Automatic Screening and Classification of Diabetic Retinopathy Eye Fundus Image

Diabetic Retinopathy (DR) is a disorder of the retinal vasculature. It develops to some degree in nearly all patients with long-standing diabetes mellitus and can result in blindness. Screening of DR is essential for both early detection and early treatment. This thesis aims to investigate automatic methods for diabetic retinopathy detection and subsequently develop an effective system for the detection and screening of diabetic retinopathy. The presented diabetic retinopathy research involves three development stages. Firstly, the thesis presents the development of a preliminary classification and screening system for diabetic retinopathy using eye fundus images. The research will then focus on the detection of the earliest signs of diabetic retinopathy, which are the microaneurysms. The detection of microaneurysms at an early stage is vital and is the first step in preventing diabetic retinopathy. Finally, the thesis will present decision support systems for the detection of diabetic retinopathy and maculopathy in eye fundus images. The detection of maculopathy, which are yellow lesions near the macula, is essential as it will eventually cause the loss of vision if the affected macula is not treated in time. An accurate retinal screening, therefore, is required to assist the retinal screeners to classify the retinal images effectively. Highly efficient and accurate image processing techniques must thus be used in order to produce an effective screening of diabetic retinopathy. In addition to the proposed diabetic retinopathy detection systems, this thesis will present a new dataset, and will highlight the dataset collection, the expert diagnosis process and the advantages of the new dataset, compared to other public eye fundus images datasets available. The new dataset will be useful to researchers and practitioners working in the retinal imaging area and would widely encourage comparative studies in the field of diabetic retinopathy research. It is envisaged that the proposed decision support system for clinical screening would greatly contribute to and assist the management and the detection of diabetic retinopathy. It is also hoped that the developed automatic detection techniques will assist clinicians to diagnose diabetic retinopathy at an early stage

Detecting Sepsis Using Sepsis-Related Organ Failure Assessment (SOFA) and an Electronic Sepsis Prompt in Intensive Care Unit Adult Patients

