Patient Generated Health Data: Framework for Decision Making

Patient information is a major part of healthcare decision making. Although currently scattered due to multiple sources and diverse formats, decision making can be improved if the patient information is readily available in a unified manner. Mobile technologies can improve decision making by integrating patient information from multiple sources. This study explores how patient generated health data (PGHD) from multiple sources can lead to improved healthcare decision making. A semi-systematic review is conducted to analyze research articles for transparency, clarity, and complete reporting. We conceptualize the data generated by healthcare professional as primarily from EHR/EMR and the data generated by patient as primarily from mobile apps and wearables. Eight themes led to the development of Convergence Model for Patient Data (CMPD). A framework was developed to illustrate several scenarios, to identify quality and timeliness requirements in mobile healthcare environment, and to provide necessary decision support

An intelligent mobile-enabled expert system for tuberculosis disease diagnosis in real time

This paper presents an investigation into the development of an intelligent mobile-enabled expert system to perform an automatic detection of tuberculosis (TB) disease in real-time. One third of the global population are infected with the TB bacterium, and the prevailing diagnosis methods are either resource-intensive or time consuming. Thus, a reliable and easy–to-use diagnosis system has become essential to make the world TB free by 2030, as envisioned by the World Health Organisation. In this work, the challenges in implementing an efficient image processing platform is presented to extract the images from plasmonic ELISAs for TB antigen-specific antibodies and analyse their features. The supervised machine learning techniques are utilised to attain binary classification from eighteen lower-order colour moments. The proposed system is trained off-line, followed by testing and validation using a separate set of images in real-time. Using an ensemble classifier, Random Forest, we demonstrated 98.4% accuracy in TB antigen-specific antibody detection on the mobile platform. Unlike the existing systems, the proposed intelligent system with real time processing capabilities and data portability can provide the prediction without any opto-mechanical attachment, which will undergo a clinical test in the next phase.</p

Multimedia sensors embedded in smartphones for ambient assisted living and e-health

The final publication is available at link.springer.com[EN] Nowadays, it is widely extended the use of smartphones to make human life more comfortable. Moreover, there is a special interest on Ambient Assisted Living (AAL) and e-Health applications. The sensor technology is growing and amount of embedded sensors in the smartphones can be very useful for AAL and e-Health. While some sensors like the accelerometer, gyroscope or light sensor are very used in applications such as motion detection or light meter, there are other ones, like the microphone and camera which can be used as multimedia sensors. This paper reviews the published papers focused on showing proposals, designs and deployments of that make use of multimedia sensors for AAL and e-health. We have classified them as a function of their main use. They are the sound gathered by the microphone and image recorded by the camera. We also include a comparative table and analyze the gathered information.Parra-Boronat, L.; Sendra, S.; Jimenez, JM.; Lloret, J. (2016). Multimedia sensors embedded in smartphones for ambient assisted living and e-health. Multimedia Tools and Applications. 75(21):13271-13297. doi:10.1007/s11042-015-2745-8S13271132977521Acampora G, Cook DJ, Rashidi P, Vasilakos AV (2013) A survey on ambient intelligence in healthcare. Proc IEEE 101(12):2470–2494Al-Attas R, Yassine A, Shirmohammadi S (2012) Tele-Medical Applications in Home-Based Health Care. In proceeding of the 2012 I.E. International Conference on Multimedia and Expo Workshops (ICMEW 2012). Jul. 9–13, 2012. Melbourne, Australia. (pp. 441–446)Alemdar H, Ersoy C (2010) Wireless sensor networks for healthcare: a survey. Comput Netw 54(15):2688–2710Alqassim S, Ganesh M, Khoja S, Zaidi M, Aloul F, Sagahyroon A (2012) Sleep apnea monitoring using mobile phones. 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An Investigation of the Managerial Use of Mobile Business Intelligence

