Clinical Decision Support System Sonares

A decision support system SonaRes destined to guide and help the ultrasound operators is proposed and compared with the existing ones. The system is based on rules and images and can be used as a second opinion in the process of ultrasound examination

A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer׳s disease and mild cognitive impairment

AbstractPopulation aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer׳s Disease (AD), have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI). Bayesian networks are well-suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data-oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer׳s Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil). The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well-known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis

Информационные системы в ультразвуковом обследовании

Ultrasound examination is an effective and widespread diagnostic technique. However, its application does not always satisfy the expectations, because it is an operator-dependent method, manifesting itself in the quality of the obtained images as well as in the interpretation and description mode. To overcome these difficulties, information systems are developed, intended to diminish the subjective factors by offering assistance during the examination. A decision support system SonaRes designed to guide and help the ultrasound operators is proposed and compared with the existing ones. The system is based on rules and images and can be used as a second opinion in the process of ultrasound examination. The proposed system does not intend to replace completely the physician; it will simply offer him a second opinion. In all the cases, the user will receive all rules and judgments on the basis of which the decision was made. If the user doesn’t agree with the decision proposed by the system, his opinion will be sent to the expert group for examination.Ультразвуковое обследование с целью диагностики – эффективная и широко распространенная процедура. Все же ее применение не всегда оправдывает ожидания, встречая некоторые трудности, связанные с зависимостью от оператора, которая отражается на качестве полученных изображений, способе их описания и интерпретации, а также на способе интерпретации описания другим специалистом. Для преодоления этих недостатков разрабатываются информационные системы, целью которых является уменьшение влияния субьективных факторов путем оказания помощи в процессе обследования. Эти системы могут быть использованы в качестве второго мнения, помогая врачу-эхографисту в получении более качественных изображений, в процесе интерпретации полученных изображений, в формулировке заключений. В данной статье рассмотрены наиболее известные клинические системы поддержки принятия решений в ультразвуковом исследовании, в сравнении с системой SonaRes, разрабатываемой нами, предназначенной для поддержки в процессе обследования гепато-панкреато-билиарной зоны

Cardiac health risk stratification system (CHRiSS): A Bayesian-based decision support system for left ventricular assist device (LVAD) therapy

This study investigated the use of Bayesian Networks (BNs) for left ventricular assist device (LVAD) therapy; a treatment for end-stage heart failure that has been steadily growing in popularity over the past decade. Despite this growth, the number of LVAD implants performed annually remains a small fraction of the estimated population of patients who might benefit from this treatment. We believe that this demonstrates a need for an accurate stratification tool that can help identify LVAD candidates at the most appropriate point in the course of their disease. We derived BNs to predict mortality at five endpoints utilizing the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) database: containing over 12,000 total enrolled patients from 153 hospital sites, collected since 2006 to the present day, and consisting of approximately 230 pre-implant clinical variables. Synthetic minority oversampling technique (SMOTE) was employed to address the uneven proportion of patients with negative outcomes and to improve the performance of the models. The resulting accuracy and area under the ROC curve (%) for predicted mortality were 30 day: 94.9 and 92.5; 90 day: 84.2 and 73.9; 6 month: 78.2 and 70.6; 1 year: 73.1 and 70.6; and 2 years: 71.4 and 70.8. To foster the translation of these models to clinical practice, they have been incorporated into a web-based application, the Cardiac Health Risk Stratification System (CHRiSS). As clinical experience with LVAD therapy continues to grow, and additional data is collected, we aim to continually update these BN models to improve their accuracy and maintain their relevance. Ongoing work also aims to extend the BN models to predict the risk of adverse events post-LVAD implant as additional factors for consideration in decision making

Asistente de evaluación clínica para la estratificación de riesgo inferido cardiovascular basado en redes bayesianas

El propósito de éste trabajo es presentar el desarrollo de una herramienta de software orientada a la salud basada en Redes Bayesianas, la cual permite estratificar el riesgo de padecer algún evento cardiovascular.Presentado en el XII Workshop Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Bayesian networks for disease diagnosis: What are they, who has used them and how?

A Bayesian network (BN) is a probabilistic graph based on Bayes' theorem, used to show dependencies or cause-and-effect relationships between variables. They are widely applied in diagnostic processes since they allow the incorporation of medical knowledge to the model while expressing uncertainty in terms of probability. This systematic review presents the state of the art in the applications of BNs in medicine in general and in the diagnosis and prognosis of diseases in particular. Indexed articles from the last 40 years were included. The studies generally used the typical measures of diagnostic and prognostic accuracy: sensitivity, specificity, accuracy, precision, and the area under the ROC curve. Overall, we found that disease diagnosis and prognosis based on BNs can be successfully used to model complex medical problems that require reasoning under conditions of uncertainty.Comment: 22 pages, 5 figures, 1 table, Student PhD first pape

Medicine in words and numbers: a cross-sectional survey comparing probability assessment scales

Contains fulltext : 56355.pdf ( ) (Open Access)Background / In the complex domain of medical decision making, reasoning under uncertainty can benefit from supporting tools. Automated decision support tools often build upon mathematical models, such as Bayesian networks. These networks require probabilities which often have to be assessed by experts in the domain of application. Probability response scales can be used to support the assessment process. We compare assessments obtained with different types of response scale. Methods / General practitioners (GPs) gave assessments on and preferences for three different probability response scales: a numerical scale, a scale with only verbal labels, and a combined verbal-numerical scale we had designed ourselves. Standard analyses of variance were performed. Results / No differences in assessments over the three response scales were found. Preferences for type of scale differed: the less experienced GPs preferred the verbal scale, the most experienced preferred the numerical scale, with the groups in between having a preference for the combined verbal-numerical scale. Conclusion / We conclude that all three response scales are equally suitable for supporting probability assessment. The combined verbal-numerical scale is a good choice for aiding the process, since it offers numerical labels to those who prefer numbers and verbal labels to those who prefer words, and accommodates both more and less experienced professionals.8 p