Data Analytics in Medicine最新文献

{"title":"Mobile Application for Patients' Waiting Time Control and Management of Diagnostic Imaging Examinations","authors":"Dimitrios Zarakovitis, Dimitrios Tsoromokos, N. Tsaloukidis, A. Lazakidou","doi":"10.4018/978-1-7998-1204-3.CH076","DOIUrl":"https://doi.org/10.4018/978-1-7998-1204-3.CH076","url":null,"abstract":"Geographic information systems (GIS) are useful informative systems for reducing the waiting time of diagnostic imaging examinations. ArcGIS software is used to develop a digital questionnaire which is used as a data collection tool. The information concerns the patients' personal information, type of examination required and medical history. Data is collected in real time and through GPS capabilities, the patients' exact coordinates are determined. GIS applications are used to create digital maps which display the average waiting time for performing imaging tests. Questionnaire data and interactive maps are uploaded to a digital platform. Through this application, it is possible to locate patients who actually need diagnostic imaging examinations in real time. Observing the location of patients on digital maps makes it easier to redirect them to the nearest health care units with the shortest waiting time. Data can also be acquired by mobile phones.","PeriodicalId":177246,"journal":{"name":"Data Analytics in Medicine","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122727757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real Time Analysis Based on Intelligent Applications of Big Data and IoT in Smart Health Care Systems","authors":"Mamata Rath","doi":"10.4018/IJBDAH.2018070104","DOIUrl":"https://doi.org/10.4018/IJBDAH.2018070104","url":null,"abstract":"Currently, there is an expanding interest for additional medical data from patients about their healthcare choices and related decisions, and they further need investment in their basic health issues. Big data provides patients presumptuous data to help them settle on the best choice and align with their medicinal treatment plan. One of the very advanced concepts related to the synthesis of big data sets to reveal the hidden pattern in them is big data analytics. It involves demanding techniques to mine and extract relevant data that includes the actions of piercing a database, effectively mine the data, query and inspect the data and is committed to enhance the technical execution of various task segments. The capacity to synthesize a lot of data can enable an association to manage data that can influence the business. In this way, the primary goal of big data analytics is to help business relationships to have enhanced comprehension of data, and subsequently, settle on proficient and very much educated decisions. Big data analytics empowers data diggers and researchers to examine an extensive volume of data that may not be outfit utilizing customary apparatuses. Big data analytics require advances and statistical instruments that can change a lot of organized, unstructured, and semi-organized data into more reasonable data and metadata designed for explanatory procedures. There is tremendous positive potential concerning the application of big data in human health care services and many related major applications are still in their developmental stages. The deployment of big data in health service demonstrates enhancing health care results and controlling the expenses of common people due to treatment, as proven by some developing use cases. Keeping in view such powerful processing capacity of big data analytics in various technical fields of modern civilization related to health care, the current research article presents a comprehensive study and investigation on big data analytics and its application in multiple sectors of society with significance in health care applications.","PeriodicalId":177246,"journal":{"name":"Data Analytics in Medicine","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116425562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of Healthcare Information Technology on Patient Outcomes","authors":"Edward T. Chen","doi":"10.4018/IJPHME.2018070103","DOIUrl":"https://doi.org/10.4018/IJPHME.2018070103","url":null,"abstract":"The purpose of this article is to review the existing literature on health information technology (HIT), specifically electronic health records (EHR) and life-long patient records, in order to provide a broad assessment of the current state of this technology in the United States. Relevant literature was reviewed to determine whether key hypotheses were validated. Areas for additional research and development were identified, and a potential path forward was proposed. HIT adoption is a worthwhile effort in the United States, and it is possible for us to enact an interoperable central records system within our current fee-for-service healthcare system. Wide scale adoption will require subsidies and regulatory involvement at the state level, but professional networks may be exploited to speed the rate of adoption. A four-tier architecture