Mobile application for preliminary diagnosis of diseases

Publisher Copyright: © 2018 CEUR-WS. All Rights Reserved.The information system for analyzing the symptoms of a patient's disease, determining a preliminary diagnosis, and providing recommendations for contacting a doctor of a certain specialization is developed. The curent recommendation system performs the following main functions: preliminary medical diagnosis according to the selected symptoms; formation of reminder about taking medications; formation of history of taking medication.publishersversionPeer reviewe

Development of rule-based mobile diagnostics system

Mobile technologies are nowadays widely been used in many fields of professions due to its highly-efficient multitasking function and it is no exceptional to the psychology field. There are many mobile apps available in Google Play Store and Apple Store but those existing apps are only diagnosed for one particular cognitive disease. This project is intended to develop a mobile rule-based diagnosis system for several cognitive diseases for instance Post-traumatic stress disorder, autism spectrum disorder, bipolar disorder, obsessive compulsive disorder and social anxiety disorder. Reason for choosing these 5 cognitive diseases is because of these cognitive diseases are more common among the citizens in Malaysia. The purpose of this project is to gather the symptoms of these diseases and hence provide preliminary diagnosis to users through mobile application and yet raise the awareness of public towards these five cognitive diseases. Knowledge-based system framework is applied in developing the rule-based diagnosis mobile application and apparently, mobile application had developed by using Corona SDK. VisiRule software was used in generating valid and semantical rule-based system for this mobile application

Development of rule-based mobile diagnostics system

Mobile technologies are nowadays widely been used in many fields of professions due to its highly-efficient multitasking function and it is no exceptional to the psychology field. There are many mobile apps available in Google Play Store and Apple Store but those existing apps are only diagnosed for one particular cognitive disease. This project is intended to develop a mobile rule-based diagnosis system for several cognitive diseases for instance Post-traumatic stress disorder, autism spectrum disorder, bipolar disorder, obsessive compulsive disorder and social anxiety disorder. Reason for choosing these 5 cognitive diseases is because of these cognitive diseases are more common among the citizens in Malaysia. The purpose of this project is to gather the symptoms of these diseases and hence provide preliminary diagnosis to users through mobile application and yet raise the awareness of public towards these five cognitive diseases. Knowledge-based system framework is applied in developing the rule-based diagnosis mobile application and apparently, mobile application had developed by using Corona SDK. VisiRule software was used in generating valid and semantical rule-based system for this mobile application

The malaria system microApp: A new, mobile device-based tool for malaria diagnosis

Background: Malaria is a public health problem that affects remote areas worldwide. Climate change has contributed to the problem by allowing for the survival of Anopheles in previously uninhabited areas. As such, several groups have made developing news systems for the automated diagnosis of malaria a priority. Objective: The objective of this study was to develop a new, automated, mobile device-based diagnostic system for malaria. The system uses Giemsa-stained peripheral blood samples combined with light microscopy to identify the Plasmodium falciparum species in the ring stage of development. Methods: The system uses image processing and artificial intelligence techniques as well as a known face detection algorithm to identify Plasmodium parasites. The algorithm is based on integral image and haar-like features concepts, and makes use of weak classifiers with adaptive boosting learning. The search scope of the learning algorithm is reduced in the preprocessing step by removing the background around blood cells. Results: As a proof of concept experiment, the tool was used on 555 malaria-positive and 777 malaria-negative previously-made slides. The accuracy of the system was, on average, 91%, meaning that for every 100 parasite-infected samples, 91 were identified correctly. Conclusions: Accessibility barriers of low-resource countries can be addressed with low-cost diagnostic tools. Our system, developed for mobile devices (mobile phones and tablets), addresses this by enabling access to health centers in remote communities, and importantly, not depending on extensive malaria expertise or expensive diagnostic detection equipment.Peer ReviewedPostprint (published version

Disease surveillance and patient care in remote regions: an exploratory study of collaboration among healthcare professionals in Amazonia

The development and deployment of information technology, particularly mobile tools, to support collaboration between different groups of healthcare professionals has been viewed as a promising way to improve disease surveillance and patient care in remote regions. The effects of global climate change combined with rapid changes to land cover and use in Amazonia are believed to be contributing to the spread of vector-borne emerging and neglected diseases. This makes empowering and providing support for local healthcare providers all the more important. We investigate the use of information technology in this context to support professionals whose activities range from diagnosing diseases and monitoring their spread to developing policies to deal with outbreaks. An analysis of stakeholders, their roles and requirements, is presented which encompasses results of fieldwork and of a process of design and prototyping complemented by questionnaires and targeted interviews. Findings are analysed with respect to the tasks of diagnosis, training of local healthcare professionals, and gathering, sharing and visualisation of data for purposes of epidemiological research and disease surveillance. Methodological issues regarding the elicitation of cooperation and collaboration requirements are discussed and implications are drawn with respect to the use of technology in tackling emerging and neglected diseases

