Duygu Çelik Ertugrul

FoodWiki: a Mobile App Examines Side Effects of Food Additives Via Semantic Web

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.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.

A Search Service for Food Consumption Mobile Applications via Hadoop and MapReduce Technology

Many mobile applications on safe food consumption and e-health have been developed recently. Health conscious users highly regard such applications for safe food consumption, especially for avoiding offending foods and additives. However, there is the lack of a comprehensive database containing structured or unstructured data to support such applications. In this paper we propose the MSS, a healthy food consumption search service for mobile applications utilizing Hadoop and MapReduce (MR). The MSS may work as a process behind a mobile application to provide a search service for information on food and food additives. MSS works by the same logic as a search engine (SE), it crawls over Web sources cataloguing relevant information for eventual use in responding to queries from mobile applications. MSS design and development are highlighted in this paper through its system architecture, query understanding, its use of the Hadoop/MapReduce Environment, and action scripts. A case study helps displaying the virtues of MSS.

TrackARTI: Mobile Health Tracking System for Pediatric Patients with Acute Respiratory Tract Infection

Bu calismada, 0-6 yas grubu cocuk hastalar icin, Akut Solunum Yolu Enfeksiyonlari (ASYE) hastaliklarina yonelik dusunulmus bir sistem olan Uzaktan Medikal Takip Mobil Sistemini (TrackARTI) tanitilmistir. Bu sistemin temel amaci, ASYE donemlerindeki medikal vakalara ait gercek verileri cesitli ortamlarda anlik olarak toplamak, istenildiginde goruntulemek, makinelerin anlayacagi sekilde yapisal formda saklamak, sistemin kendi cikarim mekanizmasi sayesinde yorumlayip kisiye ozgu medikal onermeler yapmak veya cikarsanmis ilgili yeni verileri sunmaktir. Sistemin temel kullanici grubu pediatri uzmanlari ve ailelerdir. Bu sistemin ilk paydaslari ebeveynler, cocuk hastalar, pediatri uzmanlari ve ilgili saglik personelidir. Onerilen TrackARTI sistemi akilli M-Saglik sistemlerine bir ornek teskil etmektedir. Sistemin, TrackARTI Yapay Zekâ Cikarim Mekanizmasi ve TrackARTI Mobil Uygulamasi olmak uzere iki temel ciktisi bulunmaktadir. Ayrica, Yapay Zekâ Cikarim Mekanizmasi kendi icinde iki temel calismayi icermektedir; 1) Medikal Goruntu Isleme tabanli cikarimlar ve 2) Anlamsal Veb Tabanli Cikarim Kurallari yoluyla yeni veriler ve onermelerin cikarsamasi.This study introduces the Remote Medical Tracking Mobile System (TrackARTI) which guides parents or health expert users in the treatment of Acute Respiratory Tract Infections (ASYE) diseases for children aged 0-6 years. The main purpose of this system is to gather actual data belonging to the medical cases of ASYE period in various environments instantly, to display them when requested, to store them in structural form in order to understand by machines, to deduce the data through its retrieval mechanism and to make personalized medical suggestions or to present relevant new data inferred. The main user group of the system is pediatric specialists and parents. The main stakeholders of this system are parents, child patients, pediatric specialists and health personnel. The proposed TrackARTI system is an example of intelligent M-Health systems. The system has two basic outputs: TrackARTI Artificial Intelligence based Inferencing Mechanism and TrackARTI Mobile Application. In addition, the Artificial Intelligence based Inferencing Mechanism of the system involves two main activities in itself; 1) Medical Image Processing based Inferencing and 2) Semantic Web Based Inferencing of new medical data and suggestions through its Inference Rules.

An Intelligent Tracking System: Application to Acute Respiratory Tract Infection (TrackARTI)

This article proposes an Intelligent Tracking System for Acute Respiratory Tract Infection (TrackARTI) via a smart mobile for monitoring disease term of 0-6 age group child patients remotely (e.g. home, clinics). It is possible to maximize the quality of life of the child patients and decrease parental anxiety by keeping the child under control during monitoring stage and achieve a proper distant diagnose by the patients clinician. This is possible with the designs of intelligent M-Health systems that can be used for diagnosing and monitoring the child patients away from hospitals by presenting the instant medical data to their registered doctors. Intelligent M-Health systems require strong knowledge management technology and ease of extension to provide information from additional medical tools. With the contribution of intelligent M-Health systems, it is possible to infer new facts from the certain gathered medical data during examination from child patients. This article mentions an intelligent and easy medical data gathering system that can be used by pediatricians or parents any time. In addition, the system has its own inferencing mechanism that involves two main steps, inferencing on image processing and Semantic Web rule knowledge base.

Message from ESAS 2016 Workshop Organizers

Welcome to the 11 IEEE International Workshop on Engineering Semantic Agents – E-Health Systems and Semantic Web (ESAS 2016), this time taking place in the Atlanta, Georgia, USA, 10-14 June 2016 in conjunction with Annual IEEE International Computer Software and Applications Conferences (COMPSAC). The ESAS Workshop Series has been held since 2006. The focus of ESAS Workshops Series is on concepts, foundations and applications of semantic agent systems and intends to bring forward better practices of engineering them.

Fetal Heart Rate Monitoring System (FHRMS)

This article discusses a new approach to Fetal Heart Rate Monitoring System (FHRMS) via a mobile integrated Doppler device (mDoppler) for monitoring Fetal Heart Rate (FHR) remotely (i.e. home). The aim of the system is to provide ease of FHR monitoring and computing current fetus risk conditions especially for high-risk pregnancy cases via a mobile integrated Doppler device. FHRMS has its own inferring mechanism that provides to analyze current fetus conditions by considering FHR values through connecting a hand held mDoppler device to labours abdomen during observing period at home. The instantly-gathered FHR values as output signal of the mDoppler during observation period are displayed on the labours mobile device monitor and also input data set to the FHRMS. The inferred current fetus conditions are coded as Green, Yellow, and Red status by the FHRMS signifying the Normal, Atypical/Warning, and Abnormal/Alarm conditions of the fetus respectively. Thus FHRMS informs the responsible parents, physician and the ambulance service if alarm status is observed. In this research, FHRMS considers 10 sets of FHR instance values for the last 10 gestation weeks (started at 30th up to 40th weeks) from a subjects labours that are traced as a case study to track the inferencing algorithms of the FHRMS. The FHRMS tracing mechanism is developed from visual analysis algorithms of (Electro Fetal Monitoring) EFM task in literature.