Responsive and Minimalist App Based on Explainable AI to Assess Palliative Care Needs during Bedside Consultations on Older Patients

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 object...

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Bibliographic Details
Published in:Sustainability 2021-09, Vol.13 (17), p.9844
Main Authors: Blanes-Selva, Vicent, Doñate-Martínez, Ascensión, Linklater, Gordon, Garcés-Ferrer, Jorge, García-Gómez, Juan M.
Format: Article
Language:English
Subjects:
Applications programs
Approximation
Coronaviruses
COVID-19
Criteria
Decision making
Electronic health records
Industrialized nations
Length of stay
Machine learning
Metastases
Mortality
Palliative care
Patients
Prediction models
Professional ethics
Professionals
Quality of life
Sustainability
Tumors
Urea
Usability
Variables
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su13179844