Revisão Narrativa

Referenciação de Pacientes aos Cuidados Paliativos Usando Modelos Preditivos de Inteligência Artificial

Conteúdo principal do artigo

Abel Abejas
Mariana Paulino Ferreira de Castro
Palavras-chave:
Aprendizagem Automática, Cuidados Terminais, Cuidados Paliativos, Inteligência Artificial, Sistemas de Apoio a Decisões Clínicas

Resumo

A Inteligência Artificial trouxe a possibilidade de arquivar e processar quantidades enormes de informação, nomeadamente informação de saúde, sendo esta muito extensa e complexa. Os Cuidados Paliativos têm como objetivo fornecer apoio a pacientes com doenças graves, prolongadas, progressivas e incuráveis, procurando aliviar sintomas, melhorar a qualidade de vida e atender às necessidades emocionais e psicológicas dos pacientes e das suas famílias. A Inteligência Artificial tem o potencial de revolucionar a identificação e encaminhamento atempados de pacientes que beneficiariam de Cuidados Paliativos, por forma a discutir atempadamente com estes e com as suas famílias, as expetativas e perspetivas de fim de vida.


O acesso antecipado aos CP tem inúmeros benefícios, incluindo bem-estar emocional, melhores mecanismos de coping, redução de custos no tratamento, juntamente com menos hospitalizações e mais curtas.


Com esta revisão narrativa, pretendemos perceber se a utilização concomitante de modelos de Inteligência Artificial efetivamente tem benefícios para o paciente, melhorando a sua qualidade de vida.

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Revisão Narrativa

Referências

Busnatu S, Niculescu AG, Bolocan A, Petrescu GED, Paduraru DN, Nastasa I, Lupusoru M, Geanta M, Andronic O, Grumezescu AM, Martins H. Clinical Applications of Artificial Intelligence-An Updated Overview. J Clin Med. 2022;11:2265. doi: 10.3390/jcm11082265

World Health Organization. WHO Definition of Palliative care [Internet]. [cited 2023 Aug 16]. Available from: https://www.who.int/news-room/fact-sheets/detail/palliative-care

Wilson PM, Ramar P, Philpot LM, Soleimani J, Ebbert JO, Storlie CB, et al. Effect of an Artificial Intelligence Decision Support Tool on Palliative Care Referral in Hospitalized Patients: A Randomized Clinical Trial. J Pain Symptom Manage. 2023;66:24–32. doi: 10.1016/j.jpainsymman.2023.02.317.

Peruselli C, Panfilis L De, Gobber G, Melo M, Tanzi S. Intelligenza artificiale e cure palliative: Opportunità e limiti]. Recenti Prog Med. 2020;111:639–45.

de Hond AA, Leeuwenberg AM, Hooft L, Kant IM, Nijman SW, van Os HJ, et al. Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review. NPJ Digit Med. 2022;5(1):2. doi: 10.1038/s41746-021-00549-7.

Blanes-Selva V, Doñate-Martínez A, Linklater G, Garcés-Ferrer J, García-Gómez JM. Responsive and minimalist app based on explainable ai to assess palliative care needs during bedside consultations on older patients. Sustainability. 2021;13:17.

Oliveira T, Silva A, Satoh K, Julian V, Leão P, Novais P. Survivability prediction of colorectal cancer patients: A system with evolving features for continuous improvement. Sensors. 2018;18:2983. doi: 10.3390/s18092983.

Soltani M, Farahmand M, Pourghaderi AR. Machine learning-based demand forecasting in cancer palliative care home hospitalization. J Biomed Inform. 2022;130:104075. doi: 10.1016/j.jbi.2022.104075.

Rhodes RL, Kazi S, Xuan L, Amarasingham R, Halm EA. Initial development of a computer algorithm to identify patients with breast and lung cancer having poor prognosis in a safety net hospital. Am J Hosp Palliat Care. 2016;33:678-83. doi: 10.1177/1049909115591499.

Jung K, Sudat SEK, Kwon N, Stewart WF, Shah NH. Predicting need for advanced illness or palliative care in a primary care population using electronic health record data. J Biomed Inform. 2019;92:103115. doi: 10.1016/j.jbi.2019.103115.

Zhang H, Li Y, McConnell W. Predicting potential palliative care beneficiaries for health plans: A generalized machine learning pipeline. J Biomed Inform. 2021;123:103922. doi: 10.1016/j.jbi.2021.103922.

Gajra A, Zettler ME, Miller KA, Blau S, Venkateshwaran SS, Sridharan S, et al. Augmented intelligence to predict 30-day mortality in patients with cancer. Future Oncol. 2021;17:3797-807. doi: 10.2217/fon-2021-0302.

Pierce RP, Raithel S, Brandt L, Clary KW, Craig K. A comparison of models predicting one-year mortality at time of admission. J Pain Symptom Manage. 2022;63:e287–93. doi: 10.1016/j.jpainsymman.2021.11.006.

Parikh RB, Manz CR, Nelson MN, Evans CN, Regli SH, O’Connor N, et al. Clinician perspectives on machine learning prognostic algorithms in the routine care of patients with cancer: a qualitative study. Support Care Cancer. 2022;30:4363–72. oi: 10.1007/s00520-021-06774-w.

Kelly M, O’Brien KM, Lucey M, Clough-Gorr K, Hannigan A. Indicators for early assessment of palliative care in lung cancer patients: A population study using linked health data. BMC Palliat Care. 2018;17:37. doi: 10.1186/s12904-018-0285-5.

Chalkidis G, McPherson J, Beck A, Newman M, Yui S, Staes C. Development of a Machine Learning Model Using Limited Features to Predict 6-Month Mortality at Treatment Decision Points for Patients With Advanced Solid Tumors. JCO Clin Cancer Inform. 2022;6:e2100163. doi: 10.1200/CCI.21.00163.

Agarwal R, Domenico HJ, Balla SR, Byrne DW, Whisenant JG, Woods MC, et al. Palliative Care Exposure Relative to Predicted Risk of Six-Month Mortality in Hospitalized Adults. J Pain Symptom Manage. 2022;63:645-53. doi: 10.1016/j.jpainsymman.2022.01.013.

Courtright KR, Chivers C, Becker M, Regli SH, Pepper LC, Draugelis ME, et al. Electronic health record mortality prediction model for targeted palliative care among hospitalized medical patients: a pilot quasi-experimental study. J Gen Intern Med. 2019;34:1841–7. doi: 10.1007/s11606-019-05169-2.

Avati A, Jung K, Harman S, Downing L, Ng A, Shah NH. Improving palliative care with deep learning. BMC Med Inform Decis Mak. 2018;18:122. doi: 10.1186/s12911-018-0677-8.