Multilayered rule-based expert system for diagnosing uveitis.

Uveitis is a condition caused by inflammation of the uvea, which is the middle layer of the eye. Uveitis can result in swelling or destruction of the eye tissue, which can lead to visual impairment or blindness [1]. Many diseases, either systemic or localized to the eye, are associated with the symptoms of uveitis. Thus, it is often hard to determine the underlying disease responsible for uveitis, especially when the signs and symptoms are unclear. Additionally, there are few experts on uveitis, especially in poor and developing countries. In this paper, we design and build a rule-based expert system to diagnose uveitis. The main motivation for developing this expert system was to mitigate the lack of human experts by helping general ophthalmologists achieve a correct diagnosis with minimal time and effort. Furthermore, the system can act as a good educational tool for newly graduated doctors, guiding their work with their patients and supporting their diagnostic decisions. The novel multilayer design of the system allows flexibility and ease of scaling to new cases in the future. Many techniques were used to improve the system's diagnostic flexibility and overcome incomplete user input. Tests of the system have yielded promising results.

An evaluation of selected (Q)SARs/expert systems for predicting skin sensitisation potential.

Predictive testing to characterise substances for their skin sensitisation potential has historically been based on animal models such as the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximisation Test (GPMT). In recent years, EU regulations, have provided a strong incentive to develop non-animal alternatives, such as expert systems software. Here we selected three different types of expert systems: VEGA (statistical), Derek Nexus (knowledge-based) and TIMES-SS (hybrid), and evaluated their performance using two large sets of animal data: one set of 1249 substances from eChemportal and a second set of 515 substances from NICEATM. A model was considered successful at predicting skin sensitisation potential if it had at least the same balanced accuracy as the LLNA and the GPMT had in predicting the other outcomes, which ranged from 79% to 86%. We found that the highest balanced accuracy of any of the expert systems evaluated was 65% when making global predictions. For substances within the domain of TIMES-SS, however, balanced accuracies for the two datasets were found to be 79% and 82%. In those cases where a chemical was within the TIMES-SS domain, the TIMES-SS skin sensitisation hazard prediction had the same confidence as the result from LLNA or GPMT.

Suicide Risk Assessment in Hospitals: An Expert System-Based Triage Tool.

BACKGROUND: The November 2010 Joint Commission Sentinel Event Alert on the prevention of suicides in medical/surgical units and the emergency department (ED) mandates screening every patient treated as an outpatient or admitted to the hospital for suicide risk. Our aim was to develop a suicide risk assessment tool to (1) predict the expert psychiatrist's assessment for risk of committing suicide within 72 hours in the hospital, (2) replicate the recommended intervention by the psychiatrist, and (3) demonstrate acceptable levels of participant satisfaction. METHODS: The 3 phases of tool development took place between October 2012 and February 2014. An expert panel developed key questions for a tablet-based suicide risk questionnaire. We then performed a randomized cross-sectional study comparing the questionnaire to the interview by a psychiatrist, for model derivation. A neural network model was constructed using 255 ED participants. Evaluation was the agreement between the risk/intervention scores using the questionnaire and the risk/intervention scores given by psychiatrists to the same patients. The model was validated using a new population of 124 participants from the ED and 50 participants from medical/surgical units. RESULTS: The suicide risk assessment tool performed at a remarkably high level. For levels of suicide risk (minimal or low, moderate, or high), areas under the curves were all above 0.938. For levels of intervention (routine, specialized, highly specialized, or secure), areas under the curves were all above 0.914. Participants reported that they liked the tool, and it took less than a minute to use. CONCLUSIONS: An expert-based neural network model predicted psychiatrists' assessments of risk of suicide in the hospital within 72 hours. It replicated psychiatrist-recommended interventions to mitigate risk in EDs and medical/surgical units.

A Type-2 Fuzzy Image Processing Expert System for Diagnosing Brain Tumors.

