From vagueness in medical thought to the foundations of fuzzy reasoning in medical diagnosis

Implementation and evaluation of a system for assessment of the quality of long-term management of patients at a geriatric hospital

BACKGROUND

The increasing aging population presents a significant challenge, accompanied by a shortage of professional caregivers, adding to the therapeutic burden. Clinical decision support systems, utilizing computerized clinical guidelines, can improve healthcare quality, reduce expenses, save time, and boost caregiver efficiency.

OBJECTIVES

1) Develop and evaluate an automated quality assessment (QA) system for retrospective longitudinal care quality analysis, focusing on clinical staff adherence to evidence-based guidelines (GLs). 2) Assess the system's technical feasibility and functional capability for senior nurse use in geriatric pressure-ulcer management.

METHODS

A computational QA system using our Quality Assessment Temporal Patterns (QATP) methodology was designed and implemented. Our methodology transforms the GL's procedural-knowledge into declarative-knowledge temporal-abstraction patterns representing the expected execution trace in the patient's data for correct therapy application. Fuzzy temporal logic allows for partial compliance, reflecting individual and grouped action performance considering their values and temporal aspects. The system was tested using a pressure ulcer treatment GL and data from 100 geriatric patients' Electronic Medical Records (EMR). After technical evaluation for accuracy and feasibility, an extensive functional evaluation was conducted by an experienced nurse, comparing QA scores with and without system support, and versus automated system scores. Time efficiency was also measured.

RESULTS

QA scores from the geriatric nurse, with and without system's support, did not significantly differ from those provided by the automated system (p < 0.05), demonstrating the effectiveness and reliability of both manual and automated methods. The system-supported manual QA process reduced scoring time by approximately two-thirds, from an average of 17.3 min per patient manually to about 5.9 min with the system's assistance, highlighting the system's efficiency potential in clinical practice.

CONCLUSION

The QA system based on QATP, produces scores consistent with an experienced nurse's assessment for complex care over extended periods. It enables quick and accurate quality care evaluation for multiple patients after brief training. Such automated QA systems may empower nursing staff, enabling them to manage more patients, accurately and consistently, while reducing costs due to saved time and effort, and enhanced compliance with evidence-based guidelines.

[Systems thinking, subjective findings and diagnostic "pigeonholing" in ME/CFS: A mainly qualitative public health study from a patient perspective]

BACKGROUND

ME/CFS (Myalgic encephalomyelitis/chronic fatigue syndrome) is an illness that is predominantly viewed as a neuroimmunological multisystem disease, which is still unknown to many doctors in Germany or which they classify as a psychosomatic disease. From their perspective, ME/CFS patients report significant deficits in terms of medical treatment and a doctor-patient relationship (DP relationship) that is perceived as problematic. The aim of the present study is to more precisely analyse the process of finding a diagnosis as an influencing factor on the DP relationship in ME/CFS from the point of view of those affected.

METHOD

As part of an explorative qualitative survey, 544 ME/CFS patients (> 20 years; 455 ♀, 89 ♂) with a medical diagnosis of ME/CFS were asked in writing about their experiences with regard to the process of finding a diagnosis. The sampling was previously done by self-activation and via the snowball principle. The questionnaire to be answered was structured analogously to a focused, standardized guideline interview. The evaluation was carried out as part of a qualitative content analysis according to Mayring. Some of the results were subsequently quantified.

RESULTS

The participants described what they saw as the inadequate process of making a diagnosis as a central factor in a problematic DP relationship in ME/CFS. From their point of view, many doctors deny the existence of ME/CFS or classify it as a solely psychosomatic illness, insist on their level of knowledge, ignore patient knowledge and disregard scientific information provided. They follow the standard program, think in "pigeonholes" and are incapable of systemic thinking. This has a significant impact on the DP relationship.

DISCUSSION

From the point of view of ME/CFS patients, the process of making a diagnosis and the recognition of ME/CFS as a neuroimmunological multisystem disease are the central aspects of a DP relationship that they experience as problematic. In the past, findings classified as "subjective" and thus ignored, the pigeonholing that is characteristic of biomedically oriented medicine and a healthcare system that opposes systemic thinking when making a diagnosis have all been identified as factors that may have a significant impact on the DP relationship.

