Covid‐19 is an acute respiratory infection and presents various clinical features ranging from no symptoms to severe pneumonia and death. Medical expert systems, especially in diagnosis and ...monitoring stages, can give positive consequences in the struggle against Covid‐19. In this study, a rule‐based expert system is designed as a predictive tool in self‐pre‐diagnosis of Covid‐19. The potential users are smartphone users, healthcare experts and government health authorities. The system does not only share the data gathered from the users with experts, but also analyzes the symptom data as a diagnostic assistant to predict possible Covid‐19 risk. To do this, a user needs to fill out a patient examination card that conducts an online Covid‐19 diagnostic test, to receive an unconfirmed online test prediction result and a set of precautionary and supportive action suggestions. The system was tested for 169 positive cases. The results produced by the system were compared with the real PCR test results for the same cases. For patients with certain symptomatic findings, there was no significant difference found between the results of the system and the confirmed test results with PCR test. Furthermore, a set of suitable suggestions produced by the system were compared with the written suggestions of a collaborated health expert. The suggestions deduced and the written suggestions of the health expert were similar and the system suggestions in line with suggestions of the expert. The system can be suitable for diagnosing and monitoring of positive cases in the areas other than clinics and hospitals during the Covid‐19 pandemic. The results of the case studies are promising, and it demonstrates the applicability, effectiveness, and efficiency of the proposed approach in all communities.
Ontology-based obesity management and consultation system.
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•An ontology-based obesity management and consultation system is developed.•It is a decision support system used during ...obesity treatments in childhood and adolescence.•Contributions: (1) obesity tracking ontology, (2) semantic web rules, (3) inference engine.•Eighty from real anonymous pediatric patients are analyzed and discussed as case studies.•Ontology-based inference engine is used to deduce proper suggestions for obesity management.
Obesity is defined as abnormal or excessive fat accumulation that presents a risk to health according to the World Health Organization (WHO). Pediatric or childhood obesity is the most prevalent nutritional disorder among children and adolescents worldwide. In pediatric or childhood obesity, constant monitoring of the pediatric patients by health experts is required to provide efficient obesity management and treatment. Therefore, the patients are examined on a regular basis, the measurements are compared against predefined percentile values and the development of the pediatric patient is examined.
This study discusses the design, implementation, and potential use of an ontology-based obesity management and consultation system which is a decision support system for health experts during treatments of the children and adolescent patients. The system does not only share instant gathered medical data to health experts but also examines the data as a smart medical assistant. The system includes an ontology-based inference engine module, which is a decision support module, and used to infer certain personalized suggestions for patients. Suggestions in four categories emerged as a result: (1) Development Feedback Suggestions, (2) Calorie Intake Suggestions and Physical Activities, (3) Mom Suggestions, and (4) Obesity Treatment Stage Suggestions. The methodologies applied and main technical contributions are discussed in three aspects: (1) Obesity Tracking Ontology, (2) Semantic Web Rule Knowledge base, and (3) Inference Engine Module. In this study, unlike other similar studies, ontology and rule based smart medical assistant which have different functionalities from adults’ obesity management is considered especially for obesity management of children and adolescents. The system also includes intensive pediatric health care expert involvement. Eighty case studies from real anonymous pediatric patients are analyzed and discussed in this experimental study.
The results retrieved from 80 case studies are promising in demonstrating the applicability, effectiveness and efficiency of the proposed approach. The inference engine module of the proposed system can be integrated semantically into intelligent and distributed decision support systems, and the system ontology can be used as a knowledge base in similar systems.
