An evolutionary-fuzzy DSS for assessing health status in multiple sclerosis disease

Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system that affects millions of people worldwide. The disease course varies greatly across individuals and many disease-modifying treatments with different safety and efficacy profiles have been developed recently. Prognostic models evaluated and shown to be valid in different settings have the potential to support people with MS and their physicians during the decision-making process for treatment or disease/life management, allow stratified and more precise interpretation of interventional trials, and provide insights into disease mechanisms. Many researchers have turned to prognostic models to help predict clinical outcomes in people with MS; however, to our knowledge, no widely accepted prognostic model for MS is being used in clinical practice yet.

OBJECTIVES

To identify and summarise multivariable prognostic models, and their validation studies for quantifying the risk of clinical disease progression, worsening, and activity in adults with MS.

SEARCH METHODS

We searched MEDLINE, Embase, and the Cochrane Database of Systematic Reviews from January 1996 until July 2021. We also screened the reference lists of included studies and relevant reviews, and references citing the included studies.

SELECTION CRITERIA

We included all statistically developed multivariable prognostic models aiming to predict clinical disease progression, worsening, and activity, as measured by disability, relapse, conversion to definite MS, conversion to progressive MS, or a composite of these in adult individuals with MS. We also included any studies evaluating the performance of (i.e. validating) these models. There were no restrictions based on language, data source, timing of prognostication, or timing of outcome.

DATA COLLECTION AND ANALYSIS

Pairs of review authors independently screened titles/abstracts and full texts, extracted data using a piloted form based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), assessed risk of bias using the Prediction Model Risk Of Bias Assessment Tool (PROBAST), and assessed reporting deficiencies based on the checklist items in Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). The characteristics of the included models and their validations are described narratively. We planned to meta-analyse the discrimination and calibration of models with at least three external validations outside the model development study but no model met this criterion. We summarised between-study heterogeneity narratively but again could not perform the planned meta-regression.

MAIN RESULTS

We included 57 studies, from which we identified 75 model developments, 15 external validations corresponding to only 12 (16%) of the models, and six author-reported validations. Only two models were externally validated multiple times. None of the identified external validations were performed by researchers independent of those that developed the model. The outcome was related to disease progression in 39 (41%), relapses in 8 (8%), conversion to definite MS in 17 (18%), and conversion to progressive MS in 27 (28%) of the 96 models or validations. The disease and treatment-related characteristics of included participants, and definitions of considered predictors and outcome, were highly heterogeneous amongst the studies. Based on the publication year, we observed an increase in the percent of participants on treatment, diversification of the diagnostic criteria used, an increase in consideration of biomarkers or treatment as predictors, and increased use of machine learning methods over time. Usability and reproducibility All identified models contained at least one predictor requiring the skills of a medical specialist for measurement or assessment. Most of the models (44; 59%) contained predictors that require specialist equipment likely to be absent from primary care or standard hospital settings. Over half (52%) of the developed models were not accompanied by model coefficients, tools, or instructions, which hinders their application, independent validation or reproduction. The data used in model developments were made publicly available or reported to be available on request only in a few studies (two and six, respectively). Risk of bias We rated all but one of the model developments or validations as having high overall risk of bias. The main reason for this was the statistical methods used for the development or evaluation of prognostic models; we rated all but two of the included model developments or validations as having high risk of bias in the analysis domain. None of the model developments that were externally validated or these models' external validations had low risk of bias. There were concerns related to applicability of the models to our research question in over one-third (38%) of the models or their validations. Reporting deficiencies Reporting was poor overall and there was no observable increase in the quality of reporting over time. The items that were unclearly reported or not reported at all for most of the included models or validations were related to sample size justification, blinding of outcome assessors, details of the full model or how to obtain predictions from it, amount of missing data, and treatments received by the participants. Reporting of preferred model performance measures of discrimination and calibration was suboptimal.

AUTHORS' CONCLUSIONS

The current evidence is not sufficient for recommending the use of any of the published prognostic prediction models for people with MS in clinical routine today due to lack of independent external validations. The MS prognostic research community should adhere to the current reporting and methodological guidelines and conduct many more state-of-the-art external validation studies for the existing or newly developed models.

Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review

Background

Artificial intelligence (AI) has developed rapidly, and its application extends to clinical decision support system (CDSS) for improving healthcare quality. However, the interpretability of AI-driven CDSS poses significant challenges to widespread application.

Objective

This study is a review of the knowledge-based and data-based CDSS literature regarding interpretability in health care. It highlights the relevance of interpretability for CDSS and the area for improvement from technological and medical perspectives.

Methods

A systematic search was conducted on the interpretability-related literature published from 2011 to 2020 and indexed in the five databases: Web of Science, PubMed, ScienceDirect, Cochrane, and Scopus. Journal articles that focus on the interpretability of CDSS were included for analysis. Experienced researchers also participated in manually reviewing the selected articles for inclusion/exclusion and categorization.

