A clinically applicable approach to continuous prediction of future acute kidney injury

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41. Additional file 6 of Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility
    Amitava Banerjee, Suliang Chen, Ghazaleh Fatemifar, Mohamad Zeina, Lumbers, R. Thomas, Johanna Mielke, Simrat K. Gill, Dipak Kotecha, Daniel F. Freitag, Spiros Denaxas et al. · 2021 · Figshare
42. Additional file 9 of Changing relative risk of clinical factors for hospital-acquired acute kidney injury across age groups: a retrospective cohort study
    Wu, Lijuan, Hu, Yong, Zhang, Xiangzhou, Chen, Weiqi, Yu, Alan S. L., John A. Kellum, Lemuel Russell Waitman, Liu, Mei · 2020 · Figshare
43. Additional file 5 of Changing relative risk of clinical factors for hospital-acquired acute kidney injury across age groups: a retrospective cohort study
    Wu, Lijuan, Yong Hu, Zhang, Xiangzhou, Chen, Weiqi, Yu, Alan S. L., Kellum, John A., Lemuel Russell Waitman, Liu, Mei · 2020 · Figshare
44. Additional file 6 of Interpretable recurrent neural network models for dynamic prediction of the extubation failure risk in patients with invasive mechanical ventilation in the intensive care unit
    Zeng, Zhixuan, Tang, Xianming, Liu, Yang, Zhengkun He, Xun Gong · 2022 · Figshare
45. Additional file 3 of Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility
    Amitava Banerjee, Suliang Chen, Ghazaleh Fatemifar, Mohamad Zeina, Lumbers, R. Thomas, Johanna Mielke, Simrat K. Gill, Dipak Kotecha, Daniel F. Freitag, Spiros Denaxas et al. · 2021 · Figshare
46. Additional file 8 of Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility
    Amitava Banerjee, Suliang Chen, Ghazaleh Fatemifar, Mohamad Zeina, Lumbers, R. Thomas, Johanna Mielke, Simrat K. Gill, Dipak Kotecha, Daniel F. Freitag, Spiros Denaxas et al. · 2021 · Figshare
47. Additional file 2 of Interpretable recurrent neural network models for dynamic prediction of the extubation failure risk in patients with invasive mechanical ventilation in the intensive care unit
    Zeng, Zhixuan, Tang, Xianming, Liu, Yang, Zhengkun He, Xun Gong · 2022 · Figshare
48. Additional file 1 of Ambiguous definitions for baseline serum creatinine affect acute kidney diagnosis at the emergency department
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49. Additional file 1 of Incidence of hospital-acquired acute kidney injury and trajectories of glomerular filtration rate in older adults
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50. Additional file 1 of Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy
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51. Additional file 7 of Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility
    Amitava Banerjee, Suliang Chen, Ghazaleh Fatemifar, Mohamad Zeina, Lumbers, R. Thomas, Johanna Mielke, Simrat K. Gill, Dipak Kotecha, Daniel F. Freitag, Spiros Denaxas et al. · 2021 · Figshare
52. Additional file 1 of How good is our diagnostic intuition? Clinician prediction of bacteremia in critically ill children
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53. Additional file 1 of Short-term prognostic models for severe acute kidney injury patients receiving prolonged intermittent renal replacement therapy based on machine learning
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54. Additional file 1 of Development and validation of the creatinine clearance predictor machine learning models in critically ill adults
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55. Additional file 3 of Changing relative risk of clinical factors for hospital-acquired acute kidney injury across age groups: a retrospective cohort study
    Wu, Lijuan, Yong Hu, Zhang, Xiangzhou, Chen, Weiqi, Yu, Alan S. L., John A. Kellum, Lemuel Russell Waitman, Liu, Mei · 2020 · Figshare
56. Additional file 1 of Interpretable recurrent neural network models for dynamic prediction of the extubation failure risk in patients with invasive mechanical ventilation in the intensive care unit
    Zeng, Zhixuan, Tang, Xianming, Liu, Yang, Zhengkun He, Xun Gong · 2022 · Figshare
57. Additional file 1 of Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care
