Predicting diabetes-related hospitalizations based on electronic health records

OBJECTIVE: To derive a predictive model to identify patients likely to be hospitalized during the following year due to complications attributed to Type II diabetes. METHODS: A variety of supervised machine learning classification methods were tested and a new method that discovers hidden patient clusters in the positive class (hospitalized) was developed while, at the same time, sparse linear support vector machine classifiers were derived to separate positive samples from the negative ones (non-hospitalized). The convergence of the new method was established and theoretical guarantees were proved on how the classifiers it produces generalize to a test set not seen during training. RESULTS: The methods were tested on a large set of patients from the Boston Medical Center - the largest safety net hospital in New England. It is found that our new joint clustering/classification method achieves an accuracy of 89% (measured in terms of area under the ROC Curve) and yields informative clusters which can help interpret the classification results, thus increasing the trust of physicians to the algorithmic output and providing some guidance towards preventive measures. While it is possible to increase accuracy to 92% with other methods, this comes with increased computational cost and lack of interpretability. The analysis shows that even a modest probability of preventive actions being effective (more than 19%) suffices to generate significant hospital care savings. CONCLUSIONS: Predictive models are proposed that can help avert hospitalizations, improve health outcomes and drastically reduce hospital expenditures. The scope for savings is significant as it has been estimated that in the USA alone, about $5.8 billion are spent each year on diabetes-related hospitalizations that could be prevented.Accepted manuscrip

Pathological Evidence Exploration in Deep Retinal Image Diagnosis

Though deep learning has shown successful performance in classifying the label and severity stage of certain disease, most of them give few evidence on how to make prediction. Here, we propose to exploit the interpretability of deep learning application in medical diagnosis. Inspired by Koch's Postulates, a well-known strategy in medical research to identify the property of pathogen, we define a pathological descriptor that can be extracted from the activated neurons of a diabetic retinopathy detector. To visualize the symptom and feature encoded in this descriptor, we propose a GAN based method to synthesize pathological retinal image given the descriptor and a binary vessel segmentation. Besides, with this descriptor, we can arbitrarily manipulate the position and quantity of lesions. As verified by a panel of 5 licensed ophthalmologists, our synthesized images carry the symptoms that are directly related to diabetic retinopathy diagnosis. The panel survey also shows that our generated images is both qualitatively and quantitatively superior to existing methods.Comment: to appear in AAAI (2019). The first two authors contributed equally to the paper. Corresponding Author: Feng L

Guanylate-binding protein 1 participates in cellular antiviral response to dengue virus

BACKGROUND: Dengue virus (DENV), the causative agent of human Dengue hemorrhagic fever, is a mosquito-borne virus found in tropical and sub-tropical regions around the world. Vaccines against DENV are currently unavailable. Guanylate-binding protein 1 (GBP1) is one of the Interferon (IFN) stimulated genes (ISGs) and has been shown important for host immune defense against various pathogens. However, the role of GBP1 during DENV infection remains unclarified. In this study, we evaluated the relevance of GBP1 to DENV infection in in vitro model. FINDINGS: Quantitative RT-PCR (qRT-PCR) and Western blot showed that the expression of mouse Gbp1 was dramatically upregulated in DENV-infected RAW264.7 cells. The intracellular DENV loads were significantly higher in Gbp1 silenced cells compared with controls. The expression levels of selective anti-viral cytokines were decreased in Gbp1 siRNA treated cells, while the transcription factor activity of NF-κB was impaired upon GBP1 silencing during infection. CONCLUSIONS: Our data suggested that GBP1 plays an antiviral role during DENV infection

The spectrum of low-pTp_{T} J/ψJ/\psi in heavy ion collisions in a fractal description

Transverse momentum spectrum of particles in hadron gas are affected by flow, quantum and strong interaction effects. Previously, most models focus on only one of the three effects but ignore others. The unconsidered effects are taken into the fitted parameters. In this paper, we study the three effects together from a new fractal angle by physical calculation instead of data fitting. Near the critical temperature, the three effects induce $J/\psi$ and neighboring meson to form a two-meson structure. We set up a two-particle fractal (TPF) model to describe this structure. We propose that under the three effects, $J/\psi$-$\pi$ two-meson state, $J/\psi$ and $\pi$ two-quark states form a self-similarity structure. With evolution, the two-meson structure disintegrate. We introduce an influencing factor $q_{fqs}$ to describe the flow, quantum and strong interaction effects and an escort factor $q_2$ to describe the binding force and the three effects. By solving the probability and entropy equations, we obtain the values of $q_{fqs}$ and $q_2$ at different collision energies and centrality classes. By substituting the value of $q_{fqs}$ into distribution function, we obtain the transverse momentum spectrum of low-$p_T$ $J/\psi$ and find it in good agreement with experimental data. We also analyze the evolution of $q_{fqs}$ with the temperature. It is found that $q_{fqs}$ is larger than 1. This is because the three effects decrease the number of microstates. We also find $q_{fqs}$ decreases with decreasing the temperature. This is consistent with the fact that with the system expansion, the influence of the three effects decrease.Comment: 9 pages, 3 figure

Glycosphingolipid GM3 is Indispensable for Dengue Virus Genome Replication

Dengue virus (DENV) causes the most prevalent arthropod-borne viral disease of humans worldwide. Glycosphingolipids (GSLs) are involved in virus infection by regulating various steps of viral-host interaction. However, the distinct role of GSLs during DENV infection remains unclear. In this study, we used mouse melanoma B16 cells and their GSL-deficient mutant counterpart GM95 cells to study the influence of GSLs on DENV infection. Surprisingly, GM95 cells were highly resistant to DENV infection compared with B16 cells. Pretreatment of B16 cells with synthetase inhibitor of GM3, the most abundant GSLs in B16 cells, or silencing GM3 synthetase T3GAL5, significantly inhibited DENV infection. DENV attachment and endocytosis were not impaired in GM95 cells, but DENV genome replication was obviously inhibited in GM95 cells compared to B16 cells. Furthermore, GM3 was colocalized with DENV viral replication complex on endoplasmic reticulum (ER) inside the B16 cells. Finally, GM3 synthetase inhibitor significantly reduced the mortality rate of suckling mice that challenged with DENV by impairing the viral replication in mouse brain. Taken together, these data indicated that GM3 was not required for DENV attachment and endocytosis, however, essential for viral genome replication. Targeting GM3 could be a novel strategy to inhibit DENV infection

Predicting Chronic Disease Hospitalizations from Electronic Health Records: An Interpretable Classification Approach

Urban living in modern large cities has significant adverse effects on health, increasing the risk of several chronic diseases. We focus on the two leading clusters of chronic disease, heart disease and diabetes, and develop data-driven methods to predict hospitalizations due to these conditions. We base these predictions on the patients' medical history, recent and more distant, as described in their Electronic Health Records (EHR). We formulate the prediction problem as a binary classification problem and consider a variety of machine learning methods, including kernelized and sparse Support Vector Machines (SVM), sparse logistic regression, and random forests. To strike a balance between accuracy and interpretability of the prediction, which is important in a medical setting, we propose two novel methods: K-LRT, a likelihood ratio test-based method, and a Joint Clustering and Classification (JCC) method which identifies hidden patient clusters and adapts classifiers to each cluster. We develop theoretical out-of-sample guarantees for the latter method. We validate our algorithms on large datasets from the Boston Medical Center, the largest safety-net hospital system in New England