An improved comorbidity summary score for measuring disease burden and predicting mortality with applications to two national cohorts

Abstract

Research involving administrative healthcare data to study patient outcomes requires the investigator to account for the patient’s disease burden in order to reduce the potential for biased results. Here we develop a comorbidity summary score based on variable importance measures derived from several statistical and machine learning methods and show it has superior predictive performance to the Elixhauser and Charlson indices when used to predict 1-year, 5-year, and 10-year mortality. We used two large Veterans Administration cohorts to develop and validate the summary score and compared predictive performance using the area under ROC curve (AUC) and the Brier score.     

PREDICTION INTERVALS FOR SUPPORT VECTOR MACHINE REGRESSION

ABSTRACT

The support vector machine (SVM), first developed by Vapnik and his group at AT&T Bell Laboratories, is being used as a new technique for regression and classification problems. In this paper we present an approach to estimating prediction intervals for SVM regression based on posterior predictive densities. Furthermore, the method is illustrated with a data example.     

Toward Probabilistic Prediction of Flash Flood Human Impacts

This article focuses on conceptual and methodological developments allowing the integration of physical and social dynamics leading to model forecasts of circumstance‐specific human losses during a flash flood. To reach this objective, a random forest classifier is applied to assess the likelihood of fatality occurrence for a given circumstance as a function of representative indicators. Here, vehicle‐related circumstance is chosen as the literature indicates that most fatalities from flash flooding fall in this category. A database of flash flood events, with and without human losses from 2001 to 2011 in the United States, is supplemented with other variables describing the storm event, the spatial distribution of the sensitive characteristics of the exposed population, and built environment at the county level. The catastrophic flash floods of May 2015 in the states of Texas and Oklahoma are used as a case study to map the dynamics of the estimated probabilistic human risk on a daily scale. The results indicate the importance of time‐ and space‐dependent human vulnerability and risk assessment for short‐fuse flood events. The need for more systematic human impact data collection is also highlighted to advance impact‐based predictive models for flash flood casualties using machine‐learning approaches in the future.     

Response-adaptive trial designs with accelerated Thompson sampling

In a clinical trial, sometimes it is desirable to allocate as many patients as possible to the best treatment, in particular, when a trial for a rare disease may contain a considerable portion of the whole target population. The Gittins index rule is a powerful tool for sequentially allocating patients to the best treatment based on the responses of patients already treated. However, its application in clinical trials is limited due to technical complexity and lack of randomness. Thompson sampling is an appealing approach, since it makes a compromise between optimal treatment allocation and randomness with some desirable optimal properties in the machine learning context. However, in clinical trial settings, multiple simulation studies have shown disappointing results with Thompson samplers. We consider how to improve short-run performance of Thompson sampling and propose a novel acceleration approach. This approach can also be applied to situations when patients can only be allocated by batch and is very easy to implement without using complex algorithms. A simulation study showed that this approach could improve the performance of Thompson sampling in terms of average total response rate. An application to a redesign of a preference trial to maximize patient's satisfaction is also presented.     

R package rjmcmc: reversible jump MCMC using post‐processing

The rjmcmc package for R implements the post‐processing reversible jump Markov chain Monte Carlo (MCMC) algorithm of Barker & Link. MCMC output from each of the models is used to estimate posterior model probabilities and Bayes factors. Automatic differentiation is used to simplify implementation. The package is demonstrated on two examples.     

3D打印著作权法律问题探究

面对3D打印技术发展进程中日益凸显的著作权法律问题,阐释3D打印数字模型在著作权法中的法律界定及保护范围,对3D打印数字模型究竟属于何种作品进行了探讨;指出3D数字模型到3D打印物的转换应为复制,以及3D数字模型和3D打印物的著作权侵权风险问题;提出应兼顾各方利益,对3D打印的著作权保护要在私人利益与公共利益之间寻找新的平衡.     

桶装水灌装机优化设计

针对现有桶装水灌装生产线中灌装机产能较低、产品适应能力差的缺点,提出了能够适用于19升、17升、11升等多种桶型的高速柔性化灌装机的设计方案。灌装机的灌装头采用气动比例调节阀搭配传感器进行闭环控制,使得灌装液位精确,减少液体飞溅;进瓶装置采用星轮拨爪和由伺服电机驱动的链条传动装置联动的方式,实现水桶的精准拉距,实现多种瓶型快速切换的功能;瓶盖输送机构,结合盖子杀菌柜,实现在线杀菌功能,安全可靠。应用结果表明文章的设计能改善原有桶装水灌装机柔性差、速度慢、可靠性低的现状。优化设计的桶装水灌装机提高了生产效率,降低成本。     

附着与隧通——心智的工作模式

深度学习框架加速了人工智能技术的发展,越来越多的人工智能系统在专业领域超越人类,但沿此路径能否指引机器模拟出类人智能仍然没有定论。强计算主义的观点主张意识和智能可以被物理还原,甚至可以忽略意识。哥德尔等学者则倾向于机器无法模拟或超越人类。文章提出认知坎陷（意识片段）的“附着”与“隧通”这一对范畴,从新的视角探讨心智的工作模式。认知坎陷具有超越时空和流变的特性,在某时某地某景可以“附着”在某个具体对象之上。认知坎陷之间可以通过“隧通”链接成一个网络。“自我”作为最原初和最重要的认知坎陷,是人类意识与智能的发端。智能的作用之一是在情景改变时将“自我”“附着”到更合适的对象上。     

基于AutoCAD的产品铭牌自动设计

本文在分析CAD数据交换文件DXF的基础上,根据文字绕排的形式,利用Windows系统提供的动态数据交换（DDE）技术,讨论了由VB通过DDE来控制AutoCAD自动绘图的方法,在Auto-CAD中,实现了铝牌、印章的自动设计。     

以PLC为核心的自动监测系统的设计方法

本文主要讨论了以可编程序控制器（PLC）为核心的自动监测系统的设计问题,PLC是一种新型自动控制器件,它具有计算机的部分功能,又能直接与强电相连接,是自控系统的一个重要分支,本文讨论了PLC应用系统的软、硬件设计方法．