DigiHEALTH: Suite of Digital Solutions for Long-Term Healthy and Active Aging.

The population in the world is aging dramatically, and therefore, the economic and social effort required to maintain the quality of life is being increased. Assistive technologies are progressively expanding and present great opportunities; however, given the sensitivity of health issues and the vulnerability of older adults, some considerations need to be considered. This paper presents DigiHEALTH, a suite of digital solutions for long-term healthy and active aging. It is the result of a fruitful trajectory of research in healthy aging where we have understood stakeholders' needs, defined the main suite properties (that would allow scalability and interoperability with health services), and codesigned a set of digital solutions by applying a continuous reflexive cycle. At the current stage of development, the digital suite presents eight digital solutions to carry out the following: (a) minimize digital barriers for older adults (authentication system based on face recognition and digital voice assistant), (b) facilitate active and healthy living (well-being assessment module, recommendation system, and personalized nutritional system), and (c) mitigate specific impairments (heart failure decompensation, mobility assessment and correction, and orofacial gesture trainer). The suite is available online and it includes specific details in terms of technology readiness level and specific conditions for usage and acquisition. This live website will be continually updated and enriched with more digital solutions and further experiences of collaboration.

PDSS: A Pharmacological Decision Support System for Diabetics Patients with COVID-19.

With the advent of SARS-CoV-2, several studies have shown that there is a higher mortality rate in patients with diabetes and, in some cases, it is one of the side effects of overcoming the disease. However, there is no clinical decision support tool or specific treatment protocols for these patients. To tackle this issue, in this paper we present a Pharmacological Decision Support System (PDSS) providing intelligent decision support for COVID-19 diabetic patient treatment selection, based on an analysis of risk factors with data from electronic medical records using Cox regression. The goal of the system is to create real world evidence including the ability to continuously learn to improve clinical practice and outcomes of diabetic patients with COVID-19.

Predicting Hospital Ward Admission from the Emergency Department: A Systematic Review.

BACKGROUND: The emergency department (ED) is often overburdened, due to the high influx of patients and limited availability of attending physicians. This situation highlights the need for improvement in the management of, and assistance provided in the ED. A key point for this purpose is the identification of patients with the highest risk, which can be achieved using machine learning predictive models. The objective of this study is to conduct a systematic review of predictive models used to detect ward admissions from the ED. The main targets of this review are the best predictive algorithms, their predictive capacity, the studies' quality, and the predictor variables. METHODS: This review is based on PRISMA methodology. The information has been searched in PubMed, Scopus and Google Scholar databases. Quality assessment has been performed using the QUIPS tool. RESULTS: Through the advanced search, a total of 367 articles were found, of which 14 were of interest that met the inclusion criteria. Logistic regression is the most used predictive model, achieving AUC values between 0.75-0.92. The two most used variables are the age and ED triage category. CONCLUSIONS: artificial intelligence models can contribute to improving the quality of care in the ED and reducing the burden on healthcare systems.

Prediction and Analysis of Heart Failure Decompensation Events Based on Telemonitored Data and Artificial Intelligence Methods.

Cardiovascular diseases are the leading cause of death globally, taking an estimated 17.9 million lives each year. Heart failure (HF) occurs when the heart is not able to pump enough blood to satisfy metabolic needs. People diagnosed with chronic HF may suffer from cardiac decompensation events (CDEs), which cause patients' worsening. Being able to intervene before decompensation occurs is the major challenge addressed in this study. The aim of this study is to exploit available patient data to develop an artificial intelligence (AI) model capable of predicting the risk of CDEs timely and accurately. Materials and Methods: The vital variables of patients (n = 488) diagnosed with chronic heart failure were monitored between 2014 and 2022. Several supervised classification models were trained with these monitoring data to predict CDEs, using clinicians' annotations as the gold standard. Feature extraction methods were applied to identify significant variables. Results: The XGBoost classifier achieved an AUC of 0.72 in the cross-validation process and 0.69 in the testing set. The most predictive physiological variables for CAE decompensations are weight gain, oxygen saturation in the final days, and heart rate. Additionally, the answers to questionnaires on wellbeing, orthopnoea, and ankles are strongly significant predictors.