The focus of this paper is diagnosing and differentiating Astrocytomas in MRI scans by developing an interval Type-2 fuzzy automated tumor detection system. This system consists of three modules: working memory, knowledge base, and inference engine. An image processing method with three steps of preprocessing, segmentation and feature extraction, and approximate reasoning is used in inference engine module to enhance the quality of MRI scans, segment them into desired regions, extract the required features, and finally diagnose and differentiate Astrocytomas. However, brain tumors have different characteristics in different planes, so considering one plane of patient's MRI scan may cause inaccurate results. Therefore, in the developed system, several consecutive planes are processed. The performance of this system is evaluated using 95 MRI scans and the results show good improvement in diagnosing and differentiating Astrocytomas.

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification.

A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.

Open Ambient Intelligence Environments.

The present impact of ambient intelligence concepts in eInclusion is first briefly reviewed. Suggestions and examples of how ambient intelligent environments should be specified, designed and used to favour independent living of people with activity limitations are presented.

Decision support system for age-related macular degeneration using discrete wavelet transform.

Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback-Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, [Formula: see text]-nearest neighbor ([Formula: see text]-NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70%, sensitivity of 91.11%, and specificity of 96.30% using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs.

LWAs computational platform for e-consultation using mobile devices: cases from developing nations.

BACKGROUND: Mobile devices have been impacting on human standard of living by providing timely and accurate information anywhere and anytime through wireless media in developing nations. Shortage of experts in medical fields is very obvious throughout the whole world but more pronounced in developing nations. OBJECTIVE: Thus, this study proposes a telemedicine platform for the vulnerable areas of developing nations. The vulnerable area are the interior with little or no medical facilities, hence the dwellers are very susceptible to sicknesses and diseases. METHODS: The framework uses mobile devices that can run LightWeight Agents (LWAs) to send consultation requests to a remote medical expert in urban city from the vulnerable interiors. The feedback is conveyed to the requester through the same medium. The system architecture which contained AgenRoller, LWAs, The front-end (mobile devices) and back-end (the medical server) is presented. The algorithm for the software component of the architecture (AgenRoller) is also presented. The system is modeled as M/M/1/c queuing system, and simulated using Simevents from MATLAB Simulink environment. RESULT: The simulation result presented show the average queue length, the number of entities in the queue and the number of entities departure from the system. These together present the rate of information processing in the system. CONCLUSION: A full scale development of this system with proper implementation will help extend the few medical facilities available in the urban cities in developing nations to the interiors thereby reducing the number of casualties in the vulnerable areas of the developing world especially in Sub Saharan Africa.

[Automation expert system for quantitative risk assessment in reconstructive surgical procedures on the large intestine].

The algorithm of quantitative estimation of operative risk while performing reconstructive-restoration colonic surgery was proposed. There was established high sensitivity and specificity of quantitative estimation of operative risk while application of a neuronet models, especially in usage of a neuronet owing two output neurons. While doing the operative risk estimation, using neuronets, the fault answers rate, as well as of unrecognized states, is trustworthy small, what permits to recommend the method for wide application.

Pacific Tracker 2 - expert system (PacTrac2-ES) behavioural assessment and intervention tool for the Pacific Kids DASH for Health (PacDASH) study.

The Pacific Tracker (PacTrac) is a computer program designed to analyse food intakes of individuals from the Pacific Region. PacTrac's original output included servings of daily intake of food groups according to the United States Food Guide Pyramid, nutrient intake recommendations, and a comparison to other national nutrition recommendations. PacTrac was made available for public use through the Hawaii Foods website (hawaiifoods.hawaii.edu). PacTrac2 is an updated and expanded version of PacTrac that uses the United States MyPyramid/MyPlate food groups in household units of daily intake, rather than servings. In addition, the PacTrac2 includes a physical activity analysis tool which quantifies minutes of physical activities and their intensities based on energy estimates from the compendium of physical activity and research on children. An Expert System (ES) - a computerised decision tree to guide behaviour change - was developed using information on self-efficacy and stage of readiness to change, and the fruit and vegetable intake and physical activity information from PacTrac2. The ES produces reports for the child, the parent/guardian, and the child's physician with child-specific strategies, targeted behavioural information, and feedback tailored to the child. PacTrac2-ES was designed for the Pacific Kids DASH for Health (PacDASH) intervention study, conducted in the Kaiser Permanente health care system in Hawaii. The intervention is based on the child's self-efficacy and stage of readiness to change intake of fruits and vegetables and physical activity, with a goal of maintaining body weight to prevent obesity. The intervention is complemented with stage-based mailers addressing the environment for physical activity and fruit and vegetable intake and newsletters that address related behaviours (sedentary activity and a DASH eating approach). This project is the first to expand the PacTrac to contain children's foods and physical activities from the Pacific Region and to use current US MyPyramid/MyPlate food and physical activity analysis and guidance systems, and to develop and implement an Expert System for fruits, vegetables and physical activity of 5-8-year-old children. The PacTrac2-ES was used in the PacDASH study and will be used for other programs to promote healthy eating and physical activity of children in the Pacific Region.

