Fuzzy ontology-based personalized recommendation for internet of medical things with linked open data

Clinical Information Retrieval: A Literature Review

Clinical information retrieval (IR) plays a vital role in modern healthcare by facilitating efficient access and analysis of medical literature for clinicians and researchers. This scoping review aims to offer a comprehensive overview of the current state of clinical IR research and identify gaps and potential opportunities for future studies in this field. The main objective was to assess and analyze the existing literature on clinical IR, focusing on the methods, techniques, and tools employed for effective retrieval and analysis of medical information. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted an extensive search across databases such as Ovid Embase, Ovid Medline, Scopus, ACM Digital Library, IEEE Xplore, and Web of Science, covering publications from January 1, 2010, to January 4, 2023. The rigorous screening process led to the inclusion of 184 papers in our review. Our findings provide a detailed analysis of the clinical IR research landscape, covering aspects like publication trends, data sources, methodologies, evaluation metrics, and applications. The review identifies key research gaps in clinical IR methods such as indexing, ranking, and query expansion, offering insights and opportunities for future studies in clinical IR, thus serving as a guiding framework for upcoming research efforts in this rapidly evolving field. The study also underscores an imperative for innovative research on advanced clinical IR systems capable of fast semantic vector search and adoption of neural IR techniques for effective retrieval of information from unstructured electronic health records (EHRs).

Supplementary Information

The online version contains supplementary material available at 10.1007/s41666-024-00159-4.

A Systematic Review on Food Recommender Systems for Diabetic Patients

Recommender systems are currently a relevant tool for facilitating access for online users, to information items in search spaces overloaded with possible options. With this goal in mind, they have been used in diverse domains such as e-commerce, e-learning, e-tourism, e-health, etc. Specifically, in the case of the e-health scenario, the computer science community has been focused on building recommender systems tools for supporting personalized nutrition by delivering user-tailored foods and menu recommendations, incorporating the health-aware dimension to a larger or lesser extent. However, it has been also identified the lack of a comprehensive analysis of the recent advances specifically focused on food recommendations for the domain of diabetic patients. This topic is particularly relevant, considering that in 2021 it was estimated that 537 million adults were living with diabetes, being unhealthy diets a major risk factor that leads to such an issue. This paper is centered on presenting a survey of food recommender systems for diabetic patients, supported by the PRISMA 2020 framework, and focused on characterizing the strengths and weaknesses of the research developed in this direction. The paper also introduces future directions that can be followed in the next future, for guaranteeing progress in this necessary research area.

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

In recent times, artificial intelligence (AI) methods have been applied in document and content management to make decisions and improve the organization's functionalities. However, the lack of semantics and restricted metadata hinders the current document management technique from achieving a better outcome. E-Government activities demand a sophisticated approach to handle a large corpus of data and produce valuable insights. There is a lack of methods to manage and retrieve bilingual (Arabic and English) documents. Therefore, the study aims to develop an ontology-based AI framework for managing documents. A testbed is employed to simulate the existing and proposed framework for the performance evaluation. Initially, a data extraction methodology is utilized to extract Arabic and English content from 77 documents. Researchers developed a bilingual dictionary to teach the proposed information retrieval technique. A classifier based on the Naïve Bayes approach is designed to identify the documents' relations. Finally, a ranking approach based on link analysis is used for ranking the documents according to the users' queries. The benchmark evaluation metrics are applied to measure the performance of the proposed ontological framework. The findings suggest that the proposed framework offers supreme results and outperforms the existing framework.

Fuzzy based approach for smart health monitoring systems using IoT devices

IoT systems base devices are considered an excellent research domain owning to its expertise and applications in wide range of areas. IoT in health care domain is gaining attention due to its better access to the doctor and paramedical staff as well as sensor based studies which results in less man to man interacting and less fault in the data. The health care provider can easily access the vitals and various other medical parameters by even staying miles away from the patient. However, large amount of data transfer over various communication mediums results in more data traffic. This data transfer will require more power which will be utilized to transfer the data. To reduce this data traffic issues, an efficient method is used in this work in which only the data that is predominantly important to be send to the health care provider is send via the communication medium. Rule based fuzzy logic tool is used in this work for an elder patient having cardiac issues. Blood sugar (After eating), Blood pressues (systolic), Blood pressure (Diastolic) and cholesterol level are taken as the parameter that are examined for the patient and the medical treatment required is calculated. The rules are set on the basis of real time data and human knowledge. The results from the fuzzy logic interference shows that the health care provider will be alarmed using communication medium only when active or emergency medical treatment of the patient is required. A comparative study between the power utilized in normal data driven method and fuzzy method shows that the fuzzy method utilize 8 times less power than the normal method. The simulated and MAMDANI model calculated values shows less than 1% error which shows the accuracy of the work in health care domain.

A semantic-based approach for hepatitis C virus prediction and diagnosis using a fuzzy ontology and a fuzzy Bayesian network

Medical decisions, especially when diagnosing Hepatitis C, are challenging to make as they often have to be based on uncertain and fuzzy information. In most cases, that puts doctors in complex yet uncertain decision-making situations. Therefore, it would be more suitable for doctors to use a semantically intelligent system that mimics the doctor’s thinking and enables fast Hepatitis C diagnosis. Fuzzy ontologies have been used to remedy the shortcomings of classical ontologies by using fuzzy logic, which allows dealing with fuzzy knowledge in ontologies. Moreover, Fuzzy Bayesian networks are well-known and widely used to represent and analyze uncertain medical data. This paper presents a system that combines fuzzy ontologies and Bayesian networks to diagnose Hepatitis C. The system uses a fuzzy ontology to represent sequences of uncertain and fuzzy data about patients and some features relevant to Hepatitis C diagnosis, enabling more reusable and interpretable datasets. In addition, we propose a novel semantic diagnosis process based on a fuzzy Bayesian network as an inference engine. We conducted an experimental study on 615 real cases to validate the proposed system. The experimentation allowed us to compare the results of existing machine learning algorithms for the Hepatitis C diagnosis with the results of our proposed system. Our solution shows promising results and proves effective for fast medical assistance.

Tourist Recommender Systems Based on Emotion Recognition—A Scientometric Review

Recommendation systems have overcome the overload of irrelevant information by considering users’ preferences and emotional states in the fields of tourism, health, e-commerce, and entertainment. This article reviews the principal recommendation approach documents found in scientific databases (Elsevier’s Scopus and Clarivate Web of Science) through a scientometric analysis in ScientoPy. Research publications related to the recommenders of emotion-based tourism cover the last two decades. The review highlights the collection, processing, and feature extraction of data from sensors and wearables to detect emotions. The study proposes the thematic categories of recommendation systems, emotion recognition, wearable technology, and machine learning. This paper also presents the evolution, trend analysis, theoretical background, and algorithmic approaches used to implement recommenders. Finally, the discussion section provides guidelines for designing emotion-sensitive tourist recommenders.

A Fuzzy-Ontology Based Diabetes Monitoring System Using Internet of Things

The majority of the Internet-of-things (IoT)-based health monitoring systems adopt ontologies to represent and interoperate the huge quantity of data collected. Classical ontologies cannot appropriately treat imprecise and ambiguous knowledge. The integration of Fuzzy logic theory with ontology can effectively resolve knowledge problems with uncertainty. It considerably raises the accuracy and the precision of healthcare decisions. This paper presents a fuzzy-ontology based system using the internet of things and aims to ensure continues monitoring of diabetic patients. It mainly describes the ontology-based model and the semantic fuzzy decision-making mechanism. The system is evaluated using semantic querying. The results indicate its feasibility for effective remote continuous monitoring for diabetes.