Key Factors and AI-Based Risk Prediction of Malnutrition in Hospitalized Older Women.

The numerous consequences caused by malnutrition in hospitalized patients can worsen their quality of life. The aim of this study was to evaluate the prevalence of malnutrition on the elderly population, especially focusing on women, identify key factors and develop a malnutrition risk predictive model. The study group consisted of 493 older women admitted to the Asunción Klinika Hospital in the Basque Region (Spain). For this purpose, demographic, clinical, laboratory, and admission information was gathered. Correlations and multivariate analyses and the MNA-SF screening test-based risk of malnutrition were performed. Additionally, different predictive models designed using this information were compared. The estimated frequency of malnutrition among this population in the Basque Region (Spain) is 13.8%, while 41.8% is considered at risk of malnutrition, which is increased in women, with up to 16.4% with malnutrition and 47.5% at risk of malnutrition. Sixteen variables were used to develop a predictive model obtaining Area Under the Curve (AUC) values of 0.76. Elderly women assisted at home and with high scores of dependency were identified as a risk group, as well as patients admitted in internal medicine units, and in admissions with high severity.

Prediction of heart failure decompensations using artificial intelligence and machine learning techniques / Predicción de las descompensaciones de la insuficiencia cardíaca mediante técnicas de inteligencia artificial y aprendizaje automático

Abstract Introduction: Heart failure (HF) is a major concern in public health. We have used artificial intelligence to analyze information and improve patient outcomes. Method: An Observational, retrospective, and non-randomized study with patients enrolled in our telemonitoring program (May 2014-February 2018). We collected patients’ clinical data, telemonitoring transmissions, and HF decompensations. Results: A total of 240 patients were enrolled with a follow-up of 13.44 ± 8.65 months. During this interval, 527 HF decompensations in 148 different patients were detected. Significant weight increases, desaturation below 90% and perception of clinical worsening are good predictors of HF decompensation. We have built a predictive model applying machine learning (ML) techniques, obtaining the best results with the combination of "Weight + Ankle + well-being plus alerts of systolic and diastolic blood pressure, oxygen saturation, and heart rate." Conclusions: ML techniques are useful tools for the analysis of HF datasets and the creation of predictive models that improve the accuracy of the actual remote patient telemonitoring programs.

Resumen Introducción: La insuficiencia cardíaca (IC) es un motivo de gran preocupación en la salud pública. Hemos utilizado técnicas de aprendizaje automático para analizar información y mejorar los resultados. Métodos: Estudio observacional, retrospectivo y no aleatorizado, con los pacientes incluidos en el programa de telemonitorización de IC de nuestro centro desde mayo 2014 hasta febrero 2018. Se han analizado datos clínicos, transmisiones de telemonitorización y descompensaciones de IC. Resultados: 240 pacientes incluidos con un seguimiento de 13.44 ± 8.65 meses. En este intervalo se han detectado 527 descompensaciones de IC en 148 pacientes diferentes. Los aumentos significativos de peso, la desaturación inferior al 90% y la percepción de empeoramiento clínico, han resultado buenos predictores de la descompensación de IC. Hemos construido un modelo predictivo aplicando técnicas de aprendizaje automático obteniendo los mejores resultados con la combinación de "Peso + Edemas en EEII + empeoramiento clínico + alertas de tensión arterial sistólica y diastólica, saturación de oxígeno y frecuencia cardiaca". Conclusiones: Las técnicas de inteligencia artificial son herramientas útiles para el análisis de las bases de datos de IC y para la creación de modelos predictivos que mejoran la precisión de los programas de telemonitorización actuales.

Visualization System for the Impact of Context Open Data in Clinical Settings.

The usage of context open data, such as environmental data, socio-economic resources, etc., in clinical settings has been limited till now. But in recent years the number of studies that consider these factors is increasing. However, for clinicians, researchers or other stakeholders to be able to use this information in clinical practice, appropriate visualizations tools must be developed. At the same time, the data are generally displayed in formats that are difficult to interpret. The use of geolocalized visualizing tools through maps provides a better understanding in most cases. The proposed tool aims to contribute to the development of systems that can help to the exploitation of clinical and context open data, making them more easily interpretable.

