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

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

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

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

Байесовские сети доверия как вероятностная графическая модель для оценки медицинских рисков

Realization of medical risks leads to occurrence of adverse effects which negatively affect patient’s health; result in irrational use of human and economic recourses, economic losses. In the framework of system risk analysis medical risks is connected with uncertainty related to crucial impact of human factor to the medical system. The problem of medical risks assessment and decision making on different stages of patients’ health care support systems’ construction comes up. In the paper I provide a state-of-art analysis of Bayesian belief networks use for medical risk assessment and decision making under uncertainty support in particular in the framework of health care organizations’ risk management and insurance risk assessment.Реализация медицинских рисков приводит к возникновению нежелательных событий, которые характеризуются нанесением вреда здоровью пациентов, неэффективным использованием человеческих и экономических ресурсов, экономическим ущербом организации здравоохранения. В рамках системного подхода к анализу риска, медицинский риск связан с неопределенностью, которая описывается значительным влиянием человеческого фактора в медицинской системе. Стоит задача оценки медицинских рисков и построения систем поддержки принятия решения на различных этапах работы с пациентом. В статье рассмотрено современное состояние применения аппарата байесовских сетей доверия для оценки медицинского риска и поддержки принятия решений в медицинской диагностике и прогностике, в частности, в контексте риск-менеджмента медицинских организации и оценке страховых рисков

Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach

Background:Breast cancer is the most prevalent cancer in women in most countries of the world. Many computer aided diagnostic methods have been proposed, but there are few studies on quantitative discovery of probabilistic dependencies among breast cancer data features and identification of the contribution of each feature to breast cancer diagnosis. Methods:This study aims to fill this void by utilizing a Bayesian network (BN) modelling approach. A K2 learning algorithm and statistical computation methods are used to construct BN structure and assess the obtained BN model. The data used in this study were collected from a clinical ultrasound dataset derived from a Chinese local hospital and a fine-needle aspiration cytology (FNAC) dataset from UCI machine learning repository. Results: Our study suggested that, in terms of ultrasound data, cell shape is the most significant feature for breast cancer diagnosis, and the resistance index presents a strong probabilistic dependency on blood signals. With respect to FNAC data, bare nuclei are the most important discriminating feature of malignant and benign breast tumours, and uniformity of both cell size and cell shape are tightly interdependent. Contributions: The BN modelling approach can support clinicians in making diagnostic decisions based on the significant features identified by the model, especially when some other features are missing for specific patients. The approach is also applicable to other healthcare data analytics and data modelling for disease diagnosis