with autonomic security systems, properly validated, can provide the infrastructure necessary.","PeriodicalId":177246,"journal":{"name":"Data Analytics in Medicine","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132998690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards Semantic Interoperability in Health Data Management Facilitating Process Mining","authors":"Barbara Traxler, Emmanuel Helm, O. Krauss, Andreas Schuler, J. Kueng","doi":"10.4018/IJPHIM.2018070101","DOIUrl":"https://doi.org/10.4018/IJPHIM.2018070101","url":null,"abstract":"As an evidence-based business process analysis method, process mining can be used to investigate variations in delivery of care. Existing approaches are only based on one data source. A variety of data sources means different domain languages and understanding, special processes workflows in various organizations, varying documentation with different goals and different designations and varying use of coding systems. This article describes a modular, rule-based information extraction algorithm based on CDA and compares it to a proprietary healthcare reference model approach and a resource-based extraction of healthcare data using the new standard FHIR. All three approaches can be used to derive models to extract clinical and patient pathways. Similarities and differences according to interoperability and process mining tasks are described. It is concluded that standards-based approaches allow for more interoperability and can be used for a wide range of systems to provide process insight, thus facilitating better healthcare management across institutional boundaries.","PeriodicalId":177246,"journal":{"name":"Data Analytics in Medicine","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131819953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Study on Lifetime Enhancement and Reliability in Wearable Wireless Body Area Networks","authors":"S. Vasanthamani","doi":"10.4018/IJUDH.2018070103","DOIUrl":"https://doi.org/10.4018/IJUDH.2018070103","url":null,"abstract":"The wireless body area network (WBAN) which consists of wearable or implantable sensor nodes, is a technology that enables pervasive observing and delivery of health related information and services. The radio-enabled implantable medical devices offer a revolutionary set of applications among which we can point to precision drug distribution, smart endoscope capsules, glucose level observers and eye pressure detecting systems. Devices with WBAN are generally battery powered due to sensitivity and criticality of the data carried and handled by WBAN, reliability becomes a critical issues. WBAN loads a high degree of reliability as it openly affects the quality of patient observing. Undetected life-threatening circumstances can lead to death. A main requirement is that the health care professionals receive the monitored data correctly in emergency situations. The major objective is to achieve a reliable network with minimum delay and maximum throughput while considering power consumption by reducing unnecessary communication.","PeriodicalId":177246,"journal":{"name":"Data Analytics in Medicine","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129750690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care","authors":"R. Priyadarshini, Rabindra Kumar Barik, C. Panigrahi, Harishchandra Dubey, B. K. Mishra","doi":"10.4018/978-1-7998-1204-3.ch091","DOIUrl":"https://doi.org/10.4018/978-1-7998-1204-3.ch091","url":null,"abstract":"This article describes how machine learning (ML) algorithms are very useful for analysis of data and finding some meaningful information out of them, which could be used in various other applications. In the last few years, an explosive growth has been seen in the dimension and structure of data. There are several difficulties faced by conventional ML algorithms while dealing with such highly voluminous and unstructured big data. The modern ML tools are designed and used to deal with all sorts of complexities of data. Deep learning (DL) is one of the modern ML tools which are commonly used to find the hidden structure and cohesion among these large data sets by giving proper training in parallel platforms with intelligent optimization techniques to further analyze and interpret the data for future prediction and classification. This article focuses on the use of DL tools and software which are used in past couple of years in various areas and especially in the area of healthcare applications.","PeriodicalId":177246,"journal":{"name":"Data Analytics in Medicine","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122111552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Next Generation of IT","authors":"Antonio Mallia","doi":"10.4018/IJPHIM.2018070104","DOIUrl":"https://doi.org/10.4018/IJPHIM.2018070104","url":null,"abstract":"A potential new generation computing environment is emerging which combines wiki technology with semantic web concepts. This has brought about the fusion of the wiki execution ecosystem, a semantic web for model-driven