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Ali T, Khan I, Simpson W, Prescott G, Townend J, Smith W, MacLeod A. Incidence and outcomes in acute kidney damage: a complete population-based research. J Am Soc Nephrol. 2007;18(4):1292–98.
Kellum JA, Lameire N, Group KAGW, et al. Diagnosis, analysis, and administration of acute kidney damage: a kdigo abstract (half 1). Crit Care. 2013;17(1):204.
Lameire N, Kellum JA, Group KAGW, et al. Contrast-induced acute kidney damage and renal help for acute kidney damage: A kdigo abstract (half 2). Crit Care. 2013;17(1):205.
Thakar CV, Christianson A, Freyberg R, Almenoff P, Render ML. Incidence and outcomes of acute kidney damage in intensive care models: A veterans administration research. Crit Care Med. 2009;37(9):2552–58.
Wang HE, Muntner P, Chertow GM, Warnock DG. Acute kidney damage and mortality in hospitalized sufferers. Am J Nephrol. 2012;35(4):349–55.
Geus HR, Betjes MG, Bakker J. Biomarkers for the prediction of acute kidney damage: a story evaluate on present standing and future challenges. Clin Kidney J. 2012;5(2):102–08.
Hernandez-Boussard T, Monda KL, Crespo BC, Riskin D. Real world proof in cardiovascular medication: making certain information validity in digital well being record-based research. J Am Med Inf Assoc. 2019;26(11):1189–94.
Devlin J, Chang M-W, Lee Okay, Toutanova Okay: Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 convention of the North American chapter of the affiliation for computational linguistics: Human language applied sciences, quantity 1 (lengthy and quick papers). 2019. p. 4171–86.
Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee Okay, Zettlemoyer L: Deep contextualized phrase representations. In: Proceedings of NAACL-HLT. 2018. p. 2227–37.
Howard J, Ruder S: Universal language mannequin fine-tuning for textual content classification. In: Proceedings of the 56th annual assembly of the affiliation for computational linguistics (quantity 1: Long papers). 2018. p. 328–39.
Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, Kang J. BioBERT: a pre-trained biomedical language illustration mannequin for biomedical textual content mining. Bioinformatics. 2019. https://doi.org/10.1093/bioinformatics/btz682.
Alsentzer E, Murphy J, Boag W, Weng W-H, Jindi D, Naumann T, McDermott M: Publicly obtainable scientific bert embeddings. In: Proceedings of the 2nd scientific pure language processing workshop. 2019. p. 72–78.
Zhou LZ, Yang XB, Guan Y, Xu X, Tan MT, Hou FF, Chen PY. Development and validation of a threat rating for prediction of acute kidney damage in sufferers with acute decompensated coronary heart failure: a potential cohort research in China. J Am Heart Assoc. 2016;5(11):004035.
O’Neal JB, Shaw AD, Billings FT. Acute kidney damage following cardiac surgical procedure: present understanding and future instructions. Crit Care. 2016;20(1):187.
Kim WH, Lee SM, Choi JW, Kim EH, Lee JH, Jung JW, Ahn JH, Sung KI, Kim CS, Cho HS. Simplified scientific threat rating to foretell acute kidney damage after aortic surgical procedure. J Cardiothorac And Vasc Anesth. 2013;27(6):1158–66.
Kate RJ, Perez RM, Mazumdar D, Pasupathy KS, Nilakantan V. Prediction and detection fashions for acute kidney damage in hospitalized older adults. Bmc Med Inform Decis. 2016;16(1):39.
Sanchez-Pinto LN, Khemani RG. Development of a prediction mannequin of early acute kidney damage in critically sick youngsters utilizing digital well being file information. Pediatr Crit Care Me. 2016;17(6):508–15.
Perazella MA. The urine sediment as a biomarker of kidney illness. Am J Kidney Dis. 2015;66(5):748–55.
Kerr KF, Meisner A, Thiessen-Philbrook H, Coca SG, Parikh CR. Developing threat prediction fashions for kidney damage and assessing incremental worth for novel biomarkers. Clin J Am Soc Of Nephrol. 2014;9(8):1488–96.
