Prediction of High-Risk Donors for Kidney Discard and Nonrecovery Using Structured Donor Characteristics and Unstructured Donor Narratives
出版年份 2023 全文链接
标题
Prediction of High-Risk Donors for Kidney Discard and Nonrecovery Using Structured Donor Characteristics and Unstructured Donor Narratives
作者
关键词
-
出版物
JAMA Surgery
Volume -, Issue -, Pages -
出版商
American Medical Association (AMA)
发表日期
2023-11-01
DOI
10.1001/jamasurg.2023.4679
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Operationalizing and Implementing Pretrained, Large Artificial Intelligence Linguistic Models in the US Health Care System: Outlook of Generative Pretrained Transformer 3 (GPT-3) as a Service Model
- (2022) Emre Sezgin et al. JMIR Medical Informatics
- OPTN/SRTR 2020 Annual Data Report: Kidney
- (2022) K. L. Lentine et al. AMERICAN JOURNAL OF TRANSPLANTATION
- Use of unstructured text in prognostic clinical prediction models: a systematic review
- (2022) Tom M Seinen et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- Machine learning for the prediction of acute kidney injury in patients with sepsis
- (2022) Suru Yue et al. Journal of Translational Medicine
- Implications of Accumulated Cold Time for US Kidney Transplantation Offer Acceptance
- (2022) Masoud Barah et al. Clinical Journal of the American Society of Nephrology
- Machine learning‐supported interpretation of kidney graft elementary lesions in combination with clinical data
- (2022) Marc Labriffe et al. AMERICAN JOURNAL OF TRANSPLANTATION
- BioGPT: generative pre-trained transformer for biomedical text generation and mining
- (2022) Renqian Luo et al. BRIEFINGS IN BIOINFORMATICS
- Multimodal machine learning in precision health: A scoping review
- (2022) Adrienne Kline et al. npj Digital Medicine
- Automated En Masse Machine Learning Model Generation Shows Comparable Performance as Classic Regression Models for Predicting Delayed Graft Function in Renal Allografts
- (2021) Kuang-Yu Jen et al. TRANSPLANTATION
- Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction
- (2021) Rohan Khera et al. JAMA Cardiology
- Population Preferences for Performance and Explainability of Artificial Intelligence in Health Care: Choice-Based Conjoint Survey
- (2021) Thomas Ploug et al. JOURNAL OF MEDICAL INTERNET RESEARCH
- Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
- (2020) Julia Amann et al. BMC Medical Informatics and Decision Making
- Adapting Bidirectional Encoder Representations from Transformers (BERT) to Assess Clinical Semantic Textual Similarity: Algorithm Development and Validation Study
- (2020) Klaus Kades et al. JMIR Medical Informatics
- BioBERT: a pre-trained biomedical language representation model for biomedical text mining
- (2019) Jinhyuk Lee et al. BIOINFORMATICS
- Can donor narratives yield insights? A natural language processing proof of concept to facilitate kidney allocation
- (2019) Andrew M. Placona et al. AMERICAN JOURNAL OF TRANSPLANTATION
- Hard-to-place kidney offers: Donor- and system-level predictors of discard
- (2018) J. Reinier F. Narvaez et al. AMERICAN JOURNAL OF TRANSPLANTATION
- Prospective Validation of Prediction Model for Kidney Discard
- (2018) Sheng Zhou et al. TRANSPLANTATION
- Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
- (2018) Scott M. Lundberg et al. Nature Biomedical Engineering
- Predictors of Deceased Donor Kidney Discard in the United States
- (2017) Wesley J. Marrero et al. TRANSPLANTATION
- Improving Distribution Efficiency of Hard-to-Place Deceased Donor Kidneys: Predicting Probability of Discard or Delay
- (2010) A. B. Massie et al. AMERICAN JOURNAL OF TRANSPLANTATION
- Determinants of Discard of Expanded Criteria Donor Kidneys: Impact of Biopsy and Machine Perfusion
- (2008) R. S. Sung et al. AMERICAN JOURNAL OF TRANSPLANTATION
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