Managing the moral expansion of medicine

Science and technology have vastly expanded the realm of medicine. The numbers of and knowledge about diseases has greatly increased, and we can help more people in many more ways than ever before. At the same time, the extensive expansion has also augmented harms, professional responsibility, and ethical concerns. While these challenges have been studied from a wide range of perspectives, the problems prevail. This article adds value to previous analyses by identifying how the moral imperative of medicine has expanded in three ways: (1) from targeting experienced phenomena, such as pain and suffering, to non-experienced phenomena (paraclinical signs and indicators); (2) from addressing present pain to potential future suffering; and (3) from reducing negative wellbeing (pain and suffering) to promoting positive wellbeing. These expansions create and aggravate problems in medicine: medicalization, overdiagnosis, overtreatment, risk aversion, stigmatization, and healthism. Moreover, they threaten to infringe ethical principles, to distract attention and responsibility from other competent agents and institutions, to enhance the power and responsibility of professionals, and to change the professional-beneficiary relationship. In order to find ways to manage the moral expansion of medicine, four traditional ways of setting limits are analyzed and dismissed. However, basic asymmetries in ethics suggest that it is more justified to address people's negative wellbeing (pain and suffering) than their positive wellbeing. Moreover, differences in epistemology, indicate that it is less uncertain to address present pain and suffering than future wellbeing and happiness. Based on these insights the article concludes that the moral imperative of medicine has a gradient from pain and suffering to wellbeing and happiness, and from the present to the future. Hence, in general present pain and suffering have normative priority over future positive wellbeing.

Predicting the Risk of Mortality in Children using a Fuzzy-Probabilistic Hybrid Model

Introduction

The mortality risk in children admitted to Pediatric Intensive Care Units (PICU) is usually estimated by means of validated scales, which only include objective data among their items. Human perceptions may also add relevant information to prognosticate the risk of death, and the tool to use this subjective data is fuzzy logic. The objective of our study was to develop a mathematical model to predict mortality risk based on the subjective perception of PICU staff and to evaluate its accuracy compared to validated scales.

Methods

A prospective observational study in two PICUs (one in Spain and another in Latvia) was performed. Children were consecutively included regardless of the cause of admission along a two-year period. A fuzzy set program was developed for the PICU staff to record the subjective assessment of the patients' mortality risk expressed through a short range and a long range, both between 0% and 100%. Pediatric Index of Mortality 2 (PIM2) and Therapeutic Intervention Scoring System 28 (TISS28) were also prospectively calculated for each patient. Subjective and objective predictions were compared using the logistic regression analysis. To assess the prognostication ability of the models a stratified B-random K-fold cross-validation was performed.

Results

Five hundred ninety-nine patients were included, 308 in Spain (293 survivors, 15 nonsurvivors) and 291 in Latvia (282 survivors, 9 nonsurvivors). The best logistic classification model for subjective information was the one based on MID (midpoint of the short range), whereas objective information was the one based on PIM2. Mortality estimation performance was 86.3% for PIM2, 92.6% for MID, and the combination of MID and PIM2 reached 96.4%.

Conclusions

Subjective assessment was as useful as validated scales to estimate the risk of mortality. A hybrid model including fuzzy information and probabilistic scales (PIM2) seems to increase the accuracy of prognosticating mortality in PICU.

Ranking patients on the kidney transplant waiting list based on fuzzy inference system

Background

Kidney transplantation is the best treatment for people with End-Stage Renal Disease (ESRD). Kidney allocation is the most important challenge in kidney transplantation process. In this study, a Fuzzy Inference System (FIS) was developed to rank the patients based on kidney allocation factors. The main objective was to develop an expert system, which would mimic the expert intuitive thinking and decision-making process in the face of the complexity of kidney allocation.

Methods

In the first stage, kidney allocation factors were identified. Next, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) has been used to weigh them. The purpose of this stage is to develop a point scoring system for kidney allocation. Fuzzy if-then rules were extracted from the United Network for Organ Sharing (UNOS) dataset by constructing the decision tree, in the second stage. Then, a Multi-Input Single-Output (MISO) Mamdani fuzzy inference system was developed for ranking the patients on the waiting list.

