Medical information retrieval systems for e-Health care records using fuzzy based machine learning model

Prabhu, L. Arokia Jesu; Sengan, Sudhakar; Kamalam, G. K.; Vellingiri, J.; Gopal, Jagadeesh; Velayutham, Priya; Subramaniyaswamy,

doi:10.1016/j.micpro.2020.103344

Cited by 17 publications

(5 citation statements)

References 14 publications

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“…A critical avenue for future research involves actively addressing the inherent limitations of FCMs, particularly non-dynamism, without resorting to mirroring neural network architectures [106][107][108][109][110][111]. While FCMs provide a structured approach to modelling uncertainties, their static nature poses challenges in capturing dynamic real-world systems.…”

Section: Future Directionsmentioning

confidence: 99%

Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study

Apostolopoulos,

Papandrianos,

Papathanasiou

et al. 2024

Bioengineering

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Fuzzy Cognitive Maps (FCMs) have become an invaluable tool for healthcare providers because they can capture intricate associations among variables and generate precise predictions. FCMs have demonstrated their utility in diverse medical applications, from disease diagnosis to treatment planning and prognosis prediction. Their ability to model complex relationships between symptoms, biomarkers, risk factors, and treatments has enabled healthcare providers to make informed decisions, leading to better patient outcomes. This review article provides a thorough synopsis of using FCMs within the medical domain. A systematic examination of pertinent literature spanning the last two decades forms the basis of this overview, specifically delineating the diverse applications of FCMs in medical realms, including decision-making, diagnosis, prognosis, treatment optimisation, risk assessment, and pharmacovigilance. The limitations inherent in FCMs are also scrutinised, and avenues for potential future research and application are explored.

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Section: Future Directionsmentioning

confidence: 99%

Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study

Apostolopoulos,

Papandrianos,

Papathanasiou

et al. 2024

Bioengineering

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“…Menear et al [19], Gopal et al [14], and Kopel [20] carry out a conceptual framework that gives a vision of capabilities and technologies to use in our solution as the capabilities to build health systems [19] [14], and acceptable conditions for them [20]. Sengan et al [21] and Puri et al [22] present frameworks explaining the technologies to be used in their contribution, such as the clinical nodes for the use of smart contracts [22] and the flow for the operation of a prediction system using machine learning [21].…”

Section: Architectures and Frameworkmentioning

confidence: 99%

“…Fernández-Gutiérrez et al [23] used big data analytics and data mining techniques to identify changes in sick people's electronic medical records in primary care. Sengan et al [21], Chen et al [27], and Fernández-Gutiérrez et al [23] use the DT algorithm for disease prediction to improve health services, whereas Yu et al [28] and Kumar et al [29] use the RF algorithm to manage treatments and early detection of diseases. On the other hand, blockchain [30] and the Internet of Things (IoT) [14] [28] [29] have been used in some studies.…”

Section: Technologies Applied In Health Servicesmentioning

confidence: 99%

A Learning Health-Care System for Improving Renal Health Services in Peru Using Data Analytics

Mita,

Castillo,

Castillo-Sequera

et al. 2023

Int. J. Onl. Eng.

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The health sector around the world faces the continuous challenge of improving the services provided to patients. Therefore, digital transformation in health services plays a key role in integrating new technologies such as artificial intelligence. However, the health system in Peru has not yet taken the big step towards digitising its services, currently ranking 71st according to the World Health Organisation (WHO). This article proposes a learning health system for the management and monitoring of private health services in Peru based on the three key components of intelligent health care: (1) a health data platform (HDP); (2) intelligent technologies (IT); and (3) an intelligent health care suite (HIS). The solution consists of four layers: (1) data source, (2) data warehousing, (3) data analytics, and (4) visualization. In layer 1, all data sources are selected to create a database. The proposed learning health system is built, and the data storage is executed through the extract, transform and load (ETL) process in layer 2. In layer 3, the Kaggle dataset and the decision tree (DT) and random forest (RF) algorithms are used to predict the diagnosis of disease, resulting in the RF algorithm having the best performance. Finally, in layer 4, the intelligent health-care suite dashboards and interfaces are designed. The proposed system was applied in a clinic focused on preventing chronic kidney disease. A total of 100 patients and six kidney health experts participated. The results proved that the diagnosis of chronic kidney disease by the learning health system had a low error rate in positive diagnoses (err = 1.12%). Additionally, it was demonstrated that experts were “satisfied” with the dashboards and interfaces of the intelligent health-care suite as well as the quality of the learning health system.

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“…Retrieving relevant and personalized information poses a challenge for Information Retrieval Systems (IRS) that need to satisfy a user's need presented in the form of a question and specified through a set of keywords by analyzing a collection of documents with variable volume and format [3]. The Information Retrieval (IR) process has been improved by developing several models [4][5][6][7][8] focused on solving problems related to:…”

Section: Introductionmentioning

confidence: 99%

Modelo para la recuperación de información con expansión de consulta y perfil de preferencia de los usuarios

Viltres-Sala,

Estrada-Sentí,

Febles-Rodríguez

et al. 2023

Rev. Fac. Ing.

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Comprender la intención de búsqueda del usuario permite identificar y extraer los resultados de búsqueda más relevantes y personalizados de la información disponible según sus necesidades. En el presente artículo se plantea un algoritmo para la recuperación de información relevante que combina las preferencias del perfil del usuario y la expansión de consulta para obtener resultados de búsqueda relevantes y personalizados. El proceso de recuperación de información se valida mediante las métricas de Precision, Recall y Mean Average Precision (MAP) aplicadas a un conjunto de datos que contiene los documentos estandarizados y los perfiles de preferencias. Los resultados permitieron demostrar que el algoritmo mejora el proceso de recuperación de información al arrojar documentos con mejor calidad y relevancia según las necesidades de los usuarios.

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Medical information retrieval systems for e-Health care records using fuzzy based machine learning model

Cited by 17 publications

References 14 publications

Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study

Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study

A Learning Health-Care System for Improving Renal Health Services in Peru Using Data Analytics

Modelo para la recuperación de información con expansión de consulta y perfil de preferencia de los usuarios

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