applications, and a high-level language as an extension to wiki text for accelerated development. Semantic MediaWiki provides this platform and a fragment of a health record, including allergy intolerance as structured in HL7 FHIR with terminology bindings to SNOMED CT and to HL7 terminologies was developed by the author in a short timeframe (approximately 10 hours). The system navigates around the health record and controls the entry of terms in the record from controlled ValueSets. All terminologies and ValueSets are integrated into the prototype.","PeriodicalId":177246,"journal":{"name":"Data Analytics in Medicine","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128555636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review of Type 1 Diabetes (T1D)","authors":"Eileen O'Donnell, Liam O'Donnell","doi":"10.4018/IJUDH.2018070102","DOIUrl":"https://doi.org/10.4018/IJUDH.2018070102","url":null,"abstract":"The diagnosis of Type 1 Diabetes (T1D) will come as an unwelcome surprise to most people. Within a short period of time, the person will have to come to understand and manage this chronic illness. The terminology associated with the T1D condition will also be totally new to the person: diabetes mellitus, pancreas, hyperglycaemia (hyper), hypoglycaemia (hypo), bolus (fast acting insulin), basal (slow acting insulin), ketones and blood glucose levels. The purpose of this article is to assist newly diagnosed patients' understanding of T1D, people who are already living with T1D, carers of people with T1D, partners and family members of someone with T1D, work colleagues, and friends who participate in the same sporting activities or go on holiday with a person who has T1D. In addition, this article reviews how people living with T1D can still enjoy exercise and maintain the best quality of life possible; whilst controlling the blood glucose levels in their body for the rest of their lives to prevent the onset of complications associated with diabetes.","PeriodicalId":177246,"journal":{"name":"Data Analytics in Medicine","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134331820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Information and Communication Technologies in the Healthcare","authors":"J. Gomes, M. Romão","doi":"10.4018/IJPHIM.2018070105","DOIUrl":"https://doi.org/10.4018/IJPHIM.2018070105","url":null,"abstract":"Web portals, sensors, electronic records, video communication, on-line gaming and mobile apps. These are all examples of information and communication technologies (ICT) applications or devices that might cause benefits to healthcare. The ICT has the potential to dramatically change the way individuals or society see the sector, and provide tremendous opportunities for supporting professionals, improving effectiveness and efficiency. Healthcare organizations have become more and more challenged on how to assure a fair return from ICT investments. Thus, the application of project management in health is important because it allow more productivity and, as a direct result, more accessibility, higher quality care and a safer environment to patients. The study of the success of these initiatives has become vitally important for the hospitals performance. The article collects relevant data and provide recommendations about the perceived benefits of ICT project implementations, proposing a review of the published work to provide some insights into the benefits of these implementations.","PeriodicalId":177246,"journal":{"name":"Data Analytics in Medicine","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124242324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use-Case Driven Approach for a Pragmatic Implementation of Interoperability in eHealth","authors":"K. Bourquard, A. Berler","doi":"10.4018/IJRQEH.2017070104","DOIUrl":"https://doi.org/10.4018/IJRQEH.2017070104","url":null,"abstract":"Innovation in IT solutions introduces new usages in ehealth that allows the patient to be more active in his (her) health care and well-being. Strong expectations on electronic exchanges or shared medical documents from healthcare professionals and patients are pushing decision makers and stakeholders to develop quickly efficient and structured data enabled health IT infrastructures. One of the challenges of this paradigm shift is to provide at the point of care interoperable solutions in full compliance with regulations, ethics, organizational and technical states of the art. This paper describes this approach developed as a four steps process and defines the basic concepts for designing an interoperability infrastructure. For each of the steps, examples will be provided and discussed accordingly with the different interoperability layers (legal, policy, process, information and technical) developed in the Refined eHealth European Interoperability Framework (ReEIF) (eHealth Network, 2015).","PeriodicalId":177246,"journal":{"name":"Data Analytics in Medicine","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122673275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}