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

Automated Synthetic-to-Real Generalization

Models trained on synthetic images often face degraded generalization to real data. As a convention, these models are often initialized with ImageNet pre-trained representation. Yet the role of ImageNet knowledge is seldom discussed despite common practices that leverage this knowledge to maintain the generalization ability. An example is the careful hand-tuning of early stopping and layer-wise learning rates, which is shown to improve synthetic-to-real generalization but is also laborious and heuristic. In this work, we explicitly encourage the synthetically trained model to maintain similar representations with the ImageNet pre-trained model, and propose a \textit{learning-to-optimize (L2O)} strategy to automate the selection of layer-wise learning rates. We demonstrate that the proposed framework can significantly improve the synthetic-to-real generalization performance without seeing and training on real data, while also benefiting downstream tasks such as domain adaptation. Code is available at: this https URL https://github.com/NVlabs/ASG

Automated Synthetic-to-Real Generalization

Models trained on synthetic images often face degraded generalization to real data. As a convention, these models are often initialized with ImageNet pre-trained representation. Yet the role of ImageNet knowledge is seldom discussed despite common practices that leverage this knowledge to maintain the generalization ability. An example is the careful hand-tuning of early stopping and layer-wise learning rates, which is shown to improve synthetic-to-real generalization but is also laborious and heuristic. In this work, we explicitly encourage the synthetically trained model to maintain similar representations with the ImageNet pre-trained model, and propose a \textit{learning-to-optimize (L2O)} strategy to automate the selection of layer-wise learning rates. We demonstrate that the proposed framework can significantly improve the synthetic-to-real generalization performance without seeing and training on real data, while also benefiting downstream tasks such as domain adaptation. Code is available at: https://github.com/NVlabs/ASG.Comment: Accepted to ICML 202

Glycoprotein is enough for sindbis virus-derived DNA vector to express heterogenous genes

To investigate the necessity and potential application of structural genes for expressing heterogenous genes from Sindbis virus-derived vector, the DNA-based expression vector pVaXJ was constructed by placing the recombinant genome of sindbis-like virus XJ-160 under the control of the human cytomegalovirus (CMV) promoter of the plasmid pVAX1, in which viral structural genes were replaced by a polylinker cassette to allow for insertion of heterologous genes. The defect helper plasmids pVaE or pVaC were developed by cloning the gene of glycoprotein E3E26KE1 or capsid protein of XJ-160 virus into pVAX1, respectively. The report gene cassette pVaXJ-EGFP or pV-Gluc expressing enhanced green fluorescence protein (EGFP) or Gaussia luciferase (G.luc) were constructed by cloning EGFP or G.luc gene into pVaXJ. EGFP or G.luc was expressed in the BHK-21 cells co-transfected with report gene cassettes and pVaE at levels that were comparable to those produced by report gene cassettes, pVaC and pVaE and were much higher than the levels produced by report gene cassette and pVaC, suggesting that glycoprotein is enough for Sindbis virus-derived DNA vector to express heterogenous genes in host cells. The method of gene expression from Sindbis virus-based DNA vector only co-transfected with envelop E gene increase the conveniency and the utility of alphavirus-based vector systems in general