Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks

In clinical data sets we often find static information (e.g. patient gender, blood type, etc.) combined with sequences of data that are recorded during multiple hospital visits (e.g. medications prescribed, tests performed, etc.). Recurrent Neural Networks (RNNs) have proven to be very successful for modelling sequences of data in many areas of Machine Learning. In this work we present an approach based on RNNs, specifically designed for the clinical domain, that combines static and dynamic information in order to predict future events. We work with a database collected in the Charit\'{e} Hospital in Berlin that contains complete information concerning patients that underwent a kidney transplantation. After the transplantation three main endpoints can occur: rejection of the kidney, loss of the kidney and death of the patient. Our goal is to predict, based on information recorded in the Electronic Health Record of each patient, whether any of those endpoints will occur within the next six or twelve months after each visit to the clinic. We compared different types of RNNs that we developed for this work, with a model based on a Feedforward Neural Network and a Logistic Regression model. We found that the RNN that we developed based on Gated Recurrent Units provides the best performance for this task. We also used the same models for a second task, i.e., next event prediction, and found that here the model based on a Feedforward Neural Network outperformed the other models. Our hypothesis is that long-term dependencies are not as relevant in this task

Content and services issues for digital libraries

Describes the neglected area of e-collection building, on the taxonomy of e-collections and on the possible range of online services

Cross‐campus Collaboration: A Scientometric and Network Case Study of Publication Activity Across Two Campuses of a Single Institution

Team science and collaboration have become crucial to addressing key research questions confronting society. Institutions that are spread across multiple geographic locations face additional challenges. To better understand the nature of cross‐campus collaboration within a single institution and the effects of institutional efforts to spark collaboration, we conducted a case study of collaboration at Cornell University using scientometric and network analyses. Results suggest that cross‐campus collaboration is increasingly common, but is accounted for primarily by a relatively small number of departments and individual researchers. Specific researchers involved in many collaborative projects are identified, and their unique characteristics are described. Institutional efforts, such as seed grants and topical retreats, have some effect for researchers who are central in the collaboration network, but were less clearly effective for others

Reporting an Experience on Design and Implementation of e-Health Systems on Azure Cloud

Electronic Health (e-Health) technology has brought the world with significant transformation from traditional paper-based medical practice to Information and Communication Technologies (ICT)-based systems for automatic management (storage, processing, and archiving) of information. Traditionally e-Health systems have been designed to operate within stovepipes on dedicated networks, physical computers, and locally managed software platforms that make it susceptible to many serious limitations including: 1) lack of on-demand scalability during critical situations; 2) high administrative overheads and costs; and 3) in-efficient resource utilization and energy consumption due to lack of automation. In this paper, we present an approach to migrate the ICT systems in the e-Health sector from traditional in-house Client/Server (C/S) architecture to the virtualised cloud computing environment. To this end, we developed two cloud-based e-Health applications (Medical Practice Management System and Telemedicine Practice System) for demonstrating how cloud services can be leveraged for developing and deploying such applications. The Windows Azure cloud computing platform is selected as an example public cloud platform for our study. We conducted several performance evaluation experiments to understand the Quality Service (QoS) tradeoffs of our applications under variable workload on Azure.Comment: Submitted to third IEEE International Conference on Cloud and Green Computing (CGC 2013

Community-level Monitoring of HIV Spread

Health departments are using HIV data to monitor HIV growth in real time. The main purpose of this monitoring is to come up with policies for efficient allocation of medical resources. In order to achieve the efficient medical resources allocation, a method should be established for predicting where future transmissions of HIV will occur using the partial information of the transmission history. Validity of these predictions are of paramount importance as it affects the policy for allocation of medical resources. Indeed, the more accurate the prediction is, the more efficiently preventive care or other resources can be allocated to the network.The focus of this work is on community-level monitoring of HIV spread prevention. We have modeled the sexual network as communities of individuals and proposed community-level methods for prediction. Then, we have compared predictive power of the proposed methods in different settings of the network

Analysis of Care Coordination for Children with Special Health Care Needs: A Parent\u27s Perspective

Introduction. Care coordination involves organizing patient care activities and sharing information among all of the participants concerned with a patient\u27s care to achieve improved outcomes, a recent national focus. Compared to the national average, a higher percentage of Vermont children are cared for in an office that meets medical home criteria. However, there is limited research on medical home and care coordination for children with special health care needs (CSHCN) in the state of Vermont. Objectives. The goal of this study was to assess family perceptions, knowledge, and attitudes about how well care coordination is working for Vermont families with CSHCN. Methods. A paper and an electronic anonymous survey was developed for Vermont families with CSHCN. The surveys were then distributed by Vermont Family Network and the UVMMC Department of Pediatrics. Focus group interviews were also conducted at Vermont Family Network to provide family insight to explain the quantitative data. Results. 30 participants responded to the survey; only 20 completed it. The overall composite satisfaction score is 54%. This score takes into account 4 questions regarding care coordination satisfaction. Each question was formatted into a numerical value ranging from zero to five, with an overall score of 20 equating to 100% satisfaction. Discussion. Findings indicate that families with CSHCN are not satisfied with the level of care coordination currently provided. Respondents reported many barriers regarding care coordination, including lack of communication among health care providers, insurance coverage, and lack of support during transitional periods in care. Recommended improvements were identified.https://scholarworks.uvm.edu/comphp_gallery/1251/thumbnail.jp

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution