A Multi-agent Architecture for Ontology-Based Diagnosis of Mental Disorders

Ivașcu, Todor; Manațe, Bogdan; Negru, Viorel

doi:10.1109/synasc.2015.69

Cited by 14 publications

(5 citation statements)

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“…The symbol () indicates that the research paper uses the checked technology and the opposite is indicated by the symbol (). [15] Chronic diseases [2] Cardiovascular [26] Heart diseases [152] Ubiquitous monitoring system [28] Pain assessment [29] Heart diseases [40] Knees rehabilitation [153] Vital signs gathering and processing [46] Chronic diseases [47] Hypertension [57] Tracking daily activities [61] EXP carried on healthy volunteers [92] Context aware monitoring [77] Diabetes and Diet monitoring [96] Heart diseases [97] Diabetes [90] Diabetes [93] Mental disorder [154] Chronic diseases [155] Monitor patients with depression [131] Cardiovascular diseases [156] Hypertension, hypotension [157] Diabetes [158] Heart diseases [159] Knee arthroplasty [160] Elderly [161] Diabetes [162] Parkinson's disease [106] Fall detection [117] Diabetes [116] Alzheimer's Currently, many health monitoring projects and applications have been initiated that use different architectures. Health monitoring systems are heterogeneous and have been developed for various diseases and disabilities.…”

Section: Study Resultsmentioning

confidence: 99%

“…Ontologies are used to represent attributes, domain terms, concepts and the relations between them. In various systems, ontology used to store and represent knowledge in both human-readable and machine-readable forms that facilitate information retrieval tasks [ 93 , 99 , 100 ]. In [ 15 ], the authors created an ontology knowledge base for assessing diabetes patients.…”

Section: Main Components Of the Rpm Systemmentioning

confidence: 99%

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

et al. 2021

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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 RPMs.

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Section: Study Resultsmentioning

confidence: 99%

Section: Main Components Of the Rpm Systemmentioning

confidence: 99%

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

et al. 2021

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“…Ivascu et al 32 developed an ontology-based multi-agent, as a symptom-sensor-disease system, for patients at risk for cognitive impairment. It provides real-time information to facilitate remote monitoring of patients.…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Fuzzy Rule-Based Inference System: Fuzzy Inference with Semantic Reasoning

Shoaip

El–Sappagh²,

Abuhmed

et al. 2023

Preprint

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The challenge of making flexible, standard, and early medical diagnoses is significant. It has some limitations that it hasn't fully overcome. First, the diagnosis rules established by medical experts or learned from a trained dataset prove static and too general. It leads to decisions that lack adaptive flexibility when finding new circumstances. Secondly, medical terminological interoperability is highly critical. It increases realism and medical progress. Avoid isolated systems and the difficulty of data exchange, analysis, and interpretation. Third, criteria for diagnosis are often heterogeneous and changeable. It includes symptoms, patient history, demographic, treatment, genetics, biochemical, and imaging. Symptoms represent a high-impact indicator for early detection. It is important that we deal with these symptoms differently. It has a great relationship with semantics, varies widely, and has linguistic information. Symptoms cannot be ignored or fixed with variables with specific values. Negatively affects early diagnosis decision-making. Under the circumstances, the diagnosis is made solo on imaging and various medical tests. In this case, although the accuracy of the diagnosis is very high, can these decisions be considered an early diagnosis or prove the condition is deteriorating? Our contribution in this paper is to present a real medical diagnostic system based on semantics, fuzzy, and dynamic decision rules. We attempt to integrate ontology semantics reasoning and fuzzy inference. It promotes fuzzy reasoning and handles knowledge representation problems. In complications and symptoms, ontological semantic reasoning improves the process of evaluating rules in terms of interpretability, dynamism, and intelligence. A real-world case study, ADNI, is presented involving the field of Alzheimer's disease (AD). The proposed system has indicated the possibility of the system to diagnose AD with an accuracy of 97.2%, 95.4%, 94.8%, 93.1%, and 96.3% for AD, LMCI, EMCI, SMC, and CN, respectively.

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“…However, the validation of this ontology had not been carried out. To facilitate remote monitoring of cognitive impairment patients, Ivascu et al [31] developed a multiagent ontology. This system requires automated devices/sensors and data privacy improvements.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Fuzzy Ontology-Based Decision Support System for Alzheimer’s Disease Diagnosis

et al. 2021

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The World Health Organization (WHO) indicates that the proportion of the elderly will soon include nearly a quarter of the world population. Ensuring that health systems are prepared to deal with this phenomenal rate of aging and associated diseases generates many challenges. Among these challenges is facing Alzheimer's Disease (AD) that may occur at some point in the elderly life and may harm societies. AD is considered a neurological, psychological, mental, and health setback. The Clinical Decision Support System (CDSS) can improve patient care and support many medical functions, such as diagnosing diseases that can reduce preventable harm. This research's main objective is to design, implement, and evaluate the Alzheimer's Disease Diagnosis Ontology (ADDO). It is a comprehensive semantic knowledge base toward the development of fuzzy ontology-based CDSS for AD diagnosis. ADDO can serve as a core component of CDSS, which provides representation, annotation, and access to aspects related to AD's study and diagnosis. Toward the management of this objective, ADDO is based on the essentials of the Open Biomedical Ontology (OBO) and follows the Basic Formal Ontology (BFO) and the Ontology for General Medical Sciences (OGMS) principles. ADDO focuses on representing the patient characteristics, complications, drugs, diagnosis examination tests, and key aspects of their periodic visits in a standard way. The possibility of semantic interoperability is taken into account by integrating ADDO and a heterogeneous AD dataset. We used ADNI as a case study to mapping a set of real instances. To manage the medical domain's uncertainty, ADDO is extended to fuzzy ontology to accommodate the medical linguistic variables and enhance diagnosis results' efficiency. ADDO is constructed using Protégé 5.5.0 software and evaluated using the HermiT reasoner and SPARQL semantic queries. ADDO currently includes 7060 concepts, 99 properties, 46274 axioms, and 30708 annotations. As a result, ADDO is consistent and more reliable in managing most AD aspects than other existing AD ontologies.INDEX TERMS Fuzzy ontology, Clinical decision support system, Alzheimer's disease, knowledge based, Mild cognitive impairment, Ontology representation. I. INTRODUCTIONA LZHEIMER'S Disease (AD) is classified as a neurological disorder. It is associated with progressive damage to parts of the brain that are essential in forming memo-ries and carrying out cognitive functions [1]. AD results in the death of neurons cells on a massive scale and significantly shrinking brain tissue [2]. This negatively affects basic body functions, such as vision, swallowing, walking, and

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A Multi-agent Architecture for Ontology-Based Diagnosis of Mental Disorders

Cited by 14 publications

References 32 publications

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

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

A Dynamic Fuzzy Rule-Based Inference System: Fuzzy Inference with Semantic Reasoning

A Comprehensive Fuzzy Ontology-Based Decision Support System for Alzheimer’s Disease Diagnosis

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