Colpaert Okay, Hoste EA, Steurbaut Okay, Benoit D, Van Hoecke S, De Turck F, Decruyenaere J. Impact of real-time digital alerting of acute kidney damage on therapeutic intervention and development of rifle class. Crit Care Med. 2012;40(4):1164–70.
Li Y, Yao L, Mao C, Srivastava A, Jiang X, Luo Y: Early prediction of acute kidney damage in essential care setting utilizing scientific notes. In: 2018 IEEE worldwide convention on bioinformatics and biomedicine (BIBM). IEEE; 2018. p. 683–86.
Tomašev N, Glorot X, Rae JW, Zielinski M, Askham H, Saraiva A, Mottram A, Meyer C, Ravuri S, Protsyuk I, et al. A clinically relevant method to steady prediction of future acute kidney damage. Nature. 2019;572(7767):116–19.
Sun M, Baron J, Dighe A, Szolovits P, Luo Y: Early prediction of acute kidney damage in essential care setting utilizing scientific notes and structured multivariate physiological measurements. In: The seventeenth world Congress of medical and well being informatics (MedInfo 2019). 2019.
Wang S, Manning CD: Baselines and bigrams: Simple, good sentiment and subject classification. In: Proceedings of the fiftieth annual assembly of the affiliation for computational linguistics: Short papers-volume 2. Association for Computational Linguistics; 2012. p. 90–94.
Chenthamarakshan V, Melville P, Sindhwani V, Lawrence RD: Concept labeling: constructing textual content classifiers with minimal supervision. In: Twenty-second worldwide joint convention on synthetic intelligence. 2011.
Rousseau F, Kiagias E, Vazirgiannis M. Text categorization as a graph classification downside. In: Proceedings of the 53rd annual assembly of the affiliation for computational linguistics and the seventh International joint convention on pure language processing (quantity 1: Long papers). 2015. p. 1702–12.
Skianis Okay, Rousseau F, Vazirgiannis M. Regularizing textual content categorization with clusters of phrases. In: Proceedings of the 2016 convention on empirical strategies in pure language processing. 2016. p. 1827–37.
Luo Y, Sohani AR, Hochberg EP, Szolovits P. Automatic lymphoma classification with sentence subgraph mining from pathology stories. J Am Med Inf Assoc. 2014;21(5):824–32.
Luo Y, Xin Y, Hochberg E, Joshi R, Uzuner O, Szolovits P. Subgraph augmented non-negative tensor factorization (santf) for modeling scientific narrative textual content. J Am Med Inf Assoc. 2015;22(5):1009–19.
Kim Y. Convolutional neural networks for sentence classification. In: Proceedings of the 2014 convention on Empirical Methods in Natural Language Processing (EMNLP). 2014. p. 1746–51.
Yao L, Mao C, Luo Y. Clinical textual content classification with rule-based options and knowledge-guided convolutional neural networks. Bmc Med Inform Decis. 2019;19(3):71.
Zhang X, Zhao J, LeCun Y. Character-level convolutional networks for textual content classification. In: Advances in neural info processing programs. 2015. p. 649–57.
Conneau A, Schwenk H, LeCun Y, Barrault L. Very deep convolutional networks for textual content classification. In: fifteenth convention of the European chapter of the affiliation for computational linguistics, EACL 2017. 2017. p. 1107–16. Association for Computational Linguistics (ACL).
Tai KS, Socher R, Manning CD. Improved semantic representations from tree-structured lengthy short-term reminiscence networks. In: Proceedings of the 53rd annual assembly of the affiliation for computational linguistics and the seventh International joint convention on pure language processing (quantity 1: Long papers). 2015. p. 1556–66.
Liu P, Qiu X, Huang X. Recurrent neural community for textual content classification with multi-task studying. In: Proceedings of the twenty-fifth worldwide joint convention on synthetic intelligence. 2016. p. 2873–79. AAAI Press.
Luo Y. Recurrent neural networks for classifying relations in scientific notes. J Biomed Inf. 2017;72:85–95.
Yao L, Mao C, Luo Y. Graph convolutional networks for textual content classification. Proc AAAI Conf on Artif Intel. 2019;33:7370–77.
Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E. Hierarchical consideration networks for doc classification. In: Proceedings of the 2016 convention of the North American chapter of the affiliation for computational linguistics: Human language applied sciences. 2016. p. 1480–89.
Wang Y, Huang M, Zhu X, Zhao L: Attention-based lstm for aspect-level sentiment classification. In: Proceedings of the 2016 convention on empirical strategies in pure language processing. 2016. p. 606–15.
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you want. In: Advances in neural info processing programs. 2017. p. 5998–6008.
Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y. Graph consideration networks. In: International convention on studying representations. 2018.
Shen D, Wang G, Wang W, Min MR, Su Q, Zhang Y, Li C, Henao R, Carin L. Baseline wants extra love: on easy word-embedding-based fashions and related pooling mechanisms. In: Proceedings of the 56th annual assembly of the affiliation for computational linguistics (quantity 1: Long papers). 2018. p. 440–50.
Joulin A, Grave E, Bojanowski P, Mikolov T. Bag of methods for environment friendly textual content classification. In: Proceedings of the fifteenth convention of the European chapter of the affiliation for computational linguistics: Volume 2, quick papers. 2017. p. 427–31.
Wang G, Li C, Wang W, Zhang Y, Shen D, Zhang X, Henao R, Carin L. Joint embedding of phrases and labels for textual content classification. In: Proceedings of the 56th annual assembly of the affiliation for computational linguistics (quantity 1: Long papers). 2018. p. 2321–31.
Mikolov T, Sutskever I, Chen Okay, Corrado GS, Dean J. Distributed representations of phrases and phrases and their compositionality. In: Advances in neural info processing programs. 2013. p. 3111–19.
Pennington J, Socher R, Manning C. Glove: international vectors for phrase illustration. In: Proceedings of the 2014 convention on Empirical Methods in Natural Language Processing (EMNLP). 2014. p. 1532–43.
Bojanowski P, Grave E, Joulin A, Mikolov T. Enriching phrase vectors with subword info. Trans Assoc Comput Linguist. 2017;5:135–46.
Si Y, Wang J, Xu H, Roberts Okay. Enhancing scientific idea extraction with contextual embedding. arXiv preprint arXiv:1902.08691. 2019.
Yao L, Mao C, Luo Y. Kg-bert. Bert for information graph completion. arXiv preprint arXiv:1909.03193. 2019.
Huang Okay, Altosaar J, Ranganath R. Clinicalbert: modeling scientific notes and predicting hospital readmission. arXiv preprint arXiv:1904.05342. 2019.
Yao L, Jin Z, Mao C, Zhang Y, Luo Y. Traditional Chinese medication scientific data classification with bert and area particular corpora. J Am Med Inf Assoc. 2019.
Rasmy L, Xiang Y, Xie Z, Tao C, Zhi D. Med-bert: pretrained contextualized embeddings on large-scale structured digital well being data for illness prediction. NPJ Digit Med. 2021;4(1):1–13.
Mao C, Xu J, Rasmussen L, Li Y, Adekkanattu P, Pacheco J, Bonakdarpour B, Vassar R, Shen L, Jiang G, et al. Ad-bert: Using pre-trained language mannequin to foretell the development from gentle cognitive impairment to alzheimer’s illness. J Biomed Inf. 2023;144:104442.
Group KDIGOKC-MW, et al. Kdigo scientific follow guideline for the prognosis, analysis, prevention, and remedy of persistent kidney disease-mineral and bone dysfunction (ckd-mbd). Kidney Int Suppl. 2009;1(113).
Wu Y, Schuster M, Chen Z, Le QV, Norouzi M, Macherey W, Krikun M, Cao Y, Gao Q, Macherey Okay, et al. Google’s neural machine translation system: bridging the hole between human and machine translation. arXiv preprint arXiv:1609.08144. 2016.
Zimmerman LP, Reyfman PA, Smith AD, Zeng Z, Kho A, Sanchez-Pinto LN, Luo Y. Early prediction of acute kidney damage following icu admission utilizing a multivariate panel of physiological measurements. Bmc Med Inform Decis. 2019;19(1):1–12.
Beltagy I, Peters ME, Cohan A. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150. 2020.
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