achieved by orthogonal multiple-access schemes for a large number of users. Moreover, we propose a systematic algorithm to generate the transmit beamforming vectors and the reconfigurable antenna switching patterns, showing that it can achieve the \text{2}K/(K+\text{2})$ sum DoF for any K$. Finally, we apply this algorithm to the four-user SISO interference channel and demonstrate that \text{4}/\text{3} sum DoF is indeed achievable. [ABSTRACT FROM PUBLISHER]

In this paper, we introduce the exposed secondary-user (SU) problem in underlay cognitive radio systems, where both the secondary-to-primary and primary-to-secondary channels have a line-of-sight (LoS) component. Based on a Rician model for the LoS channels, we show, both analytically and numerically, that LoS interference hinders the achievable SU capacity when interference constraints are imposed at the primary user (PU) receiver. This is caused by the poor dynamic range of interference channel fluctuations when a dominant LoS component exists. To improve the capacity of such a system, we propose the use of an electronically steerable parasitic array radiator (ESPAR) antenna at the secondary terminals. An ESPAR antenna involves a single radio frequency (RF) chain and has a reconfigurable radiation pattern that is controlled by assigning arbitrary weights to $M$ orthonormal basis radiation patterns via altering a set of reactive loads. By viewing the orthonormal patterns as multiple virtual dumb antennas, we randomly vary their weights over time, creating artificial channel fluctuations that can perfectly eliminate the undesired impact of LoS interference. This scheme is termed as random aerial beamforming (RAB) and is well suited for compact and low-cost mobile terminals as it uses a single RF chain. Moreover, we investigate the exposed-SU problem in a multiuser setting, showing that LoS interference hinders multiuser interference diversity and affects the growth rate of the SU capacity as a function of the number of users. Using RAB, we show that LoS interference can, in fact, be exploited to improve multiuser diversity by boosting the effective number of users. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic continues to have a devastating impact on Brazil. Brazil's social, health and economic crises are aggravated by strong societal inequities and persisting political disarray. This complex scenario motivates careful study of the clinical, socioeconomic, demographic and structural factors contributing to increased risk of mortality from SARS-CoV-2 in Brazil specifically. We consider the Brazilian SIVEP-Gripe catalog, a very rich respiratory infection dataset which allows us to estimate the importance of several non-laboratorial and socio-geographic factors on COVID-19 mortality. We analyze the catalog using machine learning algorithms to account for likely complex interdependence between metrics. The XGBoost algorithm achieved excellent performance, producing an AUC-ROC of 0.813 (95% CI 0.810–0.817), and outperforming logistic regression. Using our model we found that, in Brazil, socioeconomic, geographical and structural factors are more important than individual comorbidities. Particularly important factors were: The state of residence and its development index; the distance to the hospital (especially for rural and less developed areas); the level of education; hospital funding model and strain. Ethnicity is also confirmed to be more important than comorbidities but less than the aforementioned factors. In conclusion, socioeconomic and structural factors are as important as biological factors in determining the outcome of COVID-19. This has important consequences for policy making, especially on vaccination/non-pharmacological preventative measures, hospital management and healthcare network organization. [ABSTRACT FROM AUTHOR]

