[HTML][HTML] Data preprocessing techniques for ai and machine learning readiness: Sco** review of wearable sensor data in cancer care

BL Ortiz, V Gupta, R Kumar, A Jalin, X Cao… - JMIR mHealth and …, 2024 - mhealth.jmir.org
Background: Wearable sensors are increasingly being explored in health care, including in
cancer care, for their potential in continuously monitoring patients. Despite their growing …

Applications of machine learning in palliative care: a systematic review

E Vu, N Steinmann, C Schröder, R Förster… - Cancers, 2023 - mdpi.com
Simple Summary To investigate the adoption of machine learning in palliative care research
and clinical practice, we systematically searched for published research papers on the topic …

Recent advances in artificial intelligence applications for supportive and palliative care in cancer patients

V Reddy, A Nafees, S Raman - Current Opinion in Supportive and …, 2023 - journals.lww.com
This literature review indicates that AI tools can be used to support SPC clinicians in
decision-making and reduce manual workload, leading to potentially improved care and …

[HTML][HTML] Evaluating the potential of machine learning and wearable devices in end-of-life care in predicting 7-day death events among patients with terminal cancer …

JH Liu, CY Shih, HL Huang, JK Peng, SY Cheng… - Journal of Medical …, 2023 - jmir.org
Background An accurate prediction of mortality in end-of-life care is crucial but presents
challenges. Existing prognostic tools demonstrate moderate performance in predicting …

[HTML][HTML] Enhanced Lung Cancer Survival Prediction Using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets

MR Salmanpour, A Gorji, A Mousavi, A Fathi Jouzdani… - Cancers, 2025 - mdpi.com
Objective: This study explores a semi-supervised learning (SSL), pseudo-labeled strategy
using diverse datasets such as head and neck cancer (HNCa) to enhance lung cancer …

[HTML][HTML] The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review

R Smits Serena, F Hinterwimmer… - JMIR mHealth and …, 2025 - mhealth.jmir.org
Background Artificial intelligence (AI) has already revolutionized the analysis of image, text,
and tabular data, bringing significant advances across many medical sectors. Now, by …

Maintaining high-touch in high-tech digital health monitoring and multi-omics prognostication: ethical, equity, and societal considerations in precision health for …

JN Viana, C Pilbeam, M Howard, B Scholz… - OMICS: A Journal of …, 2023 - liebertpub.com
Advances in digital health, systems biology, environmental monitoring, and artificial
intelligence (AI) continue to revolutionize health care, ushering a precision health future …

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

Y Huang, N Roy, E Dhar, U Upadhyay, MA Kabir… - Cancers, 2023 - mdpi.com
Simple Summary Palliative care is a vital aspect of healthcare that aims to improve the
quality of life for individuals battling life-threatening diseases, such as cancer. Our research …

Application of artificial intelligence in oncology nursing: a sco** review

T Zhou, Y Luo, J Li, H Zhang, Z Meng, W **ong… - Cancer …, 2022 - journals.lww.com
Background Artificial intelligence (AI) has been increasingly used in healthcare during the
last decade, and recent applications in oncology nursing have shown great potential in …

Prediction of Nursing Need Proxies Using Vital Signs and Biomarkers Data: Application of Deep Learning Models

Y Baek, K Han, E Jeon, HY Yoo - Journal of Clinical Nursing, 2024 - Wiley Online Library
Aim To develop deep learning models to predict nursing need proxies among hospitalised
patients and compare their predictive efficacy to that of a traditional regression model …