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March 9, 2022Open Access

모잠비크와 나이지리아의 보건 시스템 수준에서 치료 중단의 greatest 위험에 처한 ART 고객을 식별하기 위한 기계 학습을 이용한 예측 분석

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Abstract

기계 학습 모델은 현재 분류 기술보다 우수한 성능을 보였으며, LTFU의 greatest 위험에 처한 환자들에게 건강 관리 자원을 더 잘 배분할 수 있는 잠재력을 보여주었습니다. 게다가, 모델은 성별 및 연령 그룹 전반에 걸쳐 동등한 성능을 보여 모델의 일반화 가능성과 넓은 적용 가능성을 지원했습니다.

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Authors

Jeni Stockman

Yes Technologies (United States)

Jonathan Friedman

Barnes-Jewish Hospital

Johnna Sundberg

Yes Technologies (United States)

Journals

JAIDS Journal of Acquired Immune Deficiency Syndromes

Actions

Institutions

United States Agency for International Development

Futures Group (United States)

Yes Technologies (United States)

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Stockman et al. (수요일)이 질문을 연구했습니다.

synapsesocial.com/papers/69d72e1efaf9bc6d3dbef39d — DOI: https://doi.org/10.1097/qai.0000000000002947

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United States Agency for International Development· 2011 · 1,262 citations
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Also consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

The United States President's Emergency Plan for AIDS Relief· 2012 · 122 citations
Aggregating Loss to Follow-Up Behaviour in People Living with HIV on ART: A Cluster Analysis Using Unsupervised Machine Learning Algorithm in R· 2020 · 18 citations
Scikit-learn: Machine Learning in Python· 2012 · 63,706 citations
United States Agency for International Development· 2011 · 1,262 citations
Transforming health policy through machine learning· 2018 · 66 citations

모잠비크와 나이지리아의 보건 시스템 수준에서 치료 중단의 greatest 위험에 처한 ART 고객을 식별하기 위한 기계 학습을 이용한 예측 분석

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion

Cite this study

Also consider

Also consider