Sepsis is an elusive and costly syndrome that is one of the leading causes of death globally. Annually, there are approximately 19 million cases of sepsis that result in more than 5 million deaths. The Agency for Healthcare Research and Quality (AHRQ) ranked sepsis as the most expensive condition ($23.7 billion) for patients treated in hospitals in the United States (U.S.). Nurses are critical in the early identification of sepsis and implementation of therapeutic interventions known as the “sepsis bundle”. Previously, sepsis was described as a systemic, pro-inflammatory response to an infection. Sepsis was defined as two or more systemic inflammatory response syndrome (SIRS) criteria with a suspected infection, severe sepsis was defined as sepsis with organ failure and septic shock was defined as severe sepsis with shock. For several decades SIRS criteria with organ failure criteria have been used to develop measurement systems for detection of sepsis. A recent study comparing SIRS criteria to the sepsis-related organ failure assessment (SOFA) score demonstrated that SOFA had greater prognostic accuracy of mortality in patients with an infection than SIRS. This led to sepsis definition changes in 2016. The term “severe sepsis” was dropped and sepsis was defined as a life-threatening organ dysfunction caused by a dysregulated host response to an infection leading to tissue injury and organ failure. Many clinicians were concerned that this new definition might lead to late detection of sepsis. What was unknown was how well SIRS with organ failure criteria compared with SOFA in detection of sepsis. Many clinicians in the U.S. working in a TeleICU had been using SIRS with organ failure criteria to support early identification of sepsis. Using human factors science concepts, their practice was studied and an electronic sepsis alert (sepsis prompt) was developed. Thus, the overall objective of this dissertation was to conduct a retrospective study using a large U.S. data repository to determine if an electronic prompt, that uses SIRS and organ failure (OF) criteria, can detect sepsis. Another objective of this study was to determine the prognostic accuracy of the SOFA score and the sepsis prompt in discriminating in-hospital mortality among patients with sepsis in the intensive care unit. Among 2,020,489 patients admitted to ICUs associated with a TeleICU from January 1, 2010, to December 31, 2015, at 459 hospitals throughout the U.S., we identified 912,509 (45%) eligible patients at 183 hospitals. We compared the performance of the SOFA score and sepsis prompt criteria in detecting sepsis. Of those in the primary cohort, a secondary cohort was derived based on presence of sepsis resulting 186,870 (20.5%) patients. To assess performances of the SOFA score and the sepsis prompt (a Fuzzy Logic SIRS and OF algorithm) to detect sepsis, we calculated diagnostic performance of an increase in the SOFA score of 2 or more and criteria met for the Fuzzy Logic SIRS and OF algorithm. For predictive validity, training of baseline risk models was performed on training sets with prediction and performance analytics completed on test sets for each cohort for the outcomes of mortality and sepsis. Results were expressed as the fold change in outcome over deciles of baseline risk of death or risk of sepsis, area under the receiver operating characteristic curve (AUROC), and sensitivity, specificity, and negative and positive predictive values. In the primary cohort (912,509) there were 86,219 (9.4%) who did not survive their hospital stay and 186,870 (20.5%) with suspected sepsis of whom 34,617 (18.5%) did not survive hospitalization. The Fuzzy Logic SIRS/OF (crude AUROC 0.67, 99% CI: 0.66-0.67 and adjusted AUROC 0.77, 99% CI: 0.77-0.77) outperformed SOFA (crude AUROC 0.61, 99% CI: 0.61-0.61 and adjusted AUROC 0.74, 99% CI: 0.74-0.74) in discrimination of sepsis in both crude and adjusted AUROC (in-between differences AUROC 0.06; z-value 49.06 and AUROC 0.03; z-value 36.22, respectively). In the primary cohort, Fuzzy Logic SIRS/OF (crude AUROC 0.67, 99% CI: 0.67-0.68 and adjusted AUROC 0.78, 99% CI: 0.77-0.78) outperformed SOFA (crude AUROC 0.64, 99% CI: 0.64-0.64 and adjusted AUROC 0.76, 99% CI: 0.76-0.76) in prognostic accuracy of mortality in both crude and adjusted AUROC (in-between differences AUROC 0.03; z-value 24.68 and AUROC 0.02; z-value 14.74, respectively). In the secondary cohort, Fuzzy Logic SIRS/OF (crude AUROC 0.57, 99% CI: 0.57-0.58 and adjusted AUROC 0.69, 99% CI: 0.68-0.70) outperformed SOFA (crude AUROC 0.56, 99% CI: 0.56-0.56 and adjusted AUROC 0.68, 99% CI: 0.67-0.68) in prognostic accuracy of mortality in both crude and adjusted AUROC (in-between differences AUROC 0.01; z-value 6.86 and AUROC 0.01; z-value 7.53, respectively). The results of this study demonstrated that among adult ICU patients, the predictive validity for sepsis and in-hospital mortality of a complex algorithm based on Fuzzy Logic applied to expanded SIRS criteria with organ failure criteria was better than SOFA for detection of sepsis and for prognostic accuracy of mortality. The findings of this study support the use of a computer-enhanced algorithm that includes a combination of expanded SIRS with organ failure criteria as a tool to assist nurses and healthcare providers in early identification of sepsis

The application of biomedical engineering techniques to the diagnosis and management of tropical diseases: A review

This paper reviews a number of biomedical engineering approaches to help aid in the detection and treatment of tropical diseases such as dengue, malaria, cholera, schistosomiasis, lymphatic filariasis, ebola, leprosy, leishmaniasis, and American trypanosomiasis (Chagas). Many different forms of non-invasive approaches such as ultrasound, echocardiography and electrocardiography, bioelectrical impedance, optical detection, simplified and rapid serological tests such as lab-on-chip and micro-/nano-fluidic platforms and medical support systems such as artificial intelligence clinical support systems are discussed. The paper also reviewed the novel clinical diagnosis and management systems using artificial intelligence and bioelectrical impedance techniques for dengue clinical applications

Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System

تم عمل نظام دعم القرار السريري الآلي (CDSS) كنموذج جديد في الخدمات الطبية. بحيث يتم استخدام CDSSs لمساعدة الأخصائيين (الأطباء) في اتخاذ قراراتهم المحيرة. ولهذا السبب ، تم بناء DSS اعتمادًا على معرفة الأطباء وباستخدام استخراج البيانات لمساعدة خلية الازمة الطبية للسيطرة على جائحة فيروس COVID-19 ، وبشكل عام ، لتحديد الفئة من العدوى وتقديم علاج بروتوكول مناسب حسب أعراض المريض. في البداية لتشخيص المرض تم الاعتماد على ثلاث اعراض اولية هي ( الحمى, التعب والسعال الجاف) لمعرفة الشخص المصاب وعند تحديد أي من هذه الاعراض يتم تقسيم الاشخاص المصابين الى اربعة اصناف حسب مناعة الاشخاص ( اصابة طفيفة , اصابة عالية , اصابة شديدة جدا و طبيعي). وايضا يتم التشخيص باستخدام عاملين هما ( عمر المريض و الامراض المزمنة للمريض مثل السكر ومشاكل القلب وضغط الدم ) ثم يتم تقدير حالة المصاب حيث توجد ستة مستويات للاشخاص المصابين بفيروس كورونا 2019 وتحتاج الى عناية حسب حالة المصاب. عندما يكون الفحص موجب واعتمادا على عمر المريض والامراض المزمنة يتم تحديد في أي مستوى من المستويات الستة يكون المريض حسب الاعراض . وبذلك يتم تحديد درجة حالة المريض من الدرجات الاربع ثم يتم اقتراح اربعة بروتوكولات للعلاج ويتم اختيار الانسب حسب اختيار الاطباء وايضا يوفر النظام معلومات كاملة عن الوقاية وتجنب الوباء واخيرا يتم ارسال ايميل يحتوي جميع المعلومات من مركز السيطرة لللاشخاص المسؤولين . تم اعتماد خوارزمية C4.5  في شجرة اتخاذ القرار لبناء هذا التطبيق.                                                                                                                                              Automated clinical decision support system (CDSS) acts as new paradigm in medical services today. CDSSs are utilized to increment specialists (doctors) in their perplexing decision-making. Along these lines, a reasonable decision support system is built up dependent on doctors' knowledge and data mining derivation framework so as to help with the interest the board in the medical care gracefully to control the Corona Virus Disease (COVID-19) virus pandemic and, generally, to determine the class of infection and to provide a suitable protocol treatment depending on the symptoms of patient. Firstly, it needs to determine the three early symptoms of COVID-19 pandemic criteria (fever, tiredness, dry cough and breathing difficulty) used to diagnose the person being infected by COVID-19 virus or not. Secondly, this approach divides the infected peoples into four classes, based on their immune system risk level (very high degree, high degree, mild degree, and normal), and using two indices of age and current health status like diabetes, heart disorders, or hypertension. Where, these people are graded and expected to comply with their class regulations. There are six important COVID-19 virus infections of different classes that should receive immediate health care to save their lives. When the test is positive, the patient age is considered to choose one of the six classifications depending on the patient symptoms to provide him the suitable care as one of the four types of suggested treatment protocol of COVID-19 virus infection in COVID-19 DSS application. Finally, a report of all information about any classification case of COVID-19 infection is printed where this report includes the status of patient (infection level) and the prevention protocol. Later, the program sends the report to the control centre (medical expert) containing the information. In this paper, it was suggested the use of C4.5 Algorithm for decision tree

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMsThis research work was partially supported by the Sejong University Research Faculty Program (20212023)S