Hospital Readmission Prediction via Keyword Extraction and Sentiment Analysis on Clinical Notes.

Unplanned hospital readmission is a problem that affects hospitals worldwide and is due to different factors. The identification of those factors can help determine which patients are at greater risk of hospital readmission for early intervention. Our end goal is to predict and identify patterns to (i) feed a decision support system for efficient management of patients and resources and (ii) detect patients at high risk of 30-days readmission enabling preventive actions to improve management of hospital discharges. This study aims to analyze whether natural language processing and specifically keyword extractions tools and sentiment analysis can support 30-days readmission prediction. Features extracted from medical history notes and discharge reports were used to train a Logistic Regression model. The resulting model obtains an AUC of 0.63 indicating that the sentiment polarity score of the discharge report and several of the extracted keywords are representative features to consider.

Shaping the Future of Digitally Enabled Health and Care.

People generally need more support as they grow older to maintain healthy and active lifestyles. Older people living with chronic conditions are particularly dependent on healthcare services. Yet, in an increasingly digital society, there is a danger that efforts to drive innovations in eHealth will neglect the needs of those who depend on healthcare the most-our ageing population. The SHAPES (Smart and Healthy Ageing through People Engaging in Supportive Systems) Innovation Action aims to create an open European digital platform that facilitates the provision of meaningful, holistic support to older people living independently. A pan-European pilot campaign will evaluate a catalogue of digital solutions hosted on the platform that have been specifically adapted for older people. 'Medicines control and optimisation' is one of seven themes being explored in the campaign and will investigate the impact of digital solutions that aim to optimise medicines use by way of fostering effective self-management, while facilitating timely intervention by clinicians based on remote monitoring and individualised risk assessments powered by artificial intelligence. If successful, the SHAPES Innovation Action will lead to a greater sense of self-sufficiency and empowerment in people living with chronic conditions as they grow older.

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.

Preliminary Qualitative and Quantitative Evaluation of DESIREE, a Decision Support Platform for the Management of Primary Breast Cancer Patients.

The DESIREE project has developed a platform offering several complementary therapeutic decision support systems (DSSs) to improve care quality for breast cancer patients. A first assessment of the system was carried out in close-to-real tumor boards (TBs). Fourteen TB sessions were organized corresponding to a total of 125 exploitable decisions previously made without the system and re-played with the system after a washout period in three pilot sites. Results show an overestimation of declared compliance with guidelines when not using the system as compared to measured compliance with the recommendations issued from the guideline-based DSS of DESIREE. After using the system, measured compliance rate of decisions with guidelines was significantly improved from 74.4% to 89.6%. Most of the changes in decisions when using the guideline-based DSS were associated with non-compliant decisions that became compliant. Qualitative analysis and interviews showed that despite maturity issues, clinicians found DESIREE DSSs innovative and promising.

Impact of environmental factors on heart failure decompensations.

AIMS: Heart failure (HF) is a clinical syndrome caused by a structural and/or functional cardiac abnormality, resulting in a reduced cardiac output and/or elevated intracardiac pressures at rest or during stress. This disease often causes decompensations, which may lead to hospital admissions, deteriorating patients' quality of life and causing an increment on the healthcare cost. Environmental exposure is an important but underappreciated risk factor contributing to the development and severity of cardiovascular diseases, such as HF. METHODS AND RESULTS: We used two different sets of data (January 2012 to August 2017): one related to the number of hospital admissions and the other one related to the environmental factors (weather and air quality). Admissions related data were grouped in weeks, and then two different studies were performed: (i) a univariate regression to determine whether the admissions may influence future hospitalizations prediction and (ii) a multivariate regression to determine the impact of environmental factors on admission rates. A total number of 8338 hospitalizations of 5343 different patients are available in this dataset, with a mean of 4.02 admissions per day. In European warm period (from June to October), there are significant less admissions than that in the cold period (from December to March), with a clear seasonality of admissions, because there is a similar pattern every year. Air temperature is the most significant environmental factor (r = -0.3794, P < 0.001) related to HF hospital admissions, showing an inversed correlation. Some other attributes, such as precipitation (r = 0.0795, P = 0.05), along with SO2 (precursor of acid rain) (r = 0.2692, P < 0.001) and NOX air (major air pollutant formed by combustion systems and motor vehicles) (r = 0.2196, P < 0.001) quality parameters, are also relevant. Humidity and PM10 parameters do not have significant correlations in this study (r = 0.0469 and r = -0.0485 respectively), neither relevant P-values (P = 0.238 and P = 0.324, respectively). CONCLUSIONS: Several environmental factors, such as weather temperature and precipitation, and major air pollutants, such as SO2 and NOX air, have an impact on the HF-related hospital admissions rate and, hence, on HF decompensations and patient's quality of life.

