Article

Computer aided fuzzy medical diagnosis.

Centre for Computational Intelligence, Department of Computer Science, De Montfort University, The Gateway, Leicester LE1 9BH, UK
Inf. Sci 01/2004; 162:81-104. DOI: 10.1016/j.ins.2004.03.003
Source: DBLP

ABSTRACT This paper describes a fuzzy approach to computer aided medical diagnosis in a clinical context. It introduces a formal view of diagnosis in clinical settings and shows the relevance and possible uses of fuzzy cognitive maps and fuzzy logic. A constraint satisfaction method is introduced which uses the temporal uncertainty in symptom durations that may occur with particular diseases. Together with fuzzy symptom descriptions, the method results in an estimate of the stage of a disease if the temporal constraints of the disease in relation to the occurrence of the symptoms are satisfied. The approach is evaluated through simulation experiments showing the effects of symptom ordering, temporal uncertainty and symptom strengths on the diagnosis efficiency. The method is effective and can be developed further using second order (Type 2) fuzzy logic to better represent uncertainty in the clinical context thus improving differential diagnosis accuracy.

0 Bookmarks
 · 
91 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Fuzzy Cognitive Maps (FCMs) are a causal modelling technique. FCM models contain nodes (representing the concepts to be modelled) and directed weighted edges (representing the causal relations between the concepts). Data-driven FCM learning algorithms are an objective approach with the potential to discover the causal relations that are unknown to human experts. Learning FCM from data can be a difficult problem because the size of the solution space grows quadratically with the number of nodes in the FCM models. A data-driven learning algorithm based on Ant Colony Optimization (ACO) is proposed to develop Fuzzy Cognitive Maps (FCMs). The FCM models can be isomorphically represented as weight vectors. The objective function is to minimize the difference between the estimated response of the FCM model and the target response observed from the to-be-modelled system. An ACO algorithm with heuristic information is proposed to find the best FCM model. The performance of the ACO algorithm was tested on both randomly generated data and DREAM4 project data (publicly available in-silico gene expression data). The experiment results show that the ACO algorithm is able to learn FCMs with at least 40 nodes.
    2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD); 07/2013
  • [Show abstract] [Hide abstract]
    ABSTRACT: Advanced engineering, communication and information technologies combined with medical and clinical knowledge enable the possibility of remote, wireless, continuous physiological monitoring. These technologies facilitate the implementation of patient monitoring and diagnostic systems virtually anywhere: home, hospital and outdoors (on the move). Physiological parameters are considered as critical information to assess health condition and the type of possible illness of patients. In this work, vital signs are collected using wireless medical devices and fed to a computerised decision support system consist of a diagnostic model. The proposed vital signs monitoring system is able to help clinicians by illustrating the trace of critical physiological parameters, generating early warning/alerts and indicating any significant changes to the data. Moreover, it can assist patients to monitor their health status and communicate their concerns with the healthcare providers. The system was validated with different set of collected data from 20 hospitalised older adults and achieved an accuracy of 95.83%, sensitivity of 100%, specificity of 93.15%, and predictability of 90.38% in compare with a clinician assessment for tachycardia, hypertension, hypotension, hypoxemia and hypothermia.
    2014 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI); 06/2014
  • [Show abstract] [Hide abstract]
    ABSTRACT: The problem of solving type-2 fuzzy relation equations is investigated. In order to apply semi-tensor product of matrices, a new matrix analysis method and tool, to solve type-2 fuzzy relation equations, a type-2 fuzzy relation is decomposed into two parts as principal sub-matrices and secondary sub-matrices; an r-ary symmetrical-valued type-2 fuzzy relation model and its corresponding symmetrical-valued type-2 fuzzy relation equation model are established. Then, two algorithms are developed for solving type-2 fuzzy relation equations, one of which gives a theoretical description for general type-2 fuzzy relation equations; the other one can find all the solutions to the symmetrical-valued ones. The results can improve designing type-2 fuzzy controllers, because it provides knowledge to search the optimal solutions or to find the reason if there is no solution. Finally some numerical examples verify the correctness of the results/algorithms.
    Control Theory and Technology. 05/2014; 12(2):173-186.