A CASE-BASED REASONING SYSTEM FOR THE DIAGNOSIS OF INDIVIDUAL SENSITIVITY TO STRESS IN PSYCHOPHYSIOLOGY

Abstract Stress is an increasing problem in our present world. Especially negative stress could cause serious health problems if it remains undiagnosed/misdiagnosed and untreated. In stress medicine, clinicians' measure blood pressure, ECG, finger temperature and breathing rate during a number of exercises to diagnose stressrelated disorders. One of the physiological parameters for quantifying stress levels is the finger temperature measurement which helps the clinicians in diagnosis and treatment of stress. However, in practice, it is difficult and tedious for a clinician to understand, interpret and analyze complex, lengthy sequential sensor signals. There are only few experts who are able to diagnose and predict stress-related problems. A system that can help the clinician in diagnosing stress is important, but the large individual variations make it difficult to build such a system. This research work has investigated several artificial Intelligence techniques for the purpose of developing an intelligent, integrated sensor system for establishing diagnosis and treatment plan in the psychophysiological domain. To diagnose individual sensitivity to stress, case-based reasoning is applied as a core technique to facilitate experience reuse by retrieving previous similar cases. Furthermore, fuzzy techniques are also employed and incorporated into the case-based reasoning system to handle vagueness, uncertainty inherently existing in clinicians reasoning process. The validation of the approach is based on close collaboration with experts and measurements from twenty four persons used as reference. 39 time series from these 24 persons have been used to evaluate the approach (in terms of the matching algorithms) and an expert has ranked and estimated the similarity. The result shows that the system reaches a level of performance close to an expert. The proposed system could be used as an expert for a less experienced clinician or as a second option for an experienced clinician to their decision making process in stress diagnosis. Sammanfattning Den ökande stressnivån i vårt samhälle med allt högre krav och högt tempo har ett högt pris. Stressrelaterade problem och sjukdom är en stor samhällskostnad och speciellt om negativ stress förblir oupptäckt, eller ej korrekt identifierad/diagnostiserad och obehandlad under en längre tid kan den få alvarliga hälsoeffekter för individen vilket kan leda till långvarig sjukskrivning. Inom stressmedicinen mäter kliniker blodtryck, EKG, fingertemperatur och andning under olika situationer för att diagnostisera stress. Stressdiagnos baserat fingertemperaturen (FT) är något som en skicklig klinker kan utföra vilket stämmer med forskningen inom klinisk psykofysiologi. Emellertid i praktiken är det mycket svårt, och mödosamt för att en kliniker att i detalj följa och analysera långa serier av mätvärden och det finns endast mycket få experter som är kompetent att diagnostisera och/eller förutsäga stressproblem. Därför är ett system, som kan hjälpa kliniker i diagnostisering av stress, viktig. Men de stora individvariationerna och bristen av precisa diagnosregler gör det svårt att använda ett datorbaserat system. Detta forskningsarbete har tittat på flera tekniker och metoder inom artificiell intelligens för att hitta en väg fram till ett intelligent sensorbaserat system för diagnos och utformning av behandlingsplaner inom stressområdet. För att diagnostisera individuell stress har fallbaserat resonerande visat sig framgångsrikt, en teknik som gör det möjligt att återanvända erfarenhet, förklara beslut, genom att hämta tidigare liknande fingertemperaturprofilerar. Vidare används "fuzzy logic", luddig logik så att systemet kan hantera de inneboende vagheter i domänen. Metoder och algoritmer har utvecklats för detta. Valideringen av ansatsen baseras på nära samarbete med experter och mätningar från tjugofyra användare. Trettionio tidserier från dessa 24 personer har varit basen för utvärderingen av ansatsen, och en erfaren kliniker har klassificerat alla fall och systemet har visat sig producera resultat nära en expert. Det föreslagna systemet kan användas som ett referens för en mindre erfaren kliniker eller som ett "second opinion" för en erfaren kliniker i deras beslutsprocess. Dessutom har finger temperatur visat sig passa bra för användning i hemmet vid träning eller kontroll vilket blir möjligt med ett datorbaserat stressklassificeringssystem på exempelvis en PC med en USB fingertemperaturmätare. vii Acknowledgemen

Comparing diagnosis of depression in depressed patients by EEG, based on two algorithms :Artificial Nerve Networks and Neuro-Fuzy Networks

Background and aims: Depression disorder is one of the most common diseases, but the diagnosis is widely complicated and controversial because of interventions, overlapping and confusing nature of the disease. So, keeping previous patients’ profile seems effective for diagnosis and treatment of present patients. Use of this memory is latent in synthetic neuro-fuzzy algorithm. Present article introduces two neuro-fuzzy and artificial neural network algorithms as an aid for psychologists and psychiatrists to diagnose and treat depression. Methods: Neuro-fuzzy has been carried out using data evaluated by psychiatrists and scholars in Tabriz city with the convenience sampling method. Sixty-five patients were studied from whom 40 patients were taught feed forward, back propagation by artificial neural network algorithm and 14 patients were tested. An inductive neuro-fuzzy intervention created neuro-fuzzy rules to decide about depression diagnosis. Results: The proposed neuro-fuzzy model created better classifications. Reaching maximum accuracy of 13.97is appropriate in diagnosis prediction. The results of the present study indicated that neuro-fuzzy is more powerful than artificial neural network with accuracy 76.88. Conclusion: Findings of the research showed the depression scores of beck inventory can be predicted and explained with the accuracy of 87 using EEG in F4 and alpha peak frequency. It can be said that such accuracy in predicting can’t be obtained by any regression or route analysis method. The research can be the first step to predict and even identify depression using taking the data directly from the brain. So, there is no need for inventory and even a specialist diagnosis