Results

Based on the inclusion and exclusion criteria, 20 articles from 16 journals were finally selected for this review. Interpretability, which means a transparent structure of the model, a clear relationship between input and output, and explainability of artificial intelligence algorithms, is essential for CDSS application in the healthcare setting. Methods for improving the interpretability of CDSS include ante-hoc methods such as fuzzy logic, decision rules, logistic regression, decision trees for knowledge-based AI, and white box models, post hoc methods such as feature importance, sensitivity analysis, visualization, and activation maximization for black box models. A number of factors, such as data type, biomarkers, human-AI interaction, needs of clinicians, and patients, can affect the interpretability of CDSS.

Conclusions

The review explores the meaning of the interpretability of CDSS and summarizes the current methods for improving interpretability from technological and medical perspectives. The results contribute to the understanding of the interpretability of CDSS based on AI in health care. Future studies should focus on establishing formalism for defining interpretability, identifying the properties of interpretability, and developing an appropriate and objective metric for interpretability; in addition, the user's demand for interpretability and how to express and provide explanations are also the directions for future research.

A Fuzzy Expert System for Early Diagnosis of Multiple Sclerosis

BACKGROUND

Artificial intelligence plays an important role in medicine. Specially, expert systems can be designed for diagnosis of disease.

OBJECTIVE

Artificial intelligence can be used for diagnosis of disease. This study proposes an expert system for diagnosis of Multiple Sclerosis based on clinical symptoms and demographic characteristics. Specially, it recommends patients to refer to a specialist for further investigation.

MATERIAL AND METHODS

In this empirical study, some symptoms of Multiple Sclerosis are mapped to fuzzy sets. Moreover, several rules are defined for prediction of Multiple Sclerosis. The fuzzy sets and rules form the knowledge base of the expert system. Patients enter their symptoms and demographic information via a user interface and Mamdani method is used in inference engine to produce the appropriate recommendation.

RESULTS

The precision, recall, and F-measure are used as criteria to analyze the efficiency of the expert system. The results show that the designed expert system can recommend patients for further investigation as effective as specialists. Specially, while the proposed expert system recommended referring to a doctor for some healthy users, most of the MS patients are diagnosed.

CONCLUSION

The proposed expert system in this study can analyze the symptoms of patients to predict the Multiple Sclerosis disease. Therefore, it can investigate initial status of patients in a rapid and cost-effective manner. Moreover, this system can be applied in situations and places, which human experts are unavailable.

Model-Driven Decision Making in Multiple Sclerosis Research: Existing Works and Latest Trends

Multiple sclerosis (MS) is a neurological disorder that strikes the central nervous system. Due to the complexity of this disease, healthcare sectors are increasingly in need of shared clinical decision-making tools to provide practitioners with insightful knowledge and information about MS. These tools ought to be comprehensible by both technical and non-technical healthcare audiences. To aid this cause, this literature review analyzes the state-of-the-art decision support systems (DSSs) in MS research with a special focus on model-driven decision-making processes. The review clusters common methodologies used to support the decision-making process in classifying, diagnosing, predicting, and treating MS. This work observes that the majority of the investigated DSSs rely on knowledge-based and machine learning (ML) approaches, so the utilization of ontology and ML in the MS domain is observed to extend the scope of this review. Finally, this review summarizes the state-of-the-art DSSs, discusses the methods that have commonalities, and addresses the future work of applying DSS technologies in the MS field.

Multiple sclerosis (MS) is a disorder that strikes the central nervous system of the human body. This article reviews state-of-the-art decision support systems (DSSs) in MS research, as recent studies have highlighted the importance of DSSs in the medical realm. However, the utilization of decision support systems for MS remains an open challenge. A special focus in this article is given to model-driven DSSs, which uses knowledge representation to simplify the complex process for decision makers. We find that most investigated studies use knowledge-based and machine learning approaches. Based on the literature review, we suggest some future work of applying DSSs in the MS domain. Potential future directions should focus on applying DSS technologies to understand the MS patterns, etiology, effects on the quality-of-life, and correlations with other disorders.

Design of an integrated model for diagnosis and classification of pediatric acute leukemia using machine learning

Applying artificial intelligence techniques for diagnosing diseases in hospitals often provides advanced medical services to patients such as the diagnosis of leukemia. On the other hand, surgery and bone marrow sampling, especially in the diagnosis of childhood leukemia, are even more complex and difficult, resulting in increased human error and procedure time decreased patient satisfaction and increased costs. This study investigates the use of neuro-fuzzy and group method of data handling, for the diagnosis of acute leukemia in children based on the complete blood count test. Furthermore, a principal component analysis is applied to increase the accuracy of the diagnosis. The results show that distinguishing between patient and non-patient individuals can easily be done with adaptive neuro-fuzzy inference system, whereas for classifying between the types of diseases themselves, more pre-processing operations such as reduction of features may be needed. The proposed approach may help to distinguish between two types of leukemia including acute lymphoblastic leukemia and acute myeloid leukemia. Based on the sensitivity of the diagnosis, experts can use the proposed algorithm to help identify the disease earlier and lessen the cost.

