Suchergebnisse

[EN] Palliative care is an alternative to standard care for gravely ill patients that has demonstrated many clinical benefits in cost-effective interventions. It is expected to grow in demand soon, so it is necessary to detect those patients who may benefit from these programs using a personalised objective criterion at the correct time. Our goal was to develop a responsive and minimalist web application embedding a 1-year mortality explainable predictive model to assess palliative care at bedside consultation. A 1-year mortality predictive model has been trained. We ranked the input variables and evaluated models with an increasing number of variables. We selected the model with the seven most relevant variables. Finally, we created a responsive, minimalist and explainable app to support bedside decision making for older palliative care. The selected variables are age, medication, Charlson, Barthel, urea, RDW-SD and metastatic tumour. The predictive model achieved an AUC ROC of 0.83 [CI: 0.82, 0.84]. A Shapley value graph was used for explainability. The app allows identifying patients in need of palliative care using the bad prognosis criterion, which can be a useful, easy and quick tool to support healthcare professionals in obtaining a fast recommendation in order to allocate health resources efficiently. ; This work was supported by the InAdvance project (H2020-SC1-BHC-2018-2020 grant agreement number 825750.) and the CANCERLEss project (H2020-SC1-2020-Single-Stage-RTD grant agreement number 965351), both funded by the European Union's Horizon 2020 research and innovation programme. ; Blanes-Selva, V.; Doñate-Martínez, A.; Linklater, G.; Garcés-Ferrer, J.; Garcia-Gomez, JM. (2021). Responsive and Minimalist App Based on Explainable AI to Assess Palliative Care Needs during Bedside Consultations on Older Patients. Sustainability. 13(17):1-11. https://doi.org/10.3390/su13179844 ; S ; 1 ; 11 ; 13 ; 17

International audience ; Introduction : More than 70% of world mortality is due to chronic conditions. Furthermore, it has been proven that social determinants have an enormous impact on both health-related behaviour and on the received attention from healthcare services. These determinants cause health inequalities. The objective of this study is to reduce the burden of chronic diseases in five European regions, hereby focusing on vulnerable populations, and to increase the sustainability of health systems by implementing a chronic disease self-management programme (CDSMP).Methods and analysis : 2000 people with chronic conditions or informal caregivers belonging to vulnerable populations, will be enrolled in the CDSMP in Spain, Italy, the UK, France and the Netherlands. Inclusion of patients will be based on geographical, socio economic and clinical stratification processes. The programme will be evaluated in terms of self-efficacy, quality of life and cost-effectiveness using a combination of validated questionnaires at baseline and 6 months from baseline.Ethics and dissemination : This study will follow the directives of the Helsinki Declaration and will adhere to the European Union General Data Protection Regulation. The project's activities, progress and outcomes will be disseminated via promotional materials, the use of mass media, online activities, presentations at events and scientific publications.