Methodologies of Legacy Clinical Decision Support System -A Review

Information technology playing a prominent role in the field of medical by incorporating the Clinical Decision Support System(CDSS) in their routine practices. CDSS is a computer based interactive program to assist the physician to make the right decision at the right time. Now a day's Clinical decision support system is a dynamic research area in the field of computer, but the lack of the knowledge of the understanding as well as the functioning of the system ,make the adoption slow by the physician and patient. The literature review of this paper will focus on the overview of legacy CDSS, the kind of methodologies and classifier employed to prepare such decision support system using a non-technical approach to the physician and the strategy- makers . This study will provide the scope of understanding the clinical decision support along with the gateway to physician ,policy-makers to develop and deploy the decision support system as a healthcare service to make the quick, agile and right decision. Future direction to handle the uncertainties along with the challenges of clinical decision support system are also enlightened in this study

Data science for health-care: Patient condition recognition

>Magister Scientiae - MScThe emergence of the Internet of Things (IoT) and Artificial Intelligence (AI) have elicited increased interest in many areas of our daily lives. These include health, agriculture, aviation, manufacturing, cities management and many others. In the health sector, portable vital sign monitoring devices are being developed using the IoT technology to collect patients’ vital signs in real-time. The vital sign data acquired by wearable devices is quantitative and machine learning techniques can be applied to find hidden patterns in the dataset and help the medical practitioner with decision making. There are about 30000 diseases known to man and no human being can possibly remember all of them, their relations to other diseases, their symptoms and whether the symptoms exhibited by the patients are early warnings of a fatal disease. In light of this, Medical Decision Support Systems (MDSS) can provide assistance in making these crucial assessments. In most decision support systems factors a ect each other; they can be contradictory, competitive, and complementary. All these factors contribute to the overall decision and have di erent degrees of influence [85]. However, while there is more need for automated processes to improve the health-care sector, most of MDSS and the associated devices are still under clinical trials. This thesis revisits cyber physical health systems (CPHS) with the objective of designing and implementing a data analytics platform that provides patient condition monitoring services in terms of patient prioritisation and disease identification [1]. Di erent machine learning algorithms are investigated by the platform as potential candidate for achieving patient prioritisation. These include multiple linear regression, multiple logistic regression, classification and regression decision trees, single hidden layer neural networks and deep neural networks. Graph theory concepts are used to design and implement disease identification. The data analytics platform analyses data from biomedical sensors and other descriptive data provided by the patients (this can be recent data or historical data) stored in a cloud which can be private local health Information organisation (LHIO) or belonging to a regional health information organisation (RHIO). Users of the data analytics platform consisting of medical practitioners and patients are assumed to interact with the platform through cities’ pharmacies , rural E-Health kiosks end user applications

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 309)

This bibliography lists 136 reports, articles and other documents introduced into the NASA scientific and technical information system in February, 1988

PREDICTION OF SEPSIS DISEASE BY ARTIFICIAL NEURAL NETWORKS

Sepsis is a fatal condition, which affects at least 26 million people in the world every year that is resulted by an infection. For every 100,000 people, sepsis is seen in 149-240 of them and it has a mortality rate of 30%. The presence of infection in the patient is determined in order to diagnose the sepsis disease. Organ dysfunctions associated with an infection is diagnosed as sepsis. With the increased usage of artificial intelligence in the field of medicine, the early prediction and treatment of many diseases are provided with these methods. Considering the learning, reasoning and decision making abilities of artificial neural networks, which are the sub field of artificial intelligence are inferred to be used in predicting early stages of sepsis disease and determining the sepsis level is assessed. In this study, it is aimed to help sepsis diagnosis by using multi-layered artificial neural network.In construction of artificial neural network model, feed forward back propagation network structure and Levenberg-Marquardt training algorithm were used. The input and output variables of the model were the parameters which doctors use to diagnose the sepsis disease and determine the level of sepsis. The proposed method aims to provide an alternative prediction model for the early detection of sepsis disease