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

Simulation in manufacturing and business: A review

Copyright @ 2009 Elsevier B.V.This paper reports the results of a review of simulation applications published within peer-reviewed literature between 1997 and 2006 to provide an up-to-date picture of the role of simulation techniques within manufacturing and business. The review is characterised by three factors: wide coverage, broad scope of the simulation techniques, and a focus on real-world applications. A structured methodology was followed to narrow down the search from around 20,000 papers to 281. Results include interesting trends and patterns. For instance, although discrete event simulation is the most popular technique, it has lower stakeholder engagement than other techniques, such as system dynamics or gaming. This is highly correlated with modelling lead time and purpose. Considering application areas, modelling is mostly used in scheduling. Finally, this review shows an increasing interest in hybrid modelling as an approach to cope with complex enterprise-wide systems

The rationale of e-health evaluation: The case of NHS Direct

An important area of research is that of the evaluation of e-health services. A holistic e-health evaluation framework should address the aspects that are hampering healthcare services from embracing the full potential of information and communication technologies towards successful e-health initiatives. Towards building a holistic evaluation framework for e-health services, this paper is intended to examine the rationale of e-health evaluation, as the paper argues that this aspect should be addressed first in the development of such a framework. NHS Direct which is one of the largest e-health services in the world has been chosen to discuss and validate a set of evaluation rationales and their applicability in practice

Developing a Framework for Creating mHealth Surveys

Various issues in the design of surveys for mobile health (mHealth) research projects yet exist. As mHealth solutions become more popular, new issues are brought into consideration. Researchers need to collect some critical information from participants in these mHealth studies. These mHealth studies require a specialized framework to create surveys, track progress and analyze user data. In these procedures, mHealth’s needs differ from other studies. Therefore, there has to be a new framework that satisfies needs of mHealth research studies. Although there are studies for creating efficient, robust and user-friendly surveys, there is no solution or study, which is specialized in mHealth area and solves specific problems of mHealth research studies. mHealth research studies sometimes require real-time access to user data. Reward systems may play a key role in their study. Most importantly, storing user information securely plays a key role in these studies. There is no such solution or study, which covers all these areas. In this thesis, we present guidelines for developing a framework for creating mHealth surveys. In doing this, we hope that we propose a solution for problems of creating and using of surveys in mHealth studies

An Investigation into Mobile Based Approach for Healthcare Activities, Occupational Therapy System

This research is to design and optimize the high quality of mobile apps, especially for iOS. The objective of this research is to develop a mobile system for Occupational therapy specialists to access and retrieval information. The investigation identifies the key points of using mobile-D agile methodology in mobile application development. It considers current applications within a different platform. It achieves new apps (OTS) for the health care activities

BCAS: A Web-enabled and GIS-based Decision Support System for the Diagnosis and Treatment of Breast Cancer

For decades, geographical variations in cancer rates have been observed but the precise determinants of such geographic differences in breast cancer development are unclear. Various statistical models have been proposed. Applications of these models, however, require that the data be assembled from a variety of sources, converted into the statistical models’ parameters and delivered effectively to researchers and policy makers. A web-enabled and GIS-based system can be developed to provide the needed functionality. This article overviews the conceptual web-enabled and GIS-based system (BCAS), illustrates the system’s use in diagnosing and treating breast cancer and examines the potential benefits and implications for breast cancer research and practice

Bioengineered Textiles and Nonwovens – the convergence of bio-miniaturisation and electroactive conductive polymers for assistive healthcare, portable power and design-led wearable technology

Today, there is an opportunity to bring together creative design activities to exploit the responsive and adaptive ‘smart’ materials that are a result of rapid development in electro, photo active polymers or OFEDs (organic thin film electronic devices), bio-responsive hydrogels, integrated into MEMS/NEMS devices and systems respectively. Some of these integrated systems are summarised in this paper, highlighting their use to create enhanced functionality in textiles, fabrics and non-woven large area thin films. By understanding the characteristics and properties of OFEDs and bio polymers and how they can be transformed into implementable physical forms, innovative products and services can be developed, with wide implications. The paper outlines some of these opportunities and applications, in particular, an ambient living platform, dealing with human centred needs, of people at work, people at home and people at play. The innovative design affords the accelerated development of intelligent materials (interactive, responsive and adaptive) for a new product & service design landscape, encompassing assistive healthcare (smart bandages and digital theranostics), ambient living, renewable energy (organic PV and solar textiles), interactive consumer products, interactive personal & beauty care (e-Scent) and a more intelligent built environment