Gathering Real World Evidence Through the Evaluation of Decision History.

Clinical Practice Guidelines (CPGs) gather latest evidence-based results to guide and support clinicians over the decision-making process to provide best care. Nevertheless, clinical cases may be subject to some biases (understood as non-compliance with CPGs) that can lead to adapt care delivery. In this work an experience-based decision support leaning on the structuration of the Decisional Event concept for tracking and storing each clinical decision is presented. Moreover, a visual analytics tool is provided in order to facilitate the visualization of biases from guideline-based decision support and the identification and inclusion of real-world evidence into the reasoning process by augmenting the knowledge formalized in the implemented guidelines.

Architecture for a Multimodal and Domain-Independent Clinical Decision Support System Software Development Kit.

Digitalization of the decision-making process in healthcare has been promoted to improve clinical performance and patient outcomes. The implementation of Clinical Practice Guidelines (CPGs) using Clinical Decision Support Systems (CDSSs) is widely developed in order to achieve this purpose within clinical information systems. Nevertheless, due to several factors such as (i) incompleteness of CPG clinical knowledge, (ii) out-of-date contents, or (iii) knowledge gaps for specific clinical situations, guideline-based CDSSs may not completely satisfy clinical needs. The proposed architecture aims to cope with guideline knowledge gaps and pitfalls by harmonizing different modalities of decision support (i.e. guideline-based CDSSs, experience-based CDSSs, and data mining-based CDSSs) and information sources (i.e. CPGs and patient data) to provide the most complete, personalized, and up-to-date propositions to manage patients. We have developed a decisional event structure to retrieve all the information related to the decision-making process. This structure allows the tracking, computation, and evaluation of all the decisions made over time based on patient clinical outcomes. Finally, different user-friendly and easy-to-use authoring tools have been implemented within the proposed architecture to integrate the role of clinicians in the whole process of knowledge generation and validation. A use case based on Breast Cancer management is presented to illustrate the performance of the implemented architecture.

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.

Weighting Experience-Based Decision Support on the Basis of Clinical Outcomes' Assessment.

Technologies such as decision support systems are expected to help clinicians implement clinical practice guidelines (CPGs) with the aim of decreasing practice variations and improving clinical outcomes. However, if CPGs provide recommendations to improve patient care, they may fail to take into account actual clinical outcomes associated to the recommended treatment, such as adverse events or secondary effects. In this paper, we present a novel experience-based decision support approach applied to the management of breast cancer, the most commonly diagnosed cancer among women worldwide. Capitalizing on the clinical know-how of physicians and the modeling of patient's outcomes and toxicities in a computer interpretable way, we are able to discover new knowledge that helps improving patient-centered clinical care. This work is conducted within the EU Horizon 2020 project DESIREE.

Assessment of a personalized and distributed patient guidance system.

