Revelations from a Machine Learning Analysis of the Most Downloaded Articles Published in Journal of Palliative Medicine 1999-2018.

Journal of palliative medicine(2023)

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摘要
The Journal of Palliative Medicine (JPM) is globally recognized as a leading interdisciplinary peer-reviewed palliative care journal providing balanced information that informs and improves the practice of palliative care. JPM shapes the values, integrity, and standards of the subspecialty of palliative medicine by what it chooses to publish. The global JPM readership chooses to download the articles that are of most relevance and utility to them. Utilizing machine learning methods, the top 100 most downloaded articles in JPM were analyzed to gain a better understanding of any latent trends and patterns in the topics between 1999 and 2018. The top five topic themes identified in the first decade were different from the ones identified in the second decade of publication. There is evidence of differentiation and maturation of the field in the context of comprehensive health care. Although noncancer serious illnesses have still not risen to the same prominence as cancer palliation, there is a directional quality to the emerging evidence as it pertains to cardiac, respiratory, neurological, renal, and other etiologies. Across both decades under study, there was persistent evidence of the importance of understanding and managing the mental health care needs of seriously ill patients and their families. A cause for concern is that the word "spirituality" was prominent in the first decade and was lacking in the second. Future palliative care clinical and research initiatives should focus on its development as an essential interprofessional and medical subspecialty germane to all types of serious illnesses and across all venues.
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artificial intelligence,latent Dirichlet allocation,machine learning methods,palliative care,topic modeling
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