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

Cited by (1,002)

Items 701–800 of 1,002

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6. A dual-channel deep learning approach to continuous prediction of acute kidney injury in the intensive care unit
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8. Acute Kidney Injury Provider and Survivor Education: Current and Emerging Tools
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11. Development and validation of a real-time risk prediction model for acute kidney injury in hospitalized pediatric patients
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16. Acute Kidney Injury Associated with the Concomitant Use of Vancomycin and Piperacillin-Tazobactam
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17. Leveraging artificial intelligence for early detection and prediction of acute kidney injury in clinical practice
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22. Predicting kidney injury after cardiac surgery with cardio-pulmonary bypass using machine learning
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27. Artificial Intelligence in Nephrology
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28. Prediction of intrapartum fever using continuously monitored vital signs and heart rate variability
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29. A Deep Learning Approach Incorporating Data Missing Mechanism in Predicting Acute Kidney Injury in ICU
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33. Exploring the Preprocessing of a Time-Series Healthcare Administrative Dataset on Deep Learning to Improve Prediction
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34. Development and Validation of a Clinical Prediction Model for Stages of Acute Kidney Injury in Critically Ill Patients
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35. Early diagnosis of sepsis-associated AKI: based on destruction-replenishment contrast-enhanced ultrasonography
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37. Prediction of gastrointestinal hemorrhage in cardiology inpatients using an interpretable XGBoost model
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38. Development and validation of a risk prediction model for perioperative acute kidney injury in non-cardiac and non-urological surgery patients: a retrospective cohort study
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39. Polypharmacy, renal insufficiency and elder care: a systematic review
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40. A learning pharmacy practice enabled by the pharmacists’ patient care process
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41. Privacy on Medical Big Data
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42. Clinical Application of Artificial Intelligence: Auto-Discerning the Effectiveness of Lidocaine Concentration Levels in Osteosarcoma Femoral Tumor Segment Resection
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43. Dialysis resource allocation in critical care: the impact of the COVID-19 pandemic and the promise of big data analytics
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44. Can artificial intelligence predict the need for acute renal replacement therapy among inpatients with acute kidney injury?
    Haresh Selvaskandan, Timothy Gaultney, Dan Heath, Scott Linfoot, Gang Xu · 2023 · Future Healthcare Journal
45. A Case Study on a Sustainable Framework for Ethically Aware Predictive Modeling
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46. A Recurrent Neural Network for the Prediction of Vital Sign Evolution and Sepsis in ICU
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47. Hidden in Plain Sight: Machine Learning in Acute Kidney Injury
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48. Embracing Disease Progression with a Learning System for Real World Evidence Discovery
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49. Towards Quantification of Bias in Machine Learning for Healthcare: A Case Study of Renal Failure Prediction
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50. Artificial Intelligence Assisted Early Warning System for Acute Kidney Injury Driven by Multi-Center ICU Database
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51. TzanckNet: A convolutional neural network to identify cells in the cytology of erosive-vesiculobullous diseases
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52. Machine learning to predict COVID-19 outcomes to facilitate decision making
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53. Interpretable Additive Recurrent Neural Networks For Multivariate Clinical Time Series
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54. Database Research in Acute Kidney Injury: Time to Take Stock?
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55. AKI Prediction Models in ICU: A Comparative Study (Preprint)
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