As a new trend in business intelligence (BI), mobile BI has been gaining increasing adoption by managers. However, there is little academic research about the managerial use of mobile BI. Adopting the key constructs of Task-Technology Fit theory and the Unified Theory of Acceptance and Use of Technology as the theoretical lens, this exploratory study aims to deliver a preliminary understanding on why and how managers use mobile BI, from both the managers’ and the vendor’s perspectives. A case study was conducted with a large government authority whose mobile BI vendor is an industry leader worldwide. Semi-structured interviews were carried out with seven senior managers from this organization and the vendor. Through discussing the reasons why managers use mobile BI and their use patterns, a series of emergent propositions are drawn. The empirical results from this study not only contribute to this currently underexplored area of mobile BI, but also help enable the industry to make mobile BI products that better suit managers’ needs. Available at: https://aisel.aisnet.org/pajais/vol10/iss3/4

Mapping evidence of mobile health technologies for disease diagnosis and treatment support by health workers in sub-Saharan Africa : a scoping review

BACKGROUND: The rapid growth of mobile technology has given rise to the development of mobile health (mHealth) applications aimed at treating and preventing a wide range of health conditions. However, evidence on the use of mHealth in high disease burdened settings such as sub-Sharan Africa is not clear. Given this, we systematically mapped evidence on mHealth for disease diagnosis and treatment support by health workers in sub-Saharan Africa. METHODS: We conducted a scoping review study guided by the Arksey and O’Malley’s framework, Levac et al. recommendations, and Joanna Briggs Institute guidelines. We thoroughly searched the following databases: MEDLINE and CINAHL with full text via EBSCOhost; PubMed; Science Direct and Google Scholar for relevant articles from the inception of mHealth technology to April 2020. Two reviewers independently screened abstracts and full-text articles using the eligibility criteria as reference. This study employed the mixed methods appraisal tool version 2018 to assess the methodological quality of the included studies. RESULTS: Out of the 798 articles identified, only 12 published articles presented evidence on the availability and use of mHealth for disease diagnosis and treatment support by health workers in SSA since 2010. Of the 12 studies, four studies were conducted in Kenya; two in Malawi; two in Nigeria; one in South Africa; one in Zimbabwe; one in Mozambique, and one in Lesotho. Out of the 12 studies, one reported the use of mHealth for diseases diagnosis; three reported the use of mHealth to manage HIV; two on the management of HIV/TB; two on the treatment of malaria; one each on the management of hypertension; cervical cancer; and three were not specific on any disease condition. All the 12 included studies underwent methodological quality appraisal with a scored between 70 and 100%. CONCLUSIONS: The study shows that there is limited research on the availability and use of mHealth by health workers for disease diagnosis and treatment support in sub-Saharan Africa. We, therefore, recommend primary studies focusing on the use of mHealth by health workers for disease diagnosis and treatment support in sub-Saharan Africa.Additional file 1: Electronic databases search results for the title screening.Additional file 2: Full articles screening results and output of degree of agreement in Stata version 13.Additional file 3: Methodological quality assessment.https://bmcmedinformdecismak.biomedcentral.compm2021School of Health Systems and Public Health (SHSPH

Feature Selection Using Firefly Optimization for Classification and Regression Models

In this research, we propose a variant of the Firefly Algorithm (FA) for discriminative feature selection in classification and regression models for supporting decision making processes using data-based learning methods. The FA variant employs Simulated Annealing (SA)-enhanced local and global promising solutions, chaotic-accelerated attractiveness parameters and diversion mechanisms of weak solutions to escape from the local optimum trap and mitigate the premature convergence problem in the original FA algorithm. A total of 29 classification and 11 regression benchmark data sets have been used to evaluate the efficiency of the proposed FA model. It shows statistically significant improvements over other state-of-the-art FA variants and classical search methods for diverse feature selection problems. In short, the proposed FA variant offers an effective method to identify optimal feature subsets in classification and regression models for supporting data-based decision making processes

Intelligent image-based colourimetric tests using machine learning framework for lateral flow assays

