A NEURAL NETWORK APPROACH TO FORECASTING EARNINGS PER SHARE

This paper explores the potential of neural networks to forecast earnings per share. The neural network would serve as a decision support system for finance managers, stock brokers and investment analysts and investors. Results of experiments with training/testing indicate that neural networks appear to be promising in forecasting EPS. Further investigations are necessary

HEALTHCARE INFORMATION SYSTEMS: A REVIEW OF ISSUES TOWARD RESEARCH THEMES AND AGENDAS INTO THE 21st CENTURY

Healthcare information systems encompass a wide range of issues from many disciplines including medicine, computer science, management science, statistics, biomedical engineering and numerous others. In the natural progression toward use of computers in healthcare, researchers in this multidisciplinary field are examining numerous issues ranging from examining the potential of artificial intelligence applications to application of total quality management principles to healthcare. This paper discusses various issues in an attempt toward development of a first cut framework/taxonomy of research themes and agendas for MIS researchers

Challenges in managing real-time data in health information system (HIS)

© Springer International Publishing Switzerland 2016. In this paper, we have discussed the challenges in handling real-time medical big data collection and storage in health information system (HIS). Based on challenges, we have proposed a model for realtime analysis of medical big data. We exemplify the approach through Spark Streaming and Apache Kafka using the processing of health big data Stream. Apache Kafka works very well in transporting data among different systems such as relational databases, Apache Hadoop and nonrelational databases. However, Apache Kafka lacks analyzing the stream, Spark Streaming framework has the capability to perform some operations on the stream. We have identified the challenges in current realtime systems and proposed our solution to cope with the medical big data streams

Trajectory of long-term outcome in severe pediatric diffuse axonal injury: An exploratory study

Introduction: Pediatric severe traumatic brain injury (TBI) is one of the leading causes of disability and death. One of the classic pathoanatomic brain injury lesions following severe pediatric TBI is diffuse (multifocal) axonal injury (DAI). In this single institution study, our overarching goal was to describe the clinical characteristics and long-term outcome trajectory of severe pediatric TBI patients with DAI.Methods: Pediatric patients (<18 years of age) with severe TBI who had DAI were retrospectively reviewed. We evaluated the effect of age, sex, Glasgow Coma Scale (GCS) score, early fever ≥ 38.5°C during the first day post-injury, the extent of ICP-directed therapy needed with the Pediatric Intensity Level of Therapy (PILOT) score, and MRI within the first week following trauma and analyzed their association with outcome using the Glasgow Outcome Score—Extended (GOS-E) scale at discharge, 6 months, 1, 5, and 10 years following injury.Results: Fifty-six pediatric patients with severe traumatic DAI were analyzed. The majority of the patients were >5 years of age and male. There were 2 mortalities. At discharge, 56% (30/54) of the surviving patients had unfavorable outcome. Sixty five percent (35/54) of surviving children were followed up to 10 years post-injury, and 71% (25/35) of them made a favorable recovery. Early fever and extensive DAI on MRI were associated with worse long-term outcomes.Conclusion: We describe the long-term trajectory outcome of severe pediatric TBI patients with pure DAI. While this was a single institution study with a small sample size, the majority of the children survived. Over one-third of our surviving children were lost to follow-up. Of the surviving children who had follow-up for 10 years after injury, the majority of these children made a favorable recovery

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Data-Driven Understanding of Smart Service Systems Through Text Mining

Smart service systems are everywhere, in homes and in the transportation, energy, and healthcare sectors. However, such systems have yet to be fully understood in the literature. Given the widespread applications of and research on smart service systems, we used text mining to develop a unified understanding of such systems in a data-driven way. Specifically, we used a combination of metrics and machine learning algorithms to preprocess and analyze text data related to smart service systems, including text from the scientific literature and news articles. By analyzing 5,378 scientific articles and 1,234 news articles, we identify important keywords, 16 research topics, 4 technology factors, and 13 application areas. We define ???smart service system??? based on the analytics results. Furthermore, we discuss the theoretical and methodological implications of our work, such as the 5Cs (connection, collection, computation, and communications for co-creation) of smart service systems and the text mining approach to understand service research topics. We believe this work, which aims to establish common ground for understanding these systems across multiple disciplinary perspectives, will encourage further research and development of modern service systems

NETIMIS: Dynamic Simulation of Health Economics Outcomes Using Big Data

Many healthcare organizations are now making good use of electronic health record (EHR) systems to record clinical information about their patients and the details of their healthcare. Electronic data in EHRs is generated by people engaged in complex processes within complex environments, and their human input, albeit shaped by computer systems, is compromised by many human factors. These data are potentially valuable to health economists and outcomes researchers but are sufficiently large and complex enough to be considered part of the new frontier of ‘big data’. This paper describes emerging methods that draw together data mining, process modelling, activity-based costing and dynamic simulation models. Our research infrastructure includes safe links to Leeds hospital’s EHRs with 3 million secondary and tertiary care patients. We created a multidisciplinary team of health economists, clinical specialists, and data and computer scientists, and developed a dynamic simulation tool called NETIMIS (Network Tools for Intervention Modelling with Intelligent Simulation; http://www.netimis.com) suitable for visualization of both human-designed and data-mined processes which can then be used for ‘what-if’ analysis by stakeholders interested in costing, designing and evaluating healthcare interventions. We present two examples of model development to illustrate how dynamic simulation can be informed by big data from an EHR. We found the tool provided a focal point for multidisciplinary team work to help them iteratively and collaboratively ‘deep dive’ into big data

From mechatronics to the Cloud

At its conception mechatronics was viewed purely in terms of the ability to integrate the technologies of mechanical and electrical engineering with computer science to transfer functionality, and hence complexity, from the mechanical domain to the software domain. However, as technologies, and in particular computing technologies, have evolved so the nature of mechatronics has changed from being purely associated with essentially stand-alone systems such as robots to providing the smart objects and systems which are the building blocks for Cyber-Physical Systems, and hence for Internet of Things and Cloud-based systems. With the possible advent of a 4th Industrial Revolution structured around these systems level concepts, mechatronics must again adapt its world view, if not its underlying technologies, to meet this new challenge