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Background <p>Renal recovery after acute kidney injury (AKI) significantly influences prognosis, clinical management, and resource allocation. We aimed to evaluate deep learning architectures using high-resolution intensive care unit (ICU) time-series data with explicit missingness handling to predict renal recovery within 7 days after acute kidney injury (AKI).</p> Methods <p>We conducted an ambispective cohort study of adult medical ICU patients with AKI admitted between December 2022 and February 2025, with temporal validation from March to September 2025. Hourly physiological, laboratory, ventilator, and medication data were extracted from the ICU electronic medical record. Long short-term memory (LSTM), gated recurrent unit (GRU), and Transformer were trained to predict 7-day renal recovery. Missing data were handled using last observation carried forward (LOCF), LOCF with time-gap encoding (LOCF-TG), and LOCF with time-gap encoding and masking (LOCD-TG-M). Model performance was assessed.</p> Results <p>Among 1,493 ICU admissions, 438 patients with AKI were included, of whom 162 (37%) experienced renal recovery. The temporal validation cohort included 108 patients. The Transformer with LOCF-TG-M achieved the highest performance (AUROC 0.97; F1 score 0.89; accuracy 0.91), followed by the GRU with LOCF-TG-M (AUROC 0.96). SHAP analysis identified key predictors, including lower AKI stage, sepsis-associated AKI, cardiorenal syndrome, higher urine output, lower shock index, lower serum potassium and blood urea nitrogen, a lower comorbidity, and indicators of respiratory and circulatory stability—such as a higher SpO₂/FiO₂ ratio, lower mean airway pressure, and higher dynamic compliance—were also important.</p> Conclusion <p>Transformer with LOCF-TG-M accurately predicted 7-day renal recovery in critically ill patients with AKI and identified clinically meaningful predictors to support individualized management.</p>

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Deep learning approaches for time series prediction of renal recovery in medical critically Ill patients with acute kidney injury: LSTM, GRU, and transformer models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月7日

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  • Anawat Ratchatorn,
  • Natdanai Ketdao,
  • Suphachoke Sonsilphong,
  • Donlaporn Triamwichanon,
  • Anupol Panitchote

摘要

Background

Renal recovery after acute kidney injury (AKI) significantly influences prognosis, clinical management, and resource allocation. We aimed to evaluate deep learning architectures using high-resolution intensive care unit (ICU) time-series data with explicit missingness handling to predict renal recovery within 7 days after acute kidney injury (AKI).

Methods

We conducted an ambispective cohort study of adult medical ICU patients with AKI admitted between December 2022 and February 2025, with temporal validation from March to September 2025. Hourly physiological, laboratory, ventilator, and medication data were extracted from the ICU electronic medical record. Long short-term memory (LSTM), gated recurrent unit (GRU), and Transformer were trained to predict 7-day renal recovery. Missing data were handled using last observation carried forward (LOCF), LOCF with time-gap encoding (LOCF-TG), and LOCF with time-gap encoding and masking (LOCD-TG-M). Model performance was assessed.

Results

Among 1,493 ICU admissions, 438 patients with AKI were included, of whom 162 (37%) experienced renal recovery. The temporal validation cohort included 108 patients. The Transformer with LOCF-TG-M achieved the highest performance (AUROC 0.97; F1 score 0.89; accuracy 0.91), followed by the GRU with LOCF-TG-M (AUROC 0.96). SHAP analysis identified key predictors, including lower AKI stage, sepsis-associated AKI, cardiorenal syndrome, higher urine output, lower shock index, lower serum potassium and blood urea nitrogen, a lower comorbidity, and indicators of respiratory and circulatory stability—such as a higher SpO₂/FiO₂ ratio, lower mean airway pressure, and higher dynamic compliance—were also important.

Conclusion

Transformer with LOCF-TG-M accurately predicted 7-day renal recovery in critically ill patients with AKI and identified clinically meaningful predictors to support individualized management.