This paper aims to deliberately examine the scope of an intelligent colourimetric test that fulfils ASSURED criteria (Affordable, Sensitive, Specific, User-friendly, Rapid and robust, Equipment-free, and Deliverable) and demonstrate the claim as well. This paper presents an investigation into an intelligent image-based system to perform automatic paper-based colourimetric tests in real-time to provide a proof-of-concept for a dry-chemical based or microfluidic, stable and semi-quantitative assay using a larger dataset with diverse conditions. The universal pH indicator papers were utilised as a case study. Unlike the works done in the literature, this work performs multiclass colourimetric tests using histogram based image processing and machine learning algorithm without any user intervention. The proposed image processing framework is based on colour channel separation, global thresholding, morphological operation and object detection. We have also deployed a server based convolutional neural network framework for image classification using inductive transfer learning on a mobile platform. The results obtained by both traditional machine learning and pre-trained model-based deep learning were critically analysed with the set evaluation criteria (ASSURED criteria). The features were optimised using univariate analysis and exploratory data analysis to improve the performance. The image processing algorithm showed >98% accuracy while the classification accuracy by Least Squares Support Vector Machine (LS- SVM) was 100%. On the other hand, the deep learning technique provided >86% accuracy, which could be further improved with a large amount of data. The k-fold cross validated LS- SVM based final system, examined on different datasets, confirmed the robustness and reliability of the presented approach, which was further validated using statistical analysis. The understaffed and resource limited healthcare system can benefit from such an easy-to-use technology to support remote aid workers, assist in elderly care and promote personalised healthcare by eliminating the subjectivity of interpretation

Desarrollo y evaluación de un sistema móvil de ayuda a la decisión médica en el campo de la oftalmología

Hoy en día es tal el impacto que tiene el uso de dispositivos móviles como smartphones y tablets en nuestras vidas que es difícil imaginar tener que prescindir de ellos. Estos terminales han favorecido el desarrollo de multitud de aplicaciones móviles, pero el inmenso éxito de las apps se debe al fácil manejo de estas. Prácticamente cualquier persona es capaz de manejarlas, independientemente del nivel de sus conocimientos sobre tecnología. Es tal la confianza que las personas depositamos sobre estas herramientas software que hasta les encomendamos nuestra salud. Cada vez aparecen más aplicaciones relacionadas con la salud y con hábitos de vida saludables. Y ya no sólo eso, sino que el personal médico cada día está más compenetrado con sistemas de mSalud o eHealth. Los profesionales sanitarios son conscientes del potencial que ofrecen las Tecnologías de la Información y la Comunicación (TIC), y no dudan en aprovecharlas. La aplicación de las TIC en la medicina tiene vital importancia en la mejora de la atención primaria, puesto que en zonas rurales o de difícil acceso, con pocos recursos económicos, no es viable ofrecer una atención médica especializada. Una buena atención primaria es la base de un mejor sistema sanitario. Tomar una buena decisión a tiempo por parte del médico de atención primaria podría tener una repercusión enorme. Con objeto de facilitar la tarea de diagnóstico, surgen los sistemas de ayuda a la toma de decisiones médicas, los cuales ofrecen asesoramiento y además refrescan los conocimientos del médico, en una profesión donde se sigue aprendiendo día a día. La aplicación de estos sistemas en enfermedades que son causa frecuente de visita a la consulta médica como son las patologías oftalmológicas, que además afectan directamente a la calidad de vida, tiene aún mayor importancia. El objetivo de este proyecto es el desarrollo de OphthalDSS, una aplicación móvil para el sistema operativo Android que implemente uno de estos sistemas, teniendo como referencia una versión anterior denominada DeSSEaDo, para lo cual se analizará previamente el estado del arte de los sistemas de ayuda a la toma de decisiones médicas en el campo de la oftalmología, concretamente para enfermedades del segmento anterior del ojo.Grado en Ingeniería de Tecnologías de Telecomunicació

Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

Nowadays, large amounts of data are generated in the medical domain. Various physiological signals generatedfrom different organs can be recorded to extract interesting information about patients’health. The analysis ofphysiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery inDatabases process. The application of such process in the domain of medicine has a series of implications anddifficulties, especially regarding the application of data mining techniques to data, mainly time series, gatheredfrom medical examinations of patients. The goal of this paper is to describe the lessons learned and the experiencegathered by the authors applying data mining techniques to real medical patient data including time series. In thisresearch, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epilepticpatients). We applied a previously proposed knowledge discovery framework for classification purpose obtaininggood results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in ourresearch are the groundwork for the lessons learned and recommendations made in this position paper thatintends to be a guide for experts who have to face similar medical data mining projects.2019-2