OBJECTIVES: The MobiGuide project aimed to establish a ubiquitous, user-friendly, patient-centered mobile decision-support system for patients and for their care providers, based on the continuous application of clinical guidelines and on semantically integrated electronic health records. Patients would be empowered by the system, which would enable them to lead their normal daily lives in their regular environment, while feeling safe, because their health state would be continuously monitored using mobile sensors and self-reporting of symptoms. When conditions occur that require medical attention, patients would be notified as to what they need to do, based on evidence-based guidelines, while their medical team would be informed appropriately, in parallel. We wanted to assess the system's feasibility and potential effects on patients and care providers in two different clinical domains. MATERIALS AND METHODS: We describe MobiGuide's architecture, which embodies these objectives. Our novel methodologies include a ubiquitous architecture, encompassing a knowledge elicitation process for parallel coordinated workflows for patients and care providers; the customization of computer-interpretable guidelines (CIGs) by secondary contexts affecting remote management and distributed decision-making; a mechanism for episodic, on demand projection of the relevant portions of CIGs from a centralized, backend decision-support system (DSS), to a local, mobile DSS, which continuously delivers the actual recommendations to the patient; shared decision-making that embodies patient preferences; semantic data integration; and patient and care provider notification services. MobiGuide has been implemented and assessed in a preliminary fashion in two domains: atrial fibrillation (AF), and gestational diabetes Mellitus (GDM). Ten AF patients used the AF MobiGuide system in Italy and 19 GDM patients used the GDM MobiGuide system in Spain. The evaluation of the MobiGuide system focused on patient and care providers' compliance to CIG recommendations and their satisfaction and quality of life. RESULTS: Our evaluation has demonstrated the system's capability for supporting distributed decision-making and its use by patients and clinicians. The results show that compliance of GDM patients to the most important monitoring targets - blood glucose levels (performance of four measurements a day: 0.87±0.11; measurement according to the recommended frequency of every day or twice a week: 0.99±0.03), ketonuria (0.98±0.03), and blood pressure (0.82±0.24) - was high in most GDM patients, while compliance of AF patients to the most important targets was quite high, considering the required ECG measurements (0.65±0.28) and blood-pressure measurements (0.75±1.33). This outcome was viewed by the clinicians as a major potential benefit of the system, and the patients have demonstrated that they are capable of self-monitoring - something that they had not experienced before. In addition, the system caused the clinicians managing the AF patients to change their diagnosis and subsequent treatment for two of the ten AF patients, and caused the clinicians managing the GDM patients to start insulin therapy earlier in two of the 19 patients, based on system's recommendations. Based on the end-of-study questionnaires, the sense of safety that the system has provided to the patients was its greatest asset. Analysis of the patients' quality of life (QoL) questionnaires for the AF patients was inconclusive, because while most patients reported an improvement in their quality of life in the EuroQoL questionnaire, most AF patients reported a deterioration in the AFEQT questionnaire. DISCUSSION: Feasibility and some of the potential benefits of an evidence-based distributed patient-guidance system were demonstrated in both clinical domains. The potential application of MobiGuide to other medical domains is supported by its standards-based patient health record with multiple electronic medical record linking capabilities, generic data insertion methods, generic medical knowledge representation and application methods, and the ability to communicate with a wide range of sensors. Future larger scale evaluations can assess the impact of such a system on clinical outcomes. CONCLUSION: MobiGuide's feasibility was demonstrated by a working prototype for the AF and GDM domains, which is usable by patients and clinicians, achieving high compliance to self-measurement recommendations, while enhancing the satisfaction of patients and care providers.

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.

An Ontology for Telemedicine Systems Resiliency to Technological Context Variations in Pervasive Healthcare.

Clinical data are crucial for any medical case to study and understand a patient's condition and to give the patient the best possible treatment. Pervasive healthcare systems apply information and communication technology to enable the usage of ubiquitous clinical data by authorized medical persons. However, quality of clinical data in these applications is, to a large extent, determined by the technological context of the patient. A technological context is characterized by potential technological disruptions that affect optimal functioning of technological resources. The clinical data based on input from these technological resources can therefore have quality degradations. If these degradations are not noticed, the use of this clinical data can lead to wrong treatment decisions, which potentially puts the patient's safety at risk. This paper presents an ontology that specifies the relation among technological context, quality of clinical data, and patient treatment. The presented ontology provides a formal way to represent the knowledge to specify the effect of technological context variations in the clinical data quality and the impact of the clinical data quality on a patient's treatment. Accordingly, this ontology is the foundation for a quality of data framework that enables the development of telemedicine systems that are capable of adapting the treatment when the quality of the clinical data degrades, and thus guaranteeing patients' safety even when technological context varies.