With the increasing aging population in China and rising incidence rates of diseases such as cancer and cardiovascular conditions, the demand for palliative care services continues to grow. In recent years, the integration of artificial intelligence and medical disciplines has advanced significantly, with machine learning research and applications in the palliative care field progressing steadily. This paper reviews the overview of machine learning, its applications in palliative care, and effectiveness evaluations. It also discusses existing limitations in current research and provides recommendations for future studies. The aim is to assist healthcare professionals in improving palliative care services and offer valuable insights for the development of palliative care in China.

Objective:To summarize the current status of the application of automated interactive voice response systems (AIVRS) in post-diagnosis management of chronic disease patients.Methods:A computer search was conducted in PubMed, Embase, Web of Science, CINAHL, Cochrane Library, China National Knowledge Infrastructure, Wanfang Data, China Biology Medicine disc, and VIP, covering the literature from establishing these databases to May 25, 2024. Two researchers screened the literature and extracted data based on inclusion and exclusion criteria.Results:A total of 16 articles were included. The content of AIVRS in post-diagnosis management of chronic disease patients mainly includes self-care guidance, medication management, and decision support. The outcome indicators primarily focus on feasibility evaluation and effect evaluation.Conclusions:AIVRS is feasible in post-diagnosis management of chronic disease patients. However, there are still shortcomings, such as technical limitations, performance issues, and the use of unimodal data to assess patient outcome indicators. Future research could strengthen the theoretical foundation of AIVRS in post-diagnosis management interventions, appropriately add interactive modules, and provide real-time guidance to improve the effectiveness of AIVRS in post-diagnosis management.

With the increasing aging population in China and rising incidence rates of diseases such as cancer and cardiovascular conditions, the demand for palliative care services continues to grow. In recent years, the integration of artificial intelligence and medical disciplines has advanced significantly, with machine learning research and applications in the palliative care field progressing steadily. This paper reviews the overview of machine learning, its applications in palliative care, and effectiveness evaluations. It also discusses existing limitations in current research and provides recommendations for future studies. The aim is to assist healthcare professionals in improving palliative care services and offer valuable insights for the development of palliative care in China.

Objective:To summarize the current status of the application of automated interactive voice response systems (AIVRS) in post-diagnosis management of chronic disease patients.Methods:A computer search was conducted in PubMed, Embase, Web of Science, CINAHL, Cochrane Library, China National Knowledge Infrastructure, Wanfang Data, China Biology Medicine disc, and VIP, covering the literature from establishing these databases to May 25, 2024. Two researchers screened the literature and extracted data based on inclusion and exclusion criteria.Results:A total of 16 articles were included. The content of AIVRS in post-diagnosis management of chronic disease patients mainly includes self-care guidance, medication management, and decision support. The outcome indicators primarily focus on feasibility evaluation and effect evaluation.Conclusions:AIVRS is feasible in post-diagnosis management of chronic disease patients. However, there are still shortcomings, such as technical limitations, performance issues, and the use of unimodal data to assess patient outcome indicators. Future research could strengthen the theoretical foundation of AIVRS in post-diagnosis management interventions, appropriately add interactive modules, and provide real-time guidance to improve the effectiveness of AIVRS in post-diagnosis management.

Objective To systematically evaluate the self-management behavior transformation experience of cancer patients,providing a basis for the subsequent development of targeted self-management intervention measures.Methods Qualitative studies related to self-management behavior transformation experience of cancer patients from Cochrane Library,CINAHL,Embase,PubMed,Web of science,PsycINFO,and China National Knowledge Infras-tructure,VIP Database,Wanfang Database,and China Biomedical Literature Database were searched by computer,with a search deadline of July 2023.The literature quality evaluation was conducted using the qualitative research quality evaluation standards of the Evidence Based Health Care Center at the Joanna Briggs Institute in Australia,and the results were integrated using a pooled integration method.Results A total of 12 articles were included,and 50 research results were extracted and summarized into 14 new categories.4 integrated results were synthesized,including the forms of self-management behavior transformation,the characteristics of self-management behavior transformation,the driving factors of self-management behavior transformation,and the obstacles to self-management behavior transformation.Conclusion Medical staff should pay attention to the real experience of cancer patients'self-management behavior transformation.Targeted ability cultivation and psychological intervention can be carried out based on the characteristics and influencing factors of cancer patients'self-management behavior transformation,promoting their smooth transformation of self-management behavior.

Objective To observe the effect of preventive acupuncture and moxibustion at Guanyuan(CV 4)on abnormal activity in early climacteric rats.Methods Using four-month-old female SD rats as young control group,the 24 ten-month-old SD female rats screened by vaginal smear were randomized into 4 groups.The climacteric models by natural aging were established.The changes of estrogen receptor(ER-?)and heat shock protein-70(HSP-70)expressions resulted from the effect of preventive acupuncture and preventive moxibustion were observed.Results The expression of ER-? decreased gradually in the uterus of natural climacteric rats(P