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

Ertuğrul, Duygu Çelik; Elçi, Atilla; Bitirim, Yıltan

doi:10.1109/compsac.2017.58

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…Ertugrul, Elçi, and Bitirim () proposed an RS named Intelligent Tracking System: Application to Acute Respiratory Tract Infection (TrackARTI). The TrackARTI system has two main parts: TrackARTI Mobile Application and TrackARTI Inferencing Mechanism.…”

Section: Key Phrs Applications and Case Studies In Literaturementioning

confidence: 99%

A survey on semanticized and personalized health recommender systems

Ertuğrul

Elçi

2019

Expert Systems

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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.

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Section: Key Phrs Applications and Case Studies In Literaturementioning

confidence: 99%

A survey on semanticized and personalized health recommender systems

Ertuğrul

Elçi

2019

Expert Systems

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Enhancing Accuracy in the Detection of Pneumonia in Adult Patients: An Approach by Using Convolutional Neural Networks

Lusiana,

Kurniasari,

Kusumawati

2024

Lecture Notes in Electrical Engineering

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A chatbot based question and answer system for the auxiliary diagnosis of chronic diseases based on large language model

Zhang,

Song

2024

Sci Rep

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In recent years, artificial intelligence has made remarkable strides, improving various aspects of our daily lives. One notable application is in intelligent chatbots that use deep learning models. These systems have shown tremendous promise in the medical sector, enhancing healthcare quality, treatment efficiency, and cost-effectiveness. However, their role in aiding disease diagnosis, particularly chronic conditions, remains underexplored. Addressing this issue, this study employs large language models from the GPT series, in conjunction with deep learning techniques, to design and develop a diagnostic system targeted at chronic diseases. Specifically, performed transfer learning and fine-tuning on the GPT-2 model, enabling it to assist in accurately diagnosing 24 common chronic diseases. To provide a user-friendly interface and seamless interactive experience, we further developed a dialog-based interface, naming it Chat Ella. This system can make precise predictions for chronic diseases based on the symptoms described by users. Experimental results indicate that our model achieved an accuracy rate of 97.50% on the validation set, and an area under the curve (AUC) value reaching 99.91%. Moreover, conducted user satisfaction tests, which revealed that 68.7% of participants approved of Chat Ella, while 45.3% of participants found the system made daily medical consultations more convenient. It can rapidly and accurately assess a patient’s condition based on the symptoms described and provide timely feedback, making it of significant value in the design of medical auxiliary products for household use.

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

Cited by 4 publications

References 12 publications

A survey on semanticized and personalized health recommender systems

A survey on semanticized and personalized health recommender systems

Enhancing Accuracy in the Detection of Pneumonia in Adult Patients: An Approach by Using Convolutional Neural Networks

A chatbot based question and answer system for the auxiliary diagnosis of chronic diseases based on large language model

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