DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach

Energy conservation more effective with rebound policy

This article sketches the problem of indirect energy use effects, also known as rebound, of energy conservation. There is widespread support for energy conservation, especially when it is voluntary, as this seems a cheap way to realize environmental and energy-climate goals. However, this overlooks the phenomenon of rebound. The topic of energy rebound has mainly attracted attention from energy analysts, but has been surprisingly neglected in environmental economics, even though economists generally are concerned with indirect or economy-wide impacts of technical change and policies. This paper presents definitions and interpretations of energy and environmental rebound, as well as four fundamental reasons for the existence of the rebound phenomenon. It further offers the most complete list of rebound pathways or mechanisms available in the literature. In addition, it discusses empirical estimates of rebound and addresses the implications of uncertainties and difficulties in assessing rebound. Suggestions are offered for strategies and public policies to contain rebound. It is advised that rebound evaluation is an essential part of environmental policy and project assessments. As opposed to earlier studies, this paper stresses the relevance of the distinction between energy conservation resulting from autonomous demand changes and from efficiency improvements in technology/equipment. In addition, it argues that rebound is especially relevant for developing countries. © 2010 The Author(s)

Web-MCQ: A set of methods and freely available open source code for administering online multiple choice question assessments

E-learning approaches have received increasing attention in recent years. Accordingly, a number of tools have become available to assist the nonexpert computer user in constructing and managing virtual learning environments, and implementing computer-based and / or online procedures to support pedagogy. Both commercial and free packages are now available, with new developments emerging periodically. Commercial products have the advantage of being comprehensive and reliable, but tend to require substantial financial investment, are not always transparent to use. They may also restrict pedagogical choices due to their predetermined ranges of functionality. With these issues in mind, several authors have argued for the pedagogical benefits of developing freely available, open source e-learning resources, which can be shared and further developed within a community of educational practitioners. The current paper supports this objective by presenting a set of methods, along with supporting freely available, downloadable, open source programming code, to allow administration of online multiple choice question assessments to students

Heat-release behavior of fuel combustion additives

The purpose of this study was to evaluate the relationship between solution-phase thermal stabilities of selected fuel additives and their effectiveness as combustion improvers. The additives selected for this study were 2-ethylhexyl nitrate, isopropyl nitrate, tetraethylene glycol dinitrate, di(tert-butyl) peroxide, and methylcyclopentadienyl manganese tricarbonyl. Decomposition studies were carried Out at various temperatures on the neat additives as well as the additives dissolved in various solvents and fuels. Differential scanning calorimetry was used to survey thermal stabilities; high-pressure differential scanning calorimetry was used to determine the effects of pressure on the decomposition exotherms of the additives. Critical temperatures were calculated by the Frank-Kamenetskii equation for each additive. Neither the temperature of maximum exotherm nor the heat released by additive decomposition correlated well with the effectiveness of a given additive as a cetane improver