An Application of Fuzzy Prototypes to the Diagnosis and Treatment of Fuzzy Diseases

Romero‐Córdoba, Ruben; Olivas, José Á.; Romero, Francisco P.; Alonso‐Gonzalez, Francisco; Serrano-Guerrero, Jesús

doi:10.1002/int.21836

Cited by 16 publications

(8 citation statements)

References 38 publications

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“…Computing the quality of each author by using only the references stored in Medline is a possible solution under the current constraints; nonetheless, the platform in which our proposal is integrated is more complex, and allows the registration of user profiles to allow users to upload and download documents and to describe their own interests. Consequently, considering the different characteristics of the registered users, the system can provide personalized information, working in the same way as many other recommender systems [42,43] and decision support systems [44] .…”

Section: Discussionmentioning

confidence: 99%

A relevance and quality-based ranking algorithm applied to evidence-based medicine

Serrano-Guerrero¹,

Romero²,

Olivas³

2020

Computer Methods and Programs in Biomedicine

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a b s t r a c tBackground: The amount of information available about millions of different subjects is growing every day. This has led to the birth of new search tools specialized in different domains, because classical information retrieval models have trouble dealing with the special characteristics of some of these domains. Evidence-based Medicine is a case of a complex domain where classical information retrieval models can help search engines retrieve documents by considering the presence or absence of terms, but these must be complemented with other specific strategies which allow retrieving and ranking documents including the best current evidence and methodological quality.Objective: The goal is to present a ranking algorithm able to select the best documents for clinicians considering aspects related to the relevance and the quality of said documents.Methods: In order to assess the effectiveness of this proposal, an experimental methodology has been followed by using Medline as a data set and the Cochrane Library as a gold standard.Results: Applying the evaluation methodology proposed, and after submitting 40 queries on the platform developed, the MAP (Mean Average Precision) obtained was 20.26%.Conclusions: Successful results have been achieved with the experiments, improving on other studies, but under different and even more complex circumstances. with real users [ 5 , 6 ]; nevertheless, there are studies that question whether technology applied to clinical information retrieval is useful and cast doubt on the ability of scientists to search appropriately [7] . Hence, it is necessary to develop algorithms to improve the quality of search results, especially as finding the best studies may suppose a better treatment for patients [8] .An example may be the algorithm proposed in [9] which models quality criteria through fuzzy prototypes in order to filter Personal Health Records (PHRs). This is only one example of documents in Medicine, but there are many other types of documents (review articles, original studies, case reports, etc.) that are important, especially in Evidence-based Medicine (EBM). According to [10] , EBM is "the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients." Therefore, EBM consists of integrating the best research evidence with clinical expertise and patient values. It is based on two main principles, the application of the best available evidence from the scientific method to medical decision making [11] and the assessment of the quality of evidence of the advantages and disadvantages of treatments [12] .

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Section: Discussionmentioning

confidence: 99%

A relevance and quality-based ranking algorithm applied to evidence-based medicine

Serrano-Guerrero¹,

Romero²,

Olivas³

2020

Computer Methods and Programs in Biomedicine

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“…For developing this kind of system, the Mamdani-type FIS are widely used [52,53]. Fuzzy decision support systems are used in different knowledge areas such as Medicine [53,54,55,56,57], Agriculture [58,59], Financial [60,61,62,63,64], Construction [65], Education [66], and more.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems

et al. 2019

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Clinical decision support systems (CDSS) have been designed, implemented, and validated to help clinicians and practitioners for decision-making about diagnosing some diseases. Within the CDSSs, we can find Fuzzy inference systems. For the reasons above, the objective of this study was to design, to implement, and to validate a methodology for developing data-driven Mamdani-type fuzzy clinical decision support systems using clusters and pivot tables. For validating the proposed methodology, we applied our algorithms on five public datasets including Wisconsin, Coimbra breast cancer, wart treatment (Immunotherapy and cryotherapy), and caesarian section, and compared them with other related works (Literature). The results show that the Kappa Statistics and accuracies were close to 1.0% and 100%, respectively for each output variable, which shows better accuracy than some literature results. The proposed framework could be considered as a deep learning technique because it is composed of various processing layers to learn representations of data with multiple levels of abstraction.

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“…Since ‘what is wrong with me’ is always a patient's most frequently asked question, determining the patient's most likely diagnosis on given symptoms is an essential and mostly preliminary work. In recent years, much work has been done about disease detection on symptoms (Huang & Chen, 2007; Malmir et al, 2017; Romero‐Córdoba et al, 2016; Tsipouras et al, 2008; Übeyli, 2010; Uzoka et al, 2011; Zhang et al, 2008). In addition, various clinic decision support systems (CDSS) (Huang & Chen, 2007; Malmir et al, 2017; Uzoka et al, 2011) or automatic diagnoses using electronic health records (EHR) data (Buchan et al, 2017; Byrd et al, 2014; Deleger et al, 2013; Do et al, 2013; Wang et al, 2012; Xu et al, 2011) have been developed.…”

Section: Introductionmentioning

confidence: 99%

Eliciting symptom‐diagnosis knowledge from online medical Q&A

Huang

2021

Expert Systems

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With the objective to automatically detect diseases from symptoms in free-text data, a methodology to extract symptom-diagnosis knowledge from online medical textual data in Q&A domain is proposed in this paper: (1) a term frequency-inverse document frequency and PRECISION method is adopted to retrieve symptom words from unstructured text; (2) a variable precision rough set based genetic algorithm is applied to reduce redundant symptom words, and a rough set based rule is utilized for adding discriminative symptom words assisting to discriminate diseases sharing similar symptoms; (3) by employing fuzzy linguistic variables to express the risk level of disease or severity level of symptoms, a knowledge base with fuzzy belief structure is generated. Using data extracted from a Chinese medical Q&A forum for training and testing, some classical gastrointestinal diseases serve as a case study to evaluate the efficiency of the proposed methodology. Subsequently performance comparisons are made between the proposed methodology and some other classifiers, such as the decision tree algorithms including ID3 and J45, and the Bayesian network classifier.The comparative results demonstrate that the proposed methodology outperforms the decision tree algorithms and the Bayesian network classifier.

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An Application of Fuzzy Prototypes to the Diagnosis and Treatment of Fuzzy Diseases

Cited by 16 publications

References 38 publications

A relevance and quality-based ranking algorithm applied to evidence-based medicine

A relevance and quality-based ranking algorithm applied to evidence-based medicine

Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems

Eliciting symptom‐diagnosis knowledge from online medical Q&A

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