Quality assessment of expert answers to lay questions about cystic fibrosis from various language zones in Europe: the ECORN-CF project.

BACKGROUND: The European Centres of Reference Network for Cystic Fibrosis (ECORN-CF) established an Internet forum which provides the opportunity for CF patients and other interested people to ask experts questions about CF in their mother language. The objectives of this study were to: 1) develop a detailed quality assessment tool to analyze quality of expert answers, 2) evaluate the intra- and inter-rater agreement of this tool, and 3) explore changes in the quality of expert answers over the time frame of the project. METHODS: The quality assessment tool was developed by an expert panel. Five experts within the ECORN-CF project used the quality assessment tool to analyze the quality of 108 expert answers published on ECORN-CF from six language zones. 25 expert answers were scored at two time points, one year apart. Quality of answers was also assessed at an early and later period of the project. Individual rater scores and group mean scores were analyzed for each expert answer. RESULTS: A scoring system and training manual were developed analyzing two quality categories of answers: content and formal quality. For content quality, the grades based on group mean scores for all raters showed substantial agreement between two time points, however this was not the case for the grades based on individual rater scores. For formal quality the grades based on group mean scores showed only slight agreement between two time points and there was also poor agreement between time points for the individual grades. The inter-rater agreement for content quality was fair (mean kappa value 0.232 ± 0.036, p < 0.001) while only slight agreement was observed for the grades of the formal quality (mean kappa value 0.105 ± 0.024, p < 0.001). The quality of expert answers was rated high (four language zones) or satisfactory (two language zones) and did not change over time. CONCLUSIONS: The quality assessment tool described in this study was feasible and reliable when content quality was assessed by a group of raters. Within ECORN-CF, the tool will help ensure that CF patients all over Europe have equal possibility of access to high quality expert advice on their illness.

The use of point-of-sale machines in school cafeterias as a method of parental influence over child lunch food choices.

BACKGROUND: Computerized point-of-sale (POS) machine software that allows parents to place restrictions on their child's school meal accounts is available. Parents could restrict specific foods (eg, chips), identify specific days the child can purchase extra foods, or set monetary limits. This descriptive study examines the use of parental restrictions on student cafeteria POS accounts in a convenience sample of 2 school districts. METHODS: POS alerts, with student gender, grade, ethnicity, and students' free or reduced-price meal eligibility, were obtained from 2 school food service departments for the 2007-2008 school year. The alerts were coded into 5 categories: financial, medical, restrictions, snacks OK, and extras OK. The distribution of alerts by district, students, and demographics was then tabulated. RESULTS: District A (4839 students) had more students with alerts (n = 789, 16%) than District B (8510 students; n = 217, 2.6%), and 94 District A students had a second alert. District A parents had to provide written permission for their child to purchase snacks (n = 654, 13.5%) and extra meal items (n = 113, 2.3%). Most alerts were for full-pay students in both districts (74% and 66%) and varied by demographics of the students. CONCLUSIONS: Few parents actually used this system to limit student purchases of foods outside the school meal. Future studies should investigate the influence of these restrictions on student food choices.

BrainKnowledge: a human brain function mapping knowledge-base system.

