Applications of Machine Learning in Palliative Care: A Systematic Review

Vu, Erwin; Steinmann, Nina; Schröder, Christina; Forster, R. E.; Aebersold, Daniel M.; Eychmüller, Steffen; Čihorić, Nikola; Hertler, Caroline; Windisch, Paul; Zwahlen, Daniel R.

doi:10.3390/cancers15051596

Cited by 11 publications

(9 citation statements)

References 44 publications

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“…Our study aligns with these findings, indicating that machine learning models may offer superior prognostic capabilities in oncology compared with traditional statistical methods, which are not as precise at predicting cancer prognosis. In the latest systematic review of ML in palliative care [ 48 ], Vu et al concluded that although ML in palliative care is often used to predict mortality, it is not restricted only to this purpose, as the recent literature in this domain shows the potentials of ML for other innovative use cases, e.g., for data annotation and predicting complications, as well. The authors also emphasized the need for more rigorous testing of the models to ensure their applicability in different clinical settings.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Prediction Model for Patient Survival Outcomes in Palliative Care Using Actigraphy Data and Clinical Information

Huang

Roy

Dhar

et al. 2023

Cancers

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(1) Background: Predicting the survival of patients in end-of-life care is crucial, and evaluating their performance status is a key factor in determining their likelihood of survival. However, the current traditional methods for predicting survival are limited due to their subjective nature. Wearable technology that provides continuous patient monitoring is a more favorable approach for predicting survival outcomes among palliative care patients. (2) Aims and objectives: In this study, we aimed to explore the potential of using deep learning (DL) model approaches to predict the survival outcomes of end-stage cancer patients. Furthermore, we also aimed to compare the accuracy of our proposed activity monitoring and survival prediction model with traditional prognostic tools, such as the Karnofsky Performance Scale (KPS) and the Palliative Performance Index (PPI). (3) Method: This study recruited 78 patients from the Taipei Medical University Hospital’s palliative care unit, with 66 (39 male and 27 female) patients eventually being included in our DL model for predicting their survival outcomes. (4) Results: The KPS and PPI demonstrated an overall accuracy of 0.833 and 0.615, respectively. In comparison, the actigraphy data exhibited a higher accuracy at 0.893, while the accuracy of the wearable data combined with clinical information was even better, at 0.924. (5) Conclusion: Our study highlights the significance of incorporating clinical data alongside wearable sensors to predict prognosis. Our findings suggest that 48 h of data is sufficient for accurate predictions. The integration of wearable technology and the prediction model in palliative care has the potential to improve decision making for healthcare providers and can provide better support for patients and their families. The outcomes of this study can possibly contribute to the development of personalized and patient-centered end-of-life care plans in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Deep Learning Prediction Model for Patient Survival Outcomes in Palliative Care Using Actigraphy Data and Clinical Information

Huang

Roy

Dhar

et al. 2023

Cancers

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“…For example, consider a risk prediction model being used to direct palliative care interventions. It is easier to train an ML-based tool to predict mortality, as a surrogate for palliative care needs, because mortality is less susceptible to measurement error and is available in palliative care medical records [ 58 , 59 ]. However, training an algorithm on mortality may not identify the individuals with high symptomatic or psychosocial needs who would benefit from palliative care the most.…”

Section: What Are the Factors To Consider In Using ML For Implementat...mentioning

confidence: 99%

Applying Machine Learning Techniques to Implementation Science

Huguet,

Chen,

Parikh

et al. 2024

Online J Public Health Inform

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Machine learning (ML) approaches could expand the usefulness and application of implementation science methods in clinical medicine and public health settings. The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, such as predicting what will work best, for whom, under what circumstances, and with what predicted level of support, and what and when adaptation or deimplementation are needed. We describe how ML approaches could be used and discuss challenges that implementation scientists and methodologists will need to consider when using ML throughout the stages of implementation.

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“…Chatbots should be designed to deliver information in a manner that is easily understandable to patients, using plain language and minimizing the use of medical jargon [ 57 ]. Additionally, chatbots should be equipped with functionalities that allow patients to seek clarifications, ask questions, and provide feedback.…”

Section: Ethical Considerations In the Utilization Of Chatbotsmentioning

confidence: 99%

Ethical Implications of Chatbot Utilization in Nephrology

Garcia Valencia,

Suppadungsuk,

Thongprayoon

et al. 2023

JPM

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This comprehensive critical review critically examines the ethical implications associated with integrating chatbots into nephrology, aiming to identify concerns, propose policies, and offer potential solutions. Acknowledging the transformative potential of chatbots in healthcare, responsible implementation guided by ethical considerations is of the utmost importance. The review underscores the significance of establishing robust guidelines for data collection, storage, and sharing to safeguard privacy and ensure data security. Future research should prioritize defining appropriate levels of data access, exploring anonymization techniques, and implementing encryption methods. Transparent data usage practices and obtaining informed consent are fundamental ethical considerations. Effective security measures, including encryption technologies and secure data transmission protocols, are indispensable for maintaining the confidentiality and integrity of patient data. To address potential biases and discrimination, the review suggests regular algorithm reviews, diversity strategies, and ongoing monitoring. Enhancing the clarity of chatbot capabilities, developing user-friendly interfaces, and establishing explicit consent procedures are essential for informed consent. Striking a balance between automation and human intervention is vital to preserve the doctor–patient relationship. Cultural sensitivity and multilingual support should be considered through chatbot training. To ensure ethical chatbot utilization in nephrology, it is imperative to prioritize the development of comprehensive ethical frameworks encompassing data handling, security, bias mitigation, informed consent, and collaboration. Continuous research and innovation in this field are crucial for maximizing the potential of chatbot technology and ultimately improving patient outcomes.

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Applications of Machine Learning in Palliative Care: A Systematic Review

Cited by 11 publications

References 44 publications

Deep Learning Prediction Model for Patient Survival Outcomes in Palliative Care Using Actigraphy Data and Clinical Information

Deep Learning Prediction Model for Patient Survival Outcomes in Palliative Care Using Actigraphy Data and Clinical Information

Applying Machine Learning Techniques to Implementation Science

Ethical Implications of Chatbot Utilization in Nephrology

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