Introduction: Identifying people at risk of cardiovascular diseases (CVD) is a cornerstone of preventative cardiology. Risk prediction models currently recommended by clinical guidelines are based on a limited number of predictors with sub-optimal performance across all patient groups. Hypothesis: We tested whether a state-of-the-art method machine learning (ML) method (AutoPrognosis) can improve the performance of risk predictions and discover novel CVD risk predictors. Methods: AutoPrognosis is an algorithmic tool that automatically selects and tunes ensembles of ML models and learns the relative importance of different risk predictors. Using data on 473 variables collected for 423,604 participants without CVD at baseline in UK Biobank, we evaluated the accuracy of AutoPrognosis in predicting the participants' future CVD events. We compared the predictive accuracy of AutoPrognosis with a standard risk prediction score (Framingham score), a Cox proportional hazards (PH) model based on 7 conventional risk factors, and a Cox PH model based on all variables in the UK Biobank. Predictive performances were assessed using area under the receiver operating characteristic curve (AUC-ROC). Results: Our AutoPrognosis model improved risk prediction (AUC-ROC: 0.764, 95% CI: 0.758-0.770) compared to Framingham score (AUC-ROC: 0.707, 95% CI: 0.703-0.711, p < 0.001), the Cox model that uses the 7 conventional risk factors (AUC-ROC: 0.721, 95% CI: 0.717-0.725, p < 0.001), and the Cox model that uses all of the UK Biobank variables (AUC-ROC: 0.741, 95% CI: 0.736-0.746, p < 0.001). Our AutoPrognosis model included predictors that are not usually considered in existing risk prediction models, such as the individuals' usual walking pace and their self-reported overall health rating. Furthermore, our model substantially improved risk prediction in individuals with history of diabetes (AUC-ROC: 0.756, 95% CI: 0.745-0.767) compared to the Framingham score (AUC-ROC: 0.634, 95% CI: 0.625-0.673, p < 0.001). Conclusions: Our AutoPrognosis improves the accuracy of CVD risk prediction in the UK Biobank population. Additionally, AutoPrognosis uncovered novel predictors for CVD disease that may now be tested in prospective studies. [ABSTRACT FROM AUTHOR]

Accurate prediction of survival for cystic fibrosis (CF) patients is instrumental in establishing the optimal timing for referring patients with terminal respiratory failure for lung transplantation (LT). Current practice considers referring patients for LT evaluation once the forced expiratory volume (FEV1) drops below 30% of its predicted nominal value. While FEV1 is indeed a strong predictor of CF-related mortality, we hypothesized that the survival behavior of CF patients exhibits a lot more heterogeneity. To this end, we developed an algorithmic framework, which we call AutoPrognosis, that leverages the power of machine learning to automate the process of constructing clinical prognostic models, and used it to build a prognostic model for CF using data from a contemporary cohort that involved 99% of the CF population in the UK. AutoPrognosis uses Bayesian optimization techniques to automate the process of configuring ensembles of machine learning pipelines, which involve imputation, feature processing, classification and calibration algorithms. Because it is automated, it can be used by clinical researchers to build prognostic models without the need for in-depth knowledge of machine learning. Our experiments revealed that the accuracy of the model learned by AutoPrognosis is superior to that of existing guidelines and other competing models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• G-G haplotype in IL-17A is negatively associated with HCC development in SOF, Peg-IFNα-2a, RBV treated CHC G4 patients. • Serum AFP is only associated with HCC development in Peg-IFNα-2a, RBV treated CHC G4 patients. • SOF might be linked with HCC development in some CHC G4 patients. • Haplotyping of IL-17A genes is crucial to personalize the proper treatment protocol for CHC G4 patient. Suspect has been directed towards some direct acting antivirals (DAAs) due to their reported association with hepatocellular carcinoma (HCC) development in chronic hepatitis C (CHC) patients. The mechanisms behind HCC development, following CHC treatment, were not well understood and may be linked to genetic variabilities in different patients which affect several cytokine productions involved in angiogenesis and inflammation. Of these variabilities, is the genetic polymorphisms in the interleukin-17 (IL-17) A receptor gene. Being an important pleiotropic cytokine, this study aimed to investigate the association between haplotypes in IL-17A receptor rs2275913 and rs3819024 and development of HCC in CHC patients treated with either triple therapy (sofosbuvir (SOF), pegylated interferon-alpha-2a (Peg-IFNα-2a) & ribavirin(RBV)) or with dual therapy (Peg-IFNα-2a&RBV). A cohort of 100 CHC patients was recruited in this study. Samples were tested for single nucleotide polymorphism (SNPs) in IL-17A receptor (rs2275913 and rs3819024) using TaqMan Genotyping assay. Our results showed that the presence of G-G haplotype in IL-17A (rs2275913& rs3819024) is inversely associated with HCC development in patients receiving triple therapy. While, high serum AFP levels are directly associated with HCC development in patients receiving triple therapy. However, in patients receiving dual therapy, HCC development was only associated with high serum alpha fetoprotein (AFP) levels and was not correlated to any specific allele in our studied SNPs. Such results highlight the importance of IL17A receptor gene haplotyping in the prediction of HCC development in patients receiving triple therapy. These results will aid in performing tailored, personalized strategy for CHC treatment. [ABSTRACT FROM AUTHOR]