Associating fMRI image datasets with the available literature is crucial for the analysis and interpretation of fMRI data. Here, we present a human brain function mapping knowledge-base system (BrainKnowledge) that associates fMRI data analysis and literature search functions. BrainKnowledge not only contains indexed literature, but also provides the ability to compare experimental data with those derived from the literature. BrainKnowledge provides three major functions: (1) to search for brain activation models by selecting a particular brain function; (2) to query functions by brain structure; (3) to compare the fMRI data with data extracted from the literature. All these functions are based on our literature extraction and mining module developed earlier (Hsiao, Chen, Chen. Journal of Biomedical Informatics 42, 912-922, 2009), which automatically downloads and extracts information from a vast amount of fMRI literature and generates co-occurrence models and brain association patterns to illustrate the relevance of brain structures and functions. BrainKnowledge currently provides three co-occurrence models: (1) a structure-to-function co-occurrence model; (2) a function-to-structure co-occurrence model; and (3) a brain structure co-occurrence model. Each model has been generated from over 15,000 extracted Medline abstracts. In this study, we illustrate the capabilities of BrainKnowledge and provide an application example with the studies of affect. BrainKnowledge, which combines fMRI experimental results with Medline abstracts, may be of great assistance to scientists not only by freeing up resources and valuable time, but also by providing a powerful tool that collects and organizes over ten thousand abstracts into readily usable and relevant sources of information for researchers.

Electrical compound action potentials recorded with automated neural response telemetry: threshold changes as a function of time and electrode position.

OBJECTIVE: Since the introduction of neural response telemetry (NRT) for the Nucleus 24 cochlear implant (CI24), researchers and clinicians have investigated the feasibility of using the electrically evoked compound action potential (ECAP) threshold to objectively predict psychophysical measurements that are used in the programming of the speech processor. The ability to substitute objective for behavioral measurements, particularly measurements made at the time of surgery, would greatly facilitate programming the MAP for young children and other individuals who are not able to provide reliable behavioral data required for MAP programming. There have been a number of studies that have examined characteristics of the ECAP measured at the time of surgery and postoperatively; however, all the available published data are based on the CI24. With the introduction of the Nucleus Freedom device, an automated NRT (AutoNRT) program became available, which was capable of measuring ECAP thresholds at lower levels than was previously possible with NRT software associated with the CI24 device. It was hypothesized that the enhancements to the NRT program may improve the predictability of postoperative measurements from intraoperatively recorded ECAP thresholds. The purpose of this study was to track ECAP thresholds obtained using AutoNRT as a function of time and electrode position. DESIGN: ECAP thresholds were recorded from 71 children and adults implanted with the Nucleus Freedom device using the AutoNRT test protocol. ECAP thresholds were obtained at the time of surgery, at initial stimulation, and 3 mos poststimulation. Five electrodes located at basal, middle, and apical positions in the cochlea were tested at each time interval and thresholds were compared. RESULTS: Significant differences were found in ECAP thresholds measured with AutoNRT as a function of both time and electrode position. Basal electrodes had higher ECAP thresholds than apical electrodes and that relationship was consistent for each time period. Thresholds for all electrodes decreased between surgery and initial stimulation and remained relatively stable at 3 mos poststimulation. ECAP thresholds were consistently lower for children compared with adults at each time point. Mid-array electrodes (11 and 16) showed the least amount of change over time. CONCLUSIONS: AutoNRT thresholds demonstrated significant change over time, limiting the ability to use intraoperatively recorded ECAP thresholds to predict postoperative measurements. In this study, electrodes 11 and 16 showed the least amount of change in ECAP threshold over time and therefore would be the best choices for estimating postoperative ECAP thresholds. Although not an ideal solution, mid-array ECAP thresholds obtained intraoperatively may prove to be helpful in creating a first MAP when no other behavioral or electrophysiological data are available.

Use of an expert concept map as an advance organizer to improve understanding of respiratory failure.