Fuzzy Inspired Case based Reasoning for Hematology Malignancies Classification

Conventional approaches for collecting and reporting hematological data as well as diagnosing hematologic malignancies such as leukemia, anemia, e.t.c are based on subjective professional physician personal opinions or experiences which are influenced by human error, dependent on human-to-human judgments, time consuming processes and the blood results are non-reproducible. In the light of those human limitations identified, an automatic or semi-automatic classification and corrective method is required because it reduces the load on human observers and accuracy is not affected due to fatigue. Case-Based Reasoning (CBR) as a multi-disciplinary subject that focuses on the reuse of past experiences or cases to proffer solution to new cases was adopted and combined with the power of Fuzzy logic to design a software model that will effectively mine hematology data. This study aim at helping the medical practitioners to diagnose and provide corrective treatment to both normal patients and patients with hematology disorder at the early stage which can reduce the number of deaths. This aim is achievable by developing an intelligent expert system based on fuzzy logic and case-based reasoning for classification of hematology malignancy

Diabetes Diagnosis by Case-Based Reasoning and Fuzzy Logic

In the medical field, experts’ knowledge is based on experience, theoretical knowledge and rules. Case-based reasoning is a problem-solving paradigm which is based on past experiences. For this purpose, a large number of decision support applications based on CBR have been developed. Cases retrieval is often considered as the most important step of case-based reasoning. In this article, we integrate fuzzy logic and data mining to improve the response time and the accuracy of the retrieval of similar cases. The proposed Fuzzy CBR is composed of two complementary parts; the part of classification by fuzzy decision tree realized by Fispro and the part of case-based reasoning realized by the platform JColibri. The use of fuzzy logic aims to reduce the complexity of calculating the degree of similarity that can exist between diabetic patients who require different monitoring plans. The results of the proposed approach are compared with earlier methods using accuracy as metrics. The experimental results indicate that the fuzzy decision tree is very effective in improving the accuracy for diabetes classification and hence improving the retrieval step of CBR reasoning

Identifying Complexity in Infectious Diseases Inpatient Settings: An Observation Study

Background Understanding complexity in healthcare has the potential to reduce decision and treatment uncertainty. Therefore, identifying both patient and task complexity may offer better task allocation and design recommendation for next-generation health information technology system design. Objective To identify specific complexity-contributing factors in the infectious disease domain and the relationship with the complexity perceived by clinicians. Method We observed and audio recorded clinical rounds of three infectious disease teams. Thirty cases were observed for a period of four consecutive days. Transcripts were coded based on clinical complexity-contributing factors from the clinical complexity model. Ratings of complexity on day 1 for each case were collected. We then used statistical methods to identify complexity-contributing factors in relationship to perceived complexity of clinicians. Results A factor analysis (principal component extraction with varimax rotation) of specific items revealed three factors (eigenvalues \u3e 2.0) explaining 47% of total variance, namely task interaction and goals (10 items, 26%, Cronbach’s Alpha = 0.87), urgency and acuity (6 items, 11%, Cronbach’s Alpha = 0.67), and psychosocial behavior (4 items, 10%, Cronbach’s alpha = 0.55). A linear regression analysis showed no statistically significant association between complexity perceived by the physicians and objective complexity, which was measured from coded transcripts by three clinicians (Multiple R-squared = 0.13, p = 0.61). There were no physician effects on the rating of perceived complexity. Conclusion Task complexity contributes significantly to overall complexity in the infectious diseases domain. The different complexity-contributing factors found in this study can guide health information technology system designers and researchers for intuitive design. Thus, decision support tools can help reduce the specific complexity-contributing factors. Future studies aimed at understanding clinical domain-specific complexity-contributing factors can ultimately improve task allocation and design for intuitive clinical reasoning

The “Uberization” of Healthcare: The Forthcoming Legal Storm over Mobile Health Technology’s Impact on the Medical Profession

The article examines the potential of mobile health to transform the delivery of healthcare through allowing non-physicians providing care independent of physicians and outside of traditional clinics and hospitals in the United States. It discusses licensing and scope of practice laws from large information technology (IT) corporations

The “Uberization” of Healthcare: The Forthcoming Legal Storm over Mobile Health Technology’s Impact on the Medical Profession

The article examines the potential of mobile health to transform the delivery of healthcare through allowing non-physicians providing care independent of physicians and outside of traditional clinics and hospitals in the United States. It discusses licensing and scope of practice laws from large information technology (IT) corporations