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58. Additional file 5 of Interpretable recurrent neural network models for dynamic prediction of the extubation failure risk in patients with invasive mechanical ventilation in the intensive care unit
    Zeng, Zhixuan, Tang, Xianming, Liu, Yang, Zhengkun He, Xun Gong · 2022 · Figshare
59. Additional file 4 of Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility
    Amitava Banerjee, Suliang Chen, Ghazaleh Fatemifar, Mohamad Zeina, Lumbers, R. Thomas, Johanna Mielke, Simrat K. Gill, Dipak Kotecha, Daniel F. Freitag, Spiros Denaxas et al. · 2021 · Figshare
60. Additional file 1 of Modifiable risk factors of acute kidney injury after liver transplantation: a systematic review and meta-analysis
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62. Additional file 2 of Changing relative risk of clinical factors for hospital-acquired acute kidney injury across age groups: a retrospective cohort study
    Wu, Lijuan, Yong Hu, Zhang, Xiangzhou, Chen, Weiqi, Yu, Alan S. L., Kellum, John A., Lemuel Russell Waitman, Liu, Mei · 2020 · Figshare
63. Additional file 5 of Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility
    Amitava Banerjee, Suliang Chen, Ghazaleh Fatemifar, Mohamad Zeina, Lumbers, R. Thomas, Johanna Mielke, Simrat K. Gill, Dipak Kotecha, Daniel F. Freitag, Spiros Denaxas et al. · 2021 · Figshare
64. Additional file 1 of Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility
    Amitava Banerjee, Suliang Chen, Ghazaleh Fatemifar, Mohamad Zeina, Lumbers, R. Thomas, Johanna Mielke, Simrat K. Gill, Dipak Kotecha, Daniel F. Freitag, Spiros Denaxas et al. · 2021 · Figshare
65. Additional file 3 of Interpretable recurrent neural network models for dynamic prediction of the extubation failure risk in patients with invasive mechanical ventilation in the intensive care unit
    Zeng, Zhixuan, Tang, Xianming, Liu, Yang, Zhengkun He, Gong, Xun · 2022 · Figshare
66. Additional file 4 of Changing relative risk of clinical factors for hospital-acquired acute kidney injury across age groups: a retrospective cohort study
    Wu, Lijuan, Hu, Yong, Zhang, Xiangzhou, Chen, Weiqi, Yu, Alan S. L., John A. Kellum, Lemuel Russell Waitman, Liu, Mei · 2020 · Figshare
67. Additional file 1 of Deep learning algorithms for predicting renal replacement therapy initiation in CKD patients: a retrospective cohort study
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68. Additional file 2 of Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility
    Amitava Banerjee, Suliang Chen, Ghazaleh Fatemifar, Mohamad Zeina, Lumbers, R. Thomas, Johanna Mielke, Simrat K. Gill, Dipak Kotecha, Daniel F. Freitag, Spiros Denaxas et al. · 2021 · Figshare
69. Additional file 1 of Changing relative risk of clinical factors for hospital-acquired acute kidney injury across age groups: a retrospective cohort study
    Wu, Lijuan, Hu, Yong, Zhang, Xiangzhou, Chen, Weiqi, Yu, Alan S. L., John A. Kellum, Lemuel Russell Waitman, Liu, Mei · 2020 · Figshare
70. Additional file 1 of Serum uric acid, disease severity and outcomes in COVID-19
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71. Additional file 9 of Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility
    Amitava Banerjee, Suliang Chen, Ghazaleh Fatemifar, Mohamad Zeina, Lumbers, R. Thomas, Johanna Mielke, Simrat K. Gill, Dipak Kotecha, Daniel F. Freitag, Spiros Denaxas et al. · 2021 · Figshare
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73. Additional file 2 of Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy
    Zhang, Hanfei, Ying Wang, Wu, Shukun, Johnathan Ngo, Yunlin Feng, He, Xin, Zhang, Yingfeng, Xingwei Wu, Hong, Daqing · 2024 · Figshare
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75. Additional file 1 of Interpretable machine learning models for early prediction of acute kidney injury after cardiac surgery
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76. Additional file 4 of Interpretable recurrent neural network models for dynamic prediction of the extubation failure risk in patients with invasive mechanical ventilation in the intensive care unit
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78. Additional file 1 of Prediction of acute kidney injury after cardiac surgery: model development using a Chinese electronic health record dataset
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