BACKGROUND: Helping novices transition toward expertise requires "meaningful" learning. Advance organizers are educational tools which help connect prior knowledge with new information, a critical step in making learning meaningful. Concept maps visually represent knowledge organization and can serve as advance organizers enabling deeper and more meaningful learning while enhancing knowledge integration. AIM: To compare respiratory failure understanding of resident physicians instructed, using an expert concept map advance organizer with learners receiving traditional didactic teaching. METHODS: Residents were randomized by month of service to receive either a control lecture or a session using an expert concept map as an advanced organizer. Participants completed three concept maps; pre-education (CM1), immediately post-education (CM2), and 1 week later (CM3). Concept maps were scored using a standardized structural scoring method. RESULTS: Forty-six pediatric residents (23 control and 23 experimental) participated. To account for repeated measures within subjects, the generalized estimating equations method compared concept map improvement between groups. The experimental group improved significantly more than controls (CM1-CM2-CM3 p = 0.001; CM1-CM2 p = 0.001; and CM1-CM3 p = 0.017). CONCLUSIONS: Using an expert concept map as an advance organizer improves knowledge organization and integration while offering a tool to enhance deeper understanding of medical knowledge among resident physicians.

Use of diagnostic decision support systems in medical education.

BACKGROUND: Diagnostic decision support systems are designed to assist physicians with making diagnoses. This article illustrates some of the issues that will be faced as diagnostic decision support systems become used in medical education. OBJECTIVES: The objectives of this article are to examine 1) the skills that are needed to properly use these programs as part of the students' clinical experiences; 2) the changes that will be necessary in our curricula once these programs are more extensively utilized, including the implications of using these systems as an educational resource or simulation tool, and 3) the research issues that arise when these systems become an established part of our educational programs. METHODS: This is a critical analysis of the literature on diagnostic decision support systems and medical education. RESULTS: To optimally use diagnostic decision support programs, students will need grounding in the basic knowledge and skills that have always been necessary to become a physician, such as the ability to accurately gather and interpret clinical information from the patient. In addition, students will need specific skills in 1) selecting appropriate system vocabulary and functions, and 2) applying the diagnostic system's suggestions to their particular patient. CONCLUSIONS: When computer-based decision support systems are incorporated in medical education, they will likely lead to changes in the traditional medical curriculum. Research will be needed on how use of these programs changes the students' knowledge, problem-solving and information-seeking skills.

Understanding ethics guidelines using an internet-based expert system.

UNLABELLED: National and international guidelines outlining ethical conduct in research involving humans and animals have evolved into large and complex documents making the process of gaining ethics approval a complicated task for researchers in the area. Researchers, in particular those who are relatively new to the ethics approval process, can struggle to understand the parts of an ethics guideline that apply to their research and the nature of their ethical obligations to trial participants. With the scope of medical research likely to continue to expand in the future, it is clear that ethics guidelines will only increase in complexity and number. This paper describes one possible solution to the PROBLEM: the use of an internet-based expert system to intelligently and interactively distribute the information stored in ethics guidelines to individual researchers. This paper also details how one such system was designed and tested with respect to Australian medical research ethics guidelines.

Acceptability of a stage-matched expert system intervention for domestic violence offenders.

Most interventions for men who batter are standardized and "one-size-fits-all," neglecting individual differences in readiness to change. A multimedia expert system intervention based on the transtheoretical model (the "stage model") was developed as an adjunct to traditional court-mandated programs. The expert system assesses stage of change, decisional balance, self-efficacy, and processes of change and provides immediate individualized stage-matched feedback designed to increase readiness to end the violence. Fifty-eight male batterer intervention program clients were invited by agency staff to complete an expert system session and an evaluation of the program; 33 men were recruited at program intake and the remainder from ongoing groups. Responses to the intervention were very positive. For example, 87% of participants reported that they found the program to be easy to use, and 98% said it could probably or definitely help them change their attitudes or behaviors. Findings provide encouraging evidence of the acceptability of this stage-matched approach to intervention for domestic violence offenders.

