Behavioral anomaly detection system for the wellbeign assessment and lifestyle support of older people at home

The wellbeing assessment of older people is becoming crucial in today’s era of aging and home care in order to provide the best possible care. New technologies are being used to assist older people at home, which generates an extensive amount of health and wellbeing information. The application of artificial intelligence algorithms to this healthcare and wellbeing data can enhance patient care and provide support to professionals by reducing their cognitive load. These algorithms can detect anomalous physiological, physical, and cognitive conditions in older individuals, which can help to identify emergency situations, or the early detection of an emerging health condition. However, while there has been relevant research in the field of anomaly detection for various engineering applications, there is little knowledge about healthcare and wellbeing-related anomaly detection. To this end, in this article, we propose an innovative system for detecting behavioral anomalies for older people that are being monitored at home with the aim of improving their lifestyle and wellbeing as well as the early detection of any physical or cognitive conditionThis project has received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No 857159 SHAPES Project and from the Basque Government’s HAZITEK innovation program under Grant Agreement No ZL-2021/00025 SERWES Project

Personalized Nutritional Guidance System to Prevent Malnutrition in Pluripathological Older Patients

Malnutrition is a frequent problem in the elderly population, who usually is affected by one or more pathologies. The health status of these patients can get worsened if malnutrition is left untreated. Nutritional guidelines have been developed to fulfil the nutritional needs derived from certain pathologies, but still are not easy to use. Digital tools can help implement and use these guidelines in real clinical scenarios. Current solutions are designed around a single pathology or specific scenario, but the pluripathologic scenario presents a challenge when it comes to provide nutritional support. In this paper, we present an adaptative tool that provides personalized nutritional recommendations for pluripathological patients in an efficient way, and can be extended to include other pathologies.This study was supported by the grant ZL 2019/00647 NUTRIGEP from Eusko Jaurlaritza (Basque Government) and the European Union under the European Regional Development Fund (ERDF)

Implementing Guideline-based, Experience-based, and Case-based approaches to enrich decision support for the management of breast cancer patients in the DESIREE project

DESIREE is a European-funded project to improve the management of primary breast cancer. We have developed three decision support systems (DSSs), a guideline-based, an experience-based, and a case-based DSSs, resp. GL-DSS, EXP-DSS, and CB-DSS, that operate simultaneously to offer an enriched multi-modal decision support to clinicians. A breast cancer knowledge model has been built to describe within a common ontology the data model and the termino-ontological knowledge used for representing breast cancer patient cases. It allows for rule-based and subsumption-based reasoning in the GL-DSS to provide best patient-centered reconciled care plans. It also allows for using semantic similarity in the retrieval algorithm implemented in the CB-DSS. Rainbow boxes are used to display patient cases similar to a given query patient. This innovative visualization technique translates the question of deciding the most appropriate treatment into a question of deciding the colour dominance among boxes

Reconciliation of Multiple Guidelines for Decision Support: A case study on the multidisciplinary management of breast cancer within the DESIREE project

Breast cancer is the most common cancer among women. DESIREE is a European project which aims at developing web-based services for the management of primary breast cancer by multidisciplinary breast units (BUs). We describe the guideline-based decision support system (GL-DSS) of the project. Various breast cancer clinical practice guidelines (CPGs) have been selected to be concurrently applied to provide state-of-the-art patient-specific recommendations. The aim is to reconcile CPG recommendations with the objective of complementarity to enlarge the number of clinical situations covered by the GL-DSS. Input and output data exchange with the GL-DSS is performed using FHIR. We used a knowledge model of the domain as an ontology on which relies the reasoning process performed by rules that encode the selected CPGs. Semantic web tools were used, notably the Euler/EYE inference engine, to implement the GL-DSS. "Rainbow boxes" are a synthetic tabular display used to visualize the inferred recommendations

A quality-of-data aware mobile decision support system for patients with chronic illnesses

We present a mobile decision support system (mDSS) which runs on a patient Body Area Network consisting of a smartphone and a set of biosensors. Quality-of-Data (QoD) awareness in decision making is achieved by means of a component known as the Quality-of-Data Broker, which also runs on the smartphone. The QoD-aware mDSS collaborates with a more sophisticated decision support system running on a fixed back-end server in order to provide distributed decision support. This distributed decision support system has been implemented as part of a larger system developed during the European project MobiGuide. The MobiGuide system is a guideline-based Patient Guidance System designed to assist patients in the management of chronic illnesses. The system, including the QOD-aware mDSS, has been validated by clinicians and is being evaluated in patient pilots against two clinical guidelines

Case-based decision support system for breast cancer management

Breast cancer is identified as the most common type of cancer in women worldwide with 1.6 million women around the world diagnosed every year. This prompts many active areas of research in identifying better ways to prevent, detect, and treat breast cancer. DESIREE is a European Union funded project, which aims at developing a web-based software ecosystem for the multidisciplinary management of primary breast cancer. The development of an intelligent clinical decision support system offering various modalities of decision support is one of the key objectives of the project. This paper explores case-based reasoning as a problem solving paradigm and discusses the use of an explicit domain knowledge ontology in the development of a knowledge-intensive case-based decision support system for breast cancer management

Application of a conceptual framework for the modelling and execution of clinical guidelines as networks of concurrent processes

We present a conceptual framework for modelling clinical guidelines as networks of concurrent processes. This enables the guideline to be partitioned and distributed at run-time across a knowledge-based telemedicine system, which is distributed by definition but whose exact physical configuration can only be determined after design-time by considering, amongst other factors, the individual patient's needs. The framework was applied to model a clinical guideline for gestational diabetes mellitus and to derive a prototype that executes the guideline on a smartphone. The framework is shown to support the full development trajectory of a decision support system, including analysis, design and implementation

Status and recommendations of technological and data-driven innovations in cancer care:Focus group study

Background: The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized by four Horizon 2020 (H2020) European Union (EU)-funded projects: BOUNCE, CATCH ITN, DESIREE, and MyPal. The issues covered included patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, and interoperability of information and communication technology (ICT) platforms. A series of recommendations was provided as the complex landscape of data-driven technical innovation in cancer care was portrayed. Objective: This study aims to provide information on the current state of the art of technology and data-driven innovations for the management of cancer care through the work of four EU H2020-funded projects. Methods: Two international workshops on ICT in the management of cancer care were held, and several topics were identified through discussion among the participants. A focus group was formulated after the second workshop, in which the status of technological and data-driven cancer management as well as the challenges, opportunities, and recommendations in this area were collected and analyzed. Results: Technical and data-driven innovations provide promising tools for the management of cancer care. However, several challenges must be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, and trust. This paper analyzes these challenges, which can be opportunities for further research and practical implementation and can provide practical recommendations for future work. Conclusions: Technology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem, to fully benefit from these innovations

Quality of clinical data aware telemedicine systems

Healthcare services have been evolving continuously owing to new demands caused by demographic and lifestyle changes. The advancements in information and communication technology (ICT) have propelled the development of new healthcare systems that can fulfil these demands. One of the key developments in this field is in the form of telemedicine systems, which aims to reliably deliver remote healthcare to patients through information exchange across distances.\ud This research is conducted in the context of ambulatory patient treatment guidance where a patient can receive remote treatment that is compliant with the\ud current care-plan, without the presence of his/her doctor, with the aid of a\ud telemedicine system. In order to provide treatment guidance that complies with healthcare procedures, telemedicine systems may apply clinical guidelines.\ud Additionally, the ICT of these telemedicine systems enables the acquisition\ud of patient clinical data. Hence, the clinical guidelines, in combination with ubiquitously acquired patient clinical data, may result in a personalized\ud treatment guidance that will provide the necessary remote treatment to the patient without the need of a practitioner

Comparative study of algorithms for atrial fibrillation detection

Nine algorithms for Atrial Fibrillation (AF) detection were evaluated using the same protocol. The public databases MIT-BIH Arrhythmia and AF from PhysioNet were employed for the evaluation and comparison of these nine algorithms. Their performances were reported not only in terms of sensitivity (Se), specificity (Sp), positive predictive value (PPV), and error rate (Err), but also in terms of window length, input signal length, robustness to noise and computation time. These algorithms are based on the analysis of two characteristics observed in ECGs with AF: RR intervals Irregularity (RRI) and a chaotic atrial activity (AA). This AA can be analyzed in frequency (FA) and/or in time domain (P wave absence, PWA). Five algorithms were based on RRI; one algorithm was based only on AA; and the last three algorithms combined RRI with AA techniques.Ingeniería de TelecomunicaciónTelekomunikazio Ingeniaritz