Breast cancer is one of the most dangerous types of cancer in the world among females. In the medical industry, the early detection of a breast abnormality in a mammogram can significantly decrease ...the death rate caused by breast cancer. Therefore, researchers directed their focus and efforts to find better solutions. Whereas researchers earlier used semi-automatic algorithms of machine learning, recently the attention is redirected toward deep learning algorithms that automatically extract features. Therefore, in the research study, two pre-trained Convolutional Neural Network models, VGG16 and ResNet50, have been used and applied on mammogram images to classify their abnormalities in terms of (1) the Benign Calcification, (2) the Malignant Calcification, (3) the Benign Mass, and (4) the Malignant Mass. The mammographic images of the CBIS-DDSM dataset are used. In the training phase, various experiments are performed on ROI images to decide on the best model configuration and fine-tuning depth. The experimental results showed that the VGG16 model provided a remarkable advancement over the ResNet50 model; the accuracy obtained was 80.0% in the first model whereas the second model could classify with a 60.0% accuracy almost randomly. Apart from accuracy, the other performance metrics used in this study are precision, recall, F1-Score and AUC. Our evaluation, based on these performance metrics, shows that accurate detection effect is obtained from the two networks with VGG16 being the most accurate. Finally, a decision support tool is developed which classifies the full mammogram images based on the fine-tuned VGG16 architecture into Benign Calcification, Malignant Calcification, Benign Mass, and Malignant Mass.
Health 3.0 is a health‐related extension of the Web 3.0 concept. It is based on the semantic Web which provides for semantically organizing electronic health records of individuals. Health 3.0 is ...rapidly gaining ground as a new research topic in many academic and industrial disciplines. Due to the recent rapid spread of wearable sensors and smart devices with access to social media, migrating health services from the traditional centre‐based health system to personal health care is inevitable. In this current era of greater personalization, treating patients' health problems according to their profile and medical data gathered is possible using the latest information technologies. Consequently, personalized health recommender systems have gained importance. Empowering the utility of advanced Web technology in personalized health systems is still challenging due to pressing issues, such as lack of low cost and accurate smart medical sensors and wearable devices, existing investment in legacy Web system architecture in health sector, heterogeneity of medical data gathered by myriad health care institutions and isolated health services, and interoperability issues as well as multi‐dimensionality of medical data. By tracing recent developments, this paper offers a systematic review through recent research on semantic Web‐enabled personalized health systems, namely, semanticized personalized health recommender systems with the key enabling technologies, major applications, and successful case studies. Critical questions derived from the research studies were discussed, and main directions of open issues were identified leading to recommendations for future study in the field of personalized health recommender systems.
Diabetes Mellitus (DM) is a significant risk, mostly causing blindness, kidney failure, heart attack, stroke, and lower limb amputation. A Clinical Decision Support System (CDSS) can assist ...healthcare practitioners in their daily effort and can improve the quality of healthcare provided to DM patients and save time.
In this study, a CDSS that can predict DM risk at an early stage has been developed for use by health professionals, general practitioners, hospital clinicians, health educators, and other primary care clinicians. The CDSS infers a set of personalized and suitable supportive treatment suggestions for patients.
Demographic data (e.g., age, gender, habits), body measurements (e.g., weight, height, waist circumference), comorbid conditions (e.g., autoimmune disease, heart failure), and laboratory data (e.g., IFG, IGT, OGTT, HbA1c) were collected from patients during clinical examinations and used to deduce a DM risk score and a set of personalized and suitable suggestions for the patients with the ontology reasoning ability of the tool. In this study, OWL ontology language, SWRL rule language, Java programming, Protégé ontology editor, SWRL API and OWL API tools, which are well known Semantic Web and ontology engineering tools, are used to develop the ontology reasoning module that provides to deduce a set of appropriate suggestions for a patient evaluated.
After our first-round of tests, the consistency of the tool was obtained as 96.5%. At the end of our second-round of tests, the performance was obtained as 100.0% after some necessary rule changes and ontology revisions done. While the developed semantic medical rules can predict only Type 1 and Type 2 DM in adults, the rules do not yet make DM risk assessments and deduce suggestions for pediatric patients.
The results obtained are promising in demonstrating the applicability, effectiveness, and efficiency of the tool. It can ensure that necessary precautions are taken in advance by raising awareness of society against the DM risk.
