Leveraging artificial intelligence/machine learning models to identify potential palliative care beneficiaries : a systematic review

Article indépendant

BRESSLER, Toby | SONG, Jiyoun | KAMALUMPUNDI, Vijayvardhan | CHAE, Sena | SONG, Hyunjin | TARK, Aluem

PURPOSE: The current review examined the application of artificial intelligence (AI) and machine learning (ML) techniques in palliative care, specifically focusing on models used to identify potential beneficiaries of palliative services among individuals with chronic and terminal illnesses. METHODS: A systematic review was conducted across four electronic databases. Five studies met inclusion criteria, all of which applied AI/ML models to predict outcomes relevant to palliative care, such as mortality or the need for services. RESULTS: Of 1,504 studies screened, five studies used supervised ML algorithms, whereas one used natural language processing with a deep learning model to identify potential palliative care candidates. The most common AI/ML algorithms included neural network-based models, logistic regression, and tree-based models. CONCLUSION: AI and ML models offer promising avenues for identifying palliative care beneficiaries. As AI continues to evolve, its potential to reshape palliative care through early identification is significant, providing opportunities for timely and targeted care interventions.

http://dx.doi.org/10.3928/00989134-20241210-01

Voir la revue «Journal of gerontological nursing, 51»

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