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

被引:14
作者
Reddy, Varun [1 ]
Nafees, Abdulwadud [2 ]
Raman, Srinivas [1 ,2 ,3 ]
机构
[1] Princess Margaret Hosp, Radiat Med Program, Canc Ctr, Toronto, ON, Canada
[2] Univ Toronto, Dept Radiat Oncol, Toronto, ON, Canada
[3] Princess Margaret Canc Ctr, Dept Radiat Oncol, 610 Univ Ave, Toronto, ON M5G 2M9, Canada
关键词
artificial intelligence; deep learning; machine learning; natural language processing; palliative care; supportive care; MACHINE LEARNING-MODEL; BONE METASTASES; ENSEMBLE TREES; VALIDATION; SURVIVAL; PREDICTION; MORTALITY;
D O I
10.1097/SPC.0000000000000645
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Purpose of reviewArtificial intelligence (AI) is a transformative technology that has the potential to improve and augment the clinical workflow in supportive and palliative care (SPC). The objective of this study was to provide an overview of the recent studies applying AI to SPC in cancer patients. Recent findingsBetween 2020 and 2022, 29 relevant studies were identified and categorized into two applications: predictive modeling and text screening. Predictive modeling uses machine learning and/or deep learning algorithms to make predictions regarding clinical outcomes. Most studies focused on predicting short-term mortality risk or survival within 6 months, while others used models to predict complications in patients receiving treatment and forecast the need for SPC services. Text screening typically uses natural language processing (NLP) to identify specific keywords, phrases, or documents from patient notes. Various applications of NLP were found, including the classification of symptom severity, identifying patients without documentation related to advance care planning, and monitoring online support group chat data. 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 outcomes for cancer patients. Emerging data from prospective studies supports the clinical benefit of these tools; however, more rigorous clinical validation is required before AI is routinely adopted in the SPC clinical workflow.
引用
收藏
页码:125 / 134
页数:10
相关论文
共 58 条
[1]   Can online support groups address psychological morbidity of cancer patients? An artificial intelligence based investigation of prostate cancer trajectories [J].
Adikari, Achini ;
de Silva, Daswin ;
Ranasinghe, Weranja K. B. ;
Bandaragoda, Tharindu ;
Alahakoon, Oshadi ;
Persad, Raj ;
Lawrentschuk, Nathan ;
Alahakoon, Damminda ;
Bolton, Damien .
PLOS ONE, 2020, 15 (03)
[2]   Challenges of Developing a Natural Language Processing Method With Electronic Health Records to Identify Persons With Chronic Mobility Disability [J].
Agaronnik, Nicole D. ;
Lindvall, Charlotta ;
El-Jawahri, Areej ;
He, Wei ;
Iezzoni, Lisa I. .
ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION, 2020, 101 (10) :1739-1746
[3]   Developing an Improved Statistical Approach for Survival Estimation in Bone Metastases Management: The Bone Metastases Ensemble Trees for Survival (BMETS) Model [J].
Alcorn, Sara R. ;
Fiksel, Jacob ;
Wright, Jean L. ;
Elledge, Christen R. ;
Smith, Thomas J. ;
Perng, Powell ;
Saleemi, Sarah ;
McNutt, Todd R. ;
DeWeese, Theodore L. ;
Zeger, Scott .
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2020, 108 (03) :554-563
[4]   Investigating the challenges and barriers of palliative care delivery in Iran and the World: A systematic review study [J].
Amroud, Mohammad Salimi ;
Raeissi, Pouran ;
Hashemi, Seyed-Masoud ;
Reisi, Nahid ;
Ahmadi, Seyed-Ahmad .
JOURNAL OF EDUCATION AND HEALTH PROMOTION, 2021, 10 (01)
[5]   Application of machine learning techniques for predicting survival in ovarian cancer [J].
Azar, Amir Sorayaie ;
Rikan, Samin Babaei ;
Naemi, Amin ;
Mohasefi, Jamshid Bagherzadeh ;
Pirnejad, Habibollah ;
Mohasefi, Matin Bagherzadeh ;
Wiil, Uffe Kock .
BMC MEDICAL INFORMATICS AND DECISION MAKING, 2022, 22 (01)
[6]   Advance care planning in primary care: a retrospective medical record study among patients with different illness trajectories [J].
Bekker, Yvonne A. C. ;
Suntjens, Ankie F. ;
Engels, Y. ;
Schers, H. ;
Westert, Gert P. ;
Groenewoud, A. Stef .
BMC PALLIATIVE CARE, 2022, 21 (01)
[7]   Natural Language Processing to Assess Palliative Care and End-of-Life Process Measures in Patients With Breast Cancer With Leptomeningeal Disease [J].
Brizzi, Kate ;
Zupanc, Sophia N. ;
Udelsman, Brooks V. ;
Tulsky, James A. ;
Wright, Alexi A. ;
Poort, Hanneke ;
Lindvall, Charlotta .
AMERICAN JOURNAL OF HOSPICE & PALLIATIVE MEDICINE, 2020, 37 (05) :371-376
[8]   Automated medical chart review for breast cancer outcomes research: a novel natural language processing extraction system [J].
Chen, Yifu ;
Hao, Lucy ;
Zou, Vito Z. ;
Hollander, Zsuzsanna ;
Ng, Raymond T. ;
Isaac, Kathryn, V .
BMC MEDICAL RESEARCH METHODOLOGY, 2022, 22 (01)
[9]   The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation [J].
Chicco, Davide ;
Jurman, Giuseppe .
BMC GENOMICS, 2020, 21 (01)
[10]   A Machine Learning Model Approach to Risk-Stratify Patients With Gastrointestinal Cancer for Hospitalization and Mortality Outcomes [J].
Christopherson, Kaitlin M. ;
Das, Prajnan ;
Berlind, Christopher ;
Lindsay, W. David ;
Ahern, Christopher ;
Smith, Benjamin D. ;
Subbiah, Ishwaria M. ;
Koay, Eugene J. ;
Koong, Albert C. ;
Holliday, Emma B. ;
Ludmir, Ethan B. ;
Minsky, Bruce D. ;
Taniguchi, Cullen M. ;
Smith, Grace L. .
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2021, 111 (01) :135-142