Results

To evaluate the performance of the developed Fuzzy Inference System for Kidney Allocation (FISKA), it was compared with a point scoring system and a filtering system as two common approaches for kidney allocation. The results indicated that FISKA is more acceptable to the experts than the mentioned common methods.

Conclusion

Given the scarcity of donated kidneys and the importance of optimal use of existing kidneys, FISKA can be very useful for improving kidney allocation systems. Countries that decide to change or improve the kidney allocation system can simply use the proposed model. Furthermore, this model is applicable to other organs, including lung, liver, and heart.

Vagueness in Medicine: On Disciplinary Indistinctness, Fuzzy Phenomena, Vague Concepts, Uncertain Knowledge, and Fact-Value-Interaction

This article investigates five kinds of vagueness in medicine: disciplinary, ontological, conceptual, epistemic, and vagueness with respect to descriptive-prescriptive connections. First, medicine is a discipline with unclear borders, as it builds on a wide range of other disciplines and subjects. Second, medicine deals with many indistinct phenomena resulting in borderline cases. Third, medicine uses a variety of vague concepts, making it unclear which situations, conditions, and processes that fall under them. Fourth, medicine is based on and produces uncertain knowledge and evidence. Fifth, vagueness emerges in medicine as a result of a wide range of fact-value-interactions. The various kinds of vagueness in medicine can explain many of the basic challenges of modern medicine, such as overdiagnosis, underdiagnosis, and medicalization. Even more, it illustrates how complex and challenging the field of medicine is, but also how important contributions from the philosophy can be for the practice of medicine. By clarifying and, where possible, reducing or limiting vagueness, philosophy can help improving care. Reducing the various types of vagueness can improve clinical decision-making, informing individuals, and health policy making.

Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification

This paper presents a systematic review of the literature and the classification of fuzzy logic application in an infectious disease. Although the emergence of infectious diseases and their subsequent spread have a significant impact on global health and economics, a comprehensive literature evaluation of this topic has yet to be carried out. Thus, the current study encompasses the first systematic, identifiable and comprehensive academic literature evaluation and classification of the fuzzy logic methods in infectious diseases. 40 papers on this topic, which have been published from 2005 to 2019 and related to the human infectious diseases were evaluated and analyzed. The findings of this evaluation clearly show that the fuzzy logic methods are vastly used for diagnosis of diseases such as dengue fever, hepatitis and tuberculosis. The key fuzzy logic methods used for the infectious disease are the fuzzy inference system; the rule-based fuzzy logic, Adaptive Neuro-Fuzzy Inference System (ANFIS) and fuzzy cognitive map. Furthermore, the accuracy, sensitivity, specificity and the Receiver Operating Characteristic (ROC) curve were universally applied for a performance evaluation of the fuzzy logic techniques. This thesis will also address the various needs between the different industries, practitioners and researchers to encourage more research regarding the more overlooked areas, and it will conclude with several suggestions for the future infectious disease researches.

Assessing experience in the deliberate practice of running using a fuzzy decision-support system

The judgement of skill experience and its levels is ambiguous though it is crucial for decision-making in sport sciences studies. We developed a fuzzy decision support system to classify experience of non-elite distance runners. Two Mamdani subsystems were developed based on expert running coaches' knowledge. In the first subsystem, the linguistic variables of training frequency and volume were combined and the output defined the quality of running practice. The second subsystem yielded the level of running experience from the combination of the first subsystem output with the number of competitions and practice time. The model results were highly consistent with the judgment of three expert running coaches (r>0.88, p<0.001) and also with five other expert running coaches (r>0.86, p<0.001). From the expert's knowledge and the fuzzy model, running experience is beyond the so-called "10-year rule" and depends not only on practice time, but on the quality of practice (training volume and frequency) and participation in competitions. The fuzzy rule-based model was very reliable, valid, deals with the marked ambiguities inherent in the judgment of experience and has potential applications in research, sports training, and clinical settings.