Patient classification and outcome prediction in IgA nephropathy

OBJECTIVE

IgA Nephropathy (IgAN) is a common kidney disease which may entail renal failure, known as End Stage Kidney Disease (ESKD). One of the major difficulties dealing with this disease is to predict the time of the long-term prognosis for a patient at the time of diagnosis. In fact, the progression of IgAN to ESKD depends on an intricate interrelationship between clinical and laboratory findings. Therefore, the objective of this work has been the selection of the best data mining tool to build a model able to predict (I) if a patient with a biopsy proven IgAN will reach ESKD and (II) if a patient will reach the ESKD before or after 5 years.

MATERIAL AND METHODS

The largest available cohort study worldwide on IgAN has been used to design and compare several data-driven models. The complete dataset was composed of 1174 records collected from Italian, Norwegian, and Japanese IgAN patients, in the last 30 years. The data mining tools considered in this work were artificial neural networks (ANNs), neuro fuzzy systems (NFSs), support vector machines (SVMs), and decision trees (DTs). A 10-fold cross validation was used to evaluate unbiased performances for all the models.

RESULTS

An extensive model comparison based on accuracy, precision, recall, and f-measure was provided. Overall, the results indicate that ANNs can provide superior performance compared to the other models. The ANN for time-to-ESKD prediction is characterized by accuracy, precision, recall, and f-measure greater than 90%. The ANN for ESKD prediction has accuracy greater than 90% as well as precision, recall, and f-measure for the class of patients not reaching ESKD, while precision, recall, and f-measure for the class of patients reaching ESKD are slightly lower. The obtained model has been implemented in a Web-based decision support system (DSS).

CONCLUSIONS

The extraction of novel knowledge from clinical data and the definition of predictive models to support diagnosis, prognosis, and therapy is becoming an essential tool for researchers and clinical practitioners in medicine. The proposed comparative study of several data mining models for the outcome prediction in IgAN patients, using a large dataset of clinical records from three different countries, provides an insight into the relative prediction ability of the considered methods applied to such a disease.

Fuzzy assessment of health information system users' security awareness

Health information systems (HIS) are a specific area of information systems (IS), where critical patient data is stored and quality health service is only realized with the correct use and efficient dissemination of this data to health workers. Therefore, a balance needs to be established between the levels of security and flow of information on HIS. Instead of implementing higher levels and further mechanisms of control to increase the security of HIS, it is preferable to deal with the arguably weakest link on HIS chain with respect to security: HIS users. In order to provide solutions and approaches for transforming users to the first line of defense in HIS but also to employ capable and appropriate candidates from the pool of newly graduated students, it is important to assess and evaluate the security awareness levels and characteristics of these existing and future users. This study aims to provide a new perspective to understand the phenomenon of security awareness of HIS users with the use of fuzzy analysis, and to assess the present situation of current and future HIS users of a leading medical and educational institution of Turkey, with respect to their security characteristics based on four different security scales. The results of the fuzzy analysis, the guide on how to implement this fuzzy analysis to any health institution and how to read and interpret these results, together with the possible implications of these results to the organization are provided.

Support system for decision making in the identification of risk for body dysmorphic disorder: a fuzzy model

PURPOSE

To develop a fuzzy linguistic model to quantify the level of distress of patients seeking cosmetic surgery. Body dysmorphic disorder (BDD) is a mental condition related to body image relatively common among cosmetic surgery patients; it is difficult to diagnose and is a significant cause of morbidity and mortality. Fuzzy cognitive maps are an efficient tool based on human knowledge and experience that can handle uncertainty in identifying or grading BDD symptoms and the degree of body image dissatisfaction. Individuals who seek cosmetic procedures suffer from some degree of dissatisfaction with appearance.

METHODS

A fuzzy model was developed to measure distress levels in cosmetic surgery patients based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), diagnostic criterion B for BDD. We studied 288 patients of both sexes seeking abdominoplasty, rhinoplasty, or rhytidoplasty in a university hospital.

RESULTS

Patient distress ranged from "none" to "severe" (range=7.5-31.6; cutoff point=18; area under the ROC curve=0.923). There was a significant agreement between the fuzzy model and DSM-IV criterion B (kappa=0.805; p<0.001).

CONCLUSION

The fuzzy model measured distress levels with good accuracy, indicating that it can be used as a screening tool in cosmetic surgery and psychiatric practice.