Iron is a vital mineral for the proper function of hemoglobin which is also a protein needed to transport oxygen in the blood. The lack of iron in human blood causes a range of serious health ...problems including “anemia.” In this article, the COntAneRS (Clinical ONTology-based Iron Deficiency‐ANEmia‐ Recommendation System) is proposed as a clinical decision support system to diagnose iron deficiency and manage its treatment. The applied methodologies and main technical contributions of this study are discussed in four aspects: (1) Iron Deficiency Domain Ontology (IDDOnt), (2) Semantic Web Rule Knowledgebase (SWRL), (3) Inference Engine, and (4) Physician Portal of the system. Experimental studies of the proposed system have been applied on a population of 200 people, consisting of real anemia patients and healthy individuals. First, a decision tree classifier is used to diagnose iron deficiency condition based on the patients’ demographic information and certain medical data, as well as recently measured hemoglobin CBC levels of the patients. To check the effectiveness of the system, the data of 50 anonymous patients randomly selected from 200 patients are entered manually in the IDDOnt and the system is then verified according to the inferencing results. After inferencing step, the recommendations related to appropriate iron drugs, daily consumption dose, drug consumption periods, and additional medical suggestions about drug interactions are provided by the system to the responsible physician through system ontology, SWRL rules, and web services. As a result of experimental studies, our system has provided very good accuracy (99.5%) and robust results in producing patient-suitable suggestions. In addition, the applicability of the system on the cases is discussed as case studies in this paper. The results reported from the applied case studies are promising in demonstrating the applicability, effectiveness, and efficiency of the proposed approach.
In this article, a research project on mobile safe food consumption system (FoodWiki) is discussed that performs its own inferencing rules in its own knowledge base. Currently, the developed rules ...examines the side effects that are causing some health risks: heart disease, diabetes, allergy, and asthma as initial. There are thousands compounds added to the processed food by food producers with numerous effects on the food: to add color, stabilize, texturize, preserve, sweeten, thicken, add flavor, soften, emulsify, and so forth. Those commonly used ingredients or compounds in manufactured foods may have many side effects that cause several health risks such as heart disease, hypertension, cholesterol, asthma, diabetes, allergies, alzheimer etc. according to World Health Organization. Safety in food consumption, especially by patients in these risk groups, has become crucial, given that such health problems are ranked in the top ten health risks around the world. It is needed personal e-health knowledge base systems to help patients take control of their safe food consumption. The systems with advanced semantic knowledge base can provide recommendations of appropriate foods before consumption by individuals. The proposed FoodWiki system is using a concept based search mechanism that performs on thousands food compounds to provide more relevant information.
The purpose of this survey-based study is to make an analysis of search engine users’ behaviors on the Search Engine Results Pages (SERPs) based on the three demographic characteristics gender, age, ...and program studying. In this study, a questionnaire was designed with 12 closed-ended questions. Remaining questions other than the demographic characteristic related ones were about “tab”, “advertisement”, “spelling suggestion”, “related query suggestion”, “instant search suggestion”, “video result”, “image result”, “pagination” and the amount of clicking results. The questionnaire was used and the data collected were analyzed with the descriptive statistics as well as the inferential statistics. 84.2% of the study population was reached. Some of the major results are as follows: Most of each demographic characteristic category (i.e. female, male, under-20, 20-24, above-24, English computer engineering, Turkish computer engineering, software engineering) have rarely or more click for tab, spelling suggestion, related query suggestion, instant search suggestion, video result, image result, and pagination. More than 50.0% of female category click advertisement rarely; however, for the others, 50.0% or more never click advertisement. For every demographic characteristic category, between 78.0% and 85.4% click 10 or fewer results. This study would be the first attempt with its complete content and design. Search engine providers and researchers would gain knowledge to user behaviors about the usage of the SERPs based on the demographic characteristics.
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