A Boolean Consistent Fuzzy Inference System for Diagnosing Diseases and Its Application for Determining Peritonitis Likelihood

Fuzzy inference systems (FIS) enable automated assessment and reasoning in a logically consistent manner akin to the way in which humans reason. However, since no conventional fuzzy set theory is in the Boolean frame, it is proposed that Boolean consistent fuzzy logic should be used in the evaluation of rules. The main distinction of this approach is that it requires the execution of a set of structural transformations before the actual values can be introduced, which can, in certain cases, lead to different results. While a Boolean consistent FIS could be used for establishing the diagnostic criteria for any given disease, in this paper it is applied for determining the likelihood of peritonitis, as the leading complication of peritoneal dialysis (PD). Given that patients could be located far away from healthcare institutions (as peritoneal dialysis is a form of home dialysis) the proposed Boolean consistent FIS would enable patients to easily estimate the likelihood of them having peritonitis (where a high likelihood would suggest that prompt treatment is indicated), when medical experts are not close at hand.

Towards a unified theory of health-disease: II. Holopathogenesis

This article presents a systematic framework for modeling several classes of illness-sickness-disease named as Holopathogenesis. Holopathogenesis is defined as processes of over-determination of diseases and related conditions taken as a whole, comprising selected facets of the complex object Health. First, a conceptual background of Holopathogenesis is presented as a series of significant interfaces (biomolecular-immunological, physiopathological-clinical, epidemiological-ecosocial). Second, propositions derived from Holopathogenesis are introduced in order to allow drawing the disease-illness-sickness complex as a hierarchical network of networks. Third, a formalization of intra- and inter-level correspondences, over-determination processes, effects and links of Holopathogenesis models is proposed. Finally, the Holopathogenesis frame is evaluated as a comprehensive theoretical pathology taken as a preliminary step towards a unified theory of health-disease.

An extensible six-step methodology to automatically generate fuzzy DSSs for diagnostic applications

Background

The diagnosis of many diseases can be often formulated as a decision problem; uncertainty affects these problems so that many computerized Diagnostic Decision Support Systems (in the following, DDSSs) have been developed to aid the physician in interpreting clinical data and thus to improve the quality of the whole process. Fuzzy logic, a well established attempt at the formalization and mechanization of human capabilities in reasoning and deciding with noisy information, can be profitably used. Recently, we informally proposed a general methodology to automatically build DDSSs on the top of fuzzy knowledge extracted from data.

Methods

We carefully refine and formalize our methodology that includes six stages, where the first three stages work with crisp rules, whereas the last three ones are employed on fuzzy models. Its strength relies on its generality and modularity since it supports the integration of alternative techniques in each of its stages.

Results

The methodology is designed and implemented in the form of a modular and portable software architecture according to a component-based approach. The architecture is deeply described and a summary inspection of the main components in terms of UML diagrams is outlined as well. A first implementation of the architecture has been then realized in Java following the object-oriented paradigm and used to instantiate a DDSS example aimed at accurately diagnosing breast masses as a proof of concept.

Conclusions

The results prove the feasibility of the whole methodology implemented in terms of the architecture proposed.

The fuzzy brain. Vagueness and mapping connectivity of the human cerebral cortex

While the past century of neuroscientific research has brought considerable progress in defining the boundaries of the human cerebral cortex, there are cases in which the demarcation of one area from another remains fuzzy. Despite the existence of clearly demarcated areas, examples of gradual transitions between areas are known since early cytoarchitectonic studies. Since multi-modal anatomical approaches and functional connectivity studies brought renewed attention to the topic, a better understanding of the theoretical and methodological implications of fuzzy boundaries in brain science can be conceptually useful. This article provides a preliminary conceptual framework to understand this problem by applying philosophical theories of vagueness to three levels of neuroanatomical research. For the first two levels (cytoarchitectonics and fMRI studies), vagueness will be distinguished from other forms of uncertainty, such as imprecise measurement or ambiguous causal sources of activation. The article proceeds to discuss the implications of these levels for the anatomical study of connectivity between cortical areas. There, vagueness gets imported into connectivity studies since the network structure is dependent on the parcellation scheme and thresholds have to be used to delineate functional boundaries. Functional connectivity may introduce an additional form of vagueness, as it is an organizational principle of the brain. The article concludes by discussing what steps are appropriate to define areal boundaries more precisely.