Physiology-Aware Rural Ambulance Routing

In emergency patient transport from rural medical facility to center tertiary hospital, real-time monitoring of the patient in the ambulance by a physician expert at the tertiary center is crucial. While telemetry healthcare services using mobile networks may enable remote real-time monitoring of transported patients, physiologic measures and tracking are at least as important and requires the existence of high-fidelity communication coverage. However, the wireless networks along the roads especially in rural areas can range from 4G to low-speed 2G, some parts with communication breakage. From a patient care perspective, transport during critical illness can make route selection patient state dependent. Prompt decisions with the relative advantage of a longer more secure bandwidth route versus a shorter, more rapid transport route but with less secure bandwidth must be made. The trade-off between route selection and the quality of wireless communication is an important optimization problem which unfortunately has remained unaddressed by prior work. In this paper, we propose a novel physiology-aware route scheduling approach for emergency ambulance transport of rural patients with acute, high risk diseases in need of continuous remote monitoring. We mathematically model the problem into an NP-hard graph theory problem, and approximate a solution based on a trade-off between communication coverage and shortest path. We profile communication along two major routes in a large rural hospital settings in Illinois, and use the traces to manifest the concept. Further, we design our algorithms and run preliminary experiments for scalability analysis. We believe that our scheduling techniques can become a compelling aid that enables an always-connected remote monitoring system in emergency patient transfer scenarios aimed to prevent morbidity and mortality with early diagnosis treatment.Comment: 6 pages, The Fifth IEEE International Conference on Healthcare Informatics (ICHI 2017), Park City, Utah, 201

NeuroSVM: A Graphical User Interface for Identification of Liver Patients

Diagnosis of liver infection at preliminary stage is important for better treatment. In todays scenario devices like sensors are used for detection of infections. Accurate classification techniques are required for automatic identification of disease samples. In this context, this study utilizes data mining approaches for classification of liver patients from healthy individuals. Four algorithms (Naive Bayes, Bagging, Random forest and SVM) were implemented for classification using R platform. Further to improve the accuracy of classification a hybrid NeuroSVM model was developed using SVM and feed-forward artificial neural network (ANN). The hybrid model was tested for its performance using statistical parameters like root mean square error (RMSE) and mean absolute percentage error (MAPE). The model resulted in a prediction accuracy of 98.83%. The results suggested that development of hybrid model improved the accuracy of prediction. To serve the medicinal community for prediction of liver disease among patients, a graphical user interface (GUI) has been developed using R. The GUI is deployed as a package in local repository of R platform for users to perform prediction.Comment: 9 pages, 6 figure

Application of Smartphone Technology in the Management and Treatment of Mental Illnesses

Abstract: Background: Mental illness continues to be a significant Public Health problem and the innovative use of technology to improve the treatment of mental illnesses holds great public health relevance. Over the past decade telecommunications technology has been used to increase access to and improve the quality of mental health care. There is current evidence that the use of landline and cellular telephones, computer-assisted therapy, and videoconferencing can be effective in improving treatment outcomes. Smartphones, as the newest development in communications technology, offer a new opportunity to improve mental health care through their versatile nature to perform a variety of functions. Methods: A critical literature review was performed to examine the potential of smartphones to increase access to mental health care, reduce barriers to care, and improve patient treatment outcomes. The review was performed by searching several electronic databases using a combination of keywords related to smartphones and mental health interventions using mobile devices. Literature concerning the use of cell phones, handheld computers, and smartphones to improve access to mental health care and improve treatment outcomes was identified.Results: The majority of studies identified were feasibility and pilot studies on patients with a variety of diagnosed mental illnesses using cell phones and PDAs. Authors report that most study participants, with some exceptions, were capable of using a mobile device and found them acceptable to use. Few studies extensively measured treatment outcomes and instead reported preliminary results and presented case illustrations. Studies which used smartphones successfully used them collect data on patients and deliver multimedia interventions. Discussion: The current literature offers encouraging evidence for the use of smartphones to improve mental health care but also reflects the lack of research conducted using smartphones. Studies which examine care provider use of smartphones to improve care is encouraging but has limited generalizability to mental health care. The feasibility of patient use of smartphones is also encouraging, but questions remain about feasibility in some sub-populations, particularly schizophrenia patients. Pilot testing of mobile devices and applications can greatly increase the feasibility of using smartphones in mental health care. Patients who are unfamiliar with smartphones will likely need initial training and support in their use. Conclusion: The literature identified several ways in which smartphones can increase access to care, reduce barriers, and improve treatment outcomes. Study results were encouraging but scientifically weak. Future studies are needed replicating results of studies using cell phones and PDAs on smartphones. Larger and higher quality studies are needed to examine the feasibility, efficacy, and cost-effectiveness of smartphones to deliver multiple component interventions that improve access to mental health care and improve treatment outcomes