Sistema inteligente para auxílio ao diagnóstico no monitoramento fetal eletrônico por análise de cardiotocografias / A computer aided diagnostic intelligent system for electronic fetal monitoring based on cardiotocographies

A análise acurada da frequência cardíaca fetal (FCF) correlacionada com as contrações uterinas permite diagnosticar, e consequentemente antecipar, diversos problemas relativos ao bem estar fetal e à preservação de sua vida. O presente trabalho apresenta os resultados de um sistema híbrido, baseado em regras determinísticas e em um módulo de inferência nebuloso do tipo Mamdani, para análise de sinais coletados através de exames denominados cardiotocografias (CTG). As variáveis analisadas são: o valor basal da FCF, suas variabilidades de curto e de longo prazo, acelerações transitórias e desacelerações, sendo estas classificadas por seu tipo e número de ocorrências. São utilizados dois modelos de classificação. A saída do sistema, em qualquer dos modelos, é um diagnóstico de primeiro nível baseado nestas variáveis de entrada. O sistema inteligente para auxílio ao diagnóstico no monitoramento fetal eletrônico por análise de cardiotocografias (SISCTG) foi desenvolvido na linguagem de scripts do programa MATLAB® v.7. O projeto conta também com uma parceria multi-institucional entre o Brasil e a Alemanha, envolvendo o Departamento de Engenharia de Teleinformática (DETI) da Universidade Federal do Ceará (UFC), a Maternidade-Escola Assis Chateaubriand (MEAC), a Technische Universitãt München e a empresa alemã Trium GmbH, que fornece a base de dados utilizada neste trabalho. Os resultados apresentados pelo SISCTG mostram-se promissores, com um índice de acertos (comparando-se os dois modelos utilizados) variando de 83% a 100%, de acordo com o tipo de diagnóstico. Isto permite projetar o aprimoramento deste sistema com novas variáveis de entrada (como a entropia aproximada da FCF e da sua variabilidade). A validação do sistema contou com especialistas brasileiros e alemães na área obstétrica.

The accurate analysis of the fetal heart rate (FHR) and its correlation with uterine contractions (UC) allow the diagnostic and the anticipation of many problems related to fetal distress and the preservation of its life. This paper presents the results of a hybrid system based on a set of deterministic rules and fuzzy inference system developed to analyze FHR and UC signals collected by cardiotocography (CTG) exams. The studied variables are basal FHR, short and long-term FHR variability, transitory accelerations and decelerations, these lasts classified by their type and number of occurrences. Two classification models are used. For both models, the system output is a first level diagnostic based on those input variables. The system is developed using the MATLAB® v.7 script language. The project is also supported by a multi-institutional agreement between Brazil and Germany, among the DETI (Departamento de Engenharia de Teleinformática of the Universidade Federal do Ceará), the MEAC (Maternidade-Escola Assis Chateaubriand), the TUM (Technische Universitãt München), and the Trium GmbH, a German company who supplied the database used in this project. The results are very promising with a diagnostic accuracy (considering the two models used) varying from 83% to 100%, according to the type of diagnostic. These results allow the projection of refinements of the proposed system, inserting new input variables (such as the approximate entropy of the FHR and its variability). The system validation methodology was based on the knowledge of Brazilian and German obstetricians.

The structure-based expert model of the mental disorders--a validation study.

BACKGROUND: In an earlier study, our research group presented an alternative approach to measuring knowledge about mental disorders by constructing a structure-based expert model of the ICD-10 mental disorders. This article presents a validation of this expert model by measuring the emergence of such knowledge structures in psychotherapy students. SAMPLING AND METHODS: The participants of a continuing education program in cognitive behavioral psychotherapy rated a selection of mental disorders based on their phenomenological similarity. The similarity judgments of each student were translated by nonmetric multidimensional scaling (NMDS) into a cognitive map. In a quasi-longitudinal section design, the maps of the students of the first to the fourth year of training were compared with each other and with an expert map (the expert model) of experienced therapists. RESULTS: The discrepancies of the trainee maps compared with each other and with the expert map significantly decreased with increasing training level. CONCLUSIONS: The convergence of the students' maps towards the expert model indicates that the structural knowledge about mental disorders of experienced therapists can also be found to be emerging in psychotherapy students. This finding supports the validity of the expert model and may reflect a general knowledge-structuring principle of the mental disorders. In spite of the statistical significance found, in view of the small number of participants in the third and fourth years of training, the results should be treated with caution and should be regarded as first indicators which need further confirmation.