Multivariate modeling to identify patterns in clinical data: the example of chest pain

BACKGROUND

In chest pain, physicians are confronted with numerous interrelationships between symptoms and with evidence for or against classifying a patient into different diagnostic categories. The aim of our study was to find natural groups of patients on the basis of risk factors, history and clinical examination data which should then be validated with patients' final diagnoses.

METHODS

We conducted a cross-sectional diagnostic study in 74 primary care practices to establish the validity of symptoms and findings for the diagnosis of coronary heart disease. A total of 1199 patients above age 35 presenting with chest pain were included in the study. General practitioners took a standardized history and performed a physical examination. They also recorded their preliminary diagnoses, investigations and management related to the patient's chest pain. We used multiple correspondence analysis (MCA) to examine associations on variable level, and multidimensional scaling (MDS), k-means and fuzzy cluster analyses to search for subgroups on patient level. We further used heatmaps to graphically illustrate the results.

RESULTS

A multiple correspondence analysis supported our data collection strategy on variable level. Six factors emerged from this analysis: "chest wall syndrome", "vital threat", "stomach and bowel pain", "angina pectoris", "chest infection syndrome", and " self-limiting chest pain". MDS, k-means and fuzzy cluster analysis on patient level were not able to find distinct groups. The resulting cluster solutions were not interpretable and had insufficient statistical quality criteria.

CONCLUSIONS

Chest pain is a heterogeneous clinical category with no coherent associations between signs and symptoms on patient level.

Fuzzy obesity index (MAFOI) for obesity evaluation and bariatric surgery indication

Background

The Miyahira-Araujo Fuzzy Obesity Index (MAFOI) for being used as an alternative in bariatric surgery indication (BSI) is validated in this paper. The search for a more accurate method to evaluate obesity and to indicate a better treatment is important in the world health context. Body mass index (BMI) is considered the main criteria for obesity treatment and BSI. Nevertheless, the fat excess related to the percentage of Body Fat (%BF) is actually the principal harmful factor in obesity disease that is usually neglected. The aim of this research is to validate a previous fuzzy mechanism by associating BMI with %BF that yields the Miyahira-Araujo Fuzzy Obesity Index (MAFOI) for obesity evaluation, classification, analysis, treatment, as well for better indication of surgical treatment.

Methods

Seventy-two patients were evaluated for both BMI and %BF. The BMI and %BF classes are aggregated yielding a new index (MAFOI). The input linguistic variables are the BMI and %BF, and the output linguistic variable is employed an obesity classification with entirely new types of obesity in the fuzzy context, being used for BSI, as well.

Results

There is gradual and smooth obesity classification and BSI criteria when using the Miyahira-Araujo Fuzzy Obesity Index (MAFOI), mainly if compared to BMI or %BF alone for dealing with obesity assessment, analysis, and treatment.

Conclusion

The resulting fuzzy decision support system (MAFOI) becomes a feasible alternative for obesity classification and bariatric surgery indication.

Prognosis-a wearable health-monitoring system for people at risk: methodology and modeling

Wearable health-monitoring systems (WHMSs) represent the new generation of healthcare by providing real-time unobtrusive monitoring of patients' physiological parameters through the deployment of several on-body and even intrabody biosensors. Although several technological issues regarding WHMS still need to be resolved in order to become more applicable in real-life scenarios, it is expected that continuous ambulatory monitoring of vital signs will enable proactive personal health management and better treatment of patients suffering from chronic diseases, of the elderly population, and of emergency situations. In this paper, we present a physiological data fusion model for multisensor WHMS called Prognosis. The proposed methodology is based on a fuzzy regular language for the generation of the prognoses of the health conditions of the patient, whereby the current state of the corresponding fuzzy finite-state machine signifies the current estimated health state and context of the patient. The operation of the proposed scheme is explained via detailed examples in hypothetical scenarios. Finally, a stochastic Petri net model of the human-device interaction is presented, which illustrates how additional health status feedback can be obtained from the WHMS' user.