Latent states extraction through Kalman Filter for the prediction of heart failure decompensation events

[EN] Cardiac function deterioration of heart failure patients is frequently manifested by the occurrence of decompensation events. One relevant step to adequately prevent cardiovascular status degradation is to predict decompensation episodes in order to allow preventive medical interventions.In this paper we introduce a methodology with the goal of finding onsets of worsening progressions from multiple physiological parameters which may have predictive value in decompensation events. The best performance was obtained for the model composed by only two features using a telemonitoring dataset (myHeart) with 41 patients. Results were achieved by applying leave-one-subject-out validation and correspond to a geometric mean of 83.67%. The obtained performance suggests that the methodology has the potential to be used in decision support solutions and assist in the prevention of this public health burden.The authors acknowledge the financial support of the international project Link (H2020-692023).Nunes, D.; Rocha, T.; Traver Salcedo, V.; Teixeira, C.; Ruano, M.; Paredes, S.; Carvalho, P.... (2019). Latent states extraction through Kalman Filter for the prediction of heart failure decompensation events. IEEE. 3947-3950. https://doi.org/10.1109/EMBC.2019.8857591S3947395

A Hyper-Solution Framework for SVM Classification: Application for Predicting Destabilizations in Chronic Heart Failure Patients

Support Vector Machines (SVMs) represent a powerful learning paradigm able to provide accurate and reliable decision functions in several application fields. In particular, they are really attractive for application in medical domain, where often a lack of knowledge exists. Kernel trick, on which SVMs are based, allows to map non-linearly separable data into potentially linearly separable one, according to the kernel function and its internal parameters value. During recent years non-parametric approaches have also been proposed for learning the most appropriate kernel, such as linear combination of basic kernels. Thus, SVMs classifiers may have several parameters to be tuned and their optimal values are usually difficult to be identified a-priori. Furthermore, combining different classifiers may reduce risk to perform errors on new unseen data. For such reasons, we present an hyper-solution framework for SVM classification, based on meta-heuristics, that searches for the most reliable hyper-classifier (SVM with a basic kernel, SVM with a combination of kernel, and ensemble of SVMs), and for its optimal configuration. We have applied the proposed framework on a critical and quite complex issue for the management of Chronic Heart Failure patient: the early detection of decompensation conditions. In fact, predicting new destabilizations in advance may reduce the burden of heart failure on the healthcare systems while improving quality of life of affected patients. Promising reliability has been obtained on 10-fold cross validation, proving our approach to be efficient and effective for an high-level analysis of clinical data

An intelligent risk detection model to improve decision efficiency in healthcare contexts: the case of paediatric congenital heart disease

Objectives: Healthcare is an information rich industry where successful outcomes require the processing of multi-spectral data and sound decision making. The exponential growth of data and big data issues coupled with a rapid increase of service demands in healthcare contexts today, requires a robust framework enabled by IT (information technology) solutions as well as real-time service handling in order to ensure superior decision making and successful healthcare outcomes. Such a context is appropriate for the application of a real time intelligent risk detection decision support systems using business analytics and data science technologies. To illustrate the power and potential of business analytics and data science technologies in healthcare decision making scenarios, the use of an Intelligent Risk Detection (IRD) Model is proffered for the context of Congenital Heart Disease (CHD) in children, an area which requires complex high risk decisions that need to be made expeditiously and accurately in order to ensure successful healthcare outcomes. The main aim of this research is reducing burden of complex surgeries in patients, their family and society through early detecting of surgical risk factors prior to surgery. The research question is: How can an intelligent risk detection (IRD) Model be developed in the healthcare contexts? Method: This study is exploratory in nature and endeavours to explore the main components, barriers, issues and requirement to design and develop an Intelligent Risk Detection framework to be applied to healthcare contexts. In this research a qualitative approach using an exemplar data site as a single case study is adapted to address research objectives and to answer the research question. Data collection is through semi-structured interviews, questionnaires, observation and the analysis of documents, files and data bases from the study site. After conducting the data collection phase thematic analysis is applied to analyse all collected qualitative data. Results: This study has a significant contribution to practice and theory; namely confirming a role for business analytics and data science technologies in healthcare contexts. Also, this research serves to demonstrate that the selection of risk detection, prediction by data mining tools as one of the data science techniques and then decision support are very important for decision making in the complex surgeries. IRD, in practice, can also be used as a training tool to train nurses and medical students to detect the CHD surgery risk factors and their impact on surgery outcomes. Moreover, it can also provide decision support to assist doctors to make better clinical and surgical decisions or at least provide a second opinion. Furthermore, IRD can be used as a knowledge sharing and information transferring tools between clinicians, between clinicians and patients or their families and also between patients with the other patients. In this study also main components, barriers, issues and requirement to design and develop an Intelligent Risk Detection solution are explored and a comprehensive real time Intelligent Risk Detection Model in the healthcare context is designed