In critical care, intensivists are required to continuously monitor high dimensional vital signs and lab measurements to detect and diagnose acute patient conditions. This has always been a challenging task. In this study, we propose a novel self-correcting deep learning prediction approach to address this challenge. We focus on an example of the prediction of acute kidney injury (AKI). Compared with the existing models, our method has a number of distinct features: we utilized the accumulative data of patients in ICU; we developed a self-correcting mechanism that feeds errors from the previous predictions back into the network; we also proposed a regularization method that takes into account not only the model's prediction error on the label but also its estimation errors on the input data. This mechanism is applied in both regression and classification tasks. We compared the performance of our proposed method with the conventional deep learning models on two real-world clinical datasets and demonstrated that our proposed model constantly outperforms these baseline models. In particular, the proposed model achieved area under ROC curve at 0.893 on the MIMIC III dataset, and 0.871 on the Philips eICU dataset.
Background:A major problem in treating acute kidney injury (AKI) is that clinical criteria for recognition are markers of established kidney damage or impaired function; treatment before such damag... Acute Kidney Injury (AKI), the abrupt decline in kidney function due to temporary or permanent injury, is associated with increased mortality, morbidity, length of stay, and hospital cost. Sometimes, simple interventions such as medication review or hydration can prevent AKI. There is therefore interest in estimating risk of AKI at hospitalization. To gain insight into this task, we employ multilayer perceptron (MLP) and recurrent neural networks (RNNs) using serum creatinine (sCr) as a lone feature. We explore different feature input structures, including variable-length look-backs and a nested formulation for rehospitalized patients with previous sCr measurements. Experimental results show that the simplest model, MLP processing the sum of sCr, had best performance: AUROC 0.92 and AUPRC 0.70. Such a simple model could be easily integrated into an EHR. Preliminary results also suggest that inpatient data streams with missing outpatient measurements---common in the medical setting---might be best modeled with a tailored architecture. The risk of mortality associated with acute renal failure (ARF) after open-heart surgery continues to be distressingly high. Accurate prediction of ARF provides an opportunity to develop strategies for early diagnosis and treatment. The aim of this study was to develop a clinical score to predict postoperative ARF by incorporating the effect of all of its major risk factors. A total of 33,217 patients underwent open-heart surgery at the Cleveland Clinic Foundation (1993 to 2002). The primary outcome was ARF that required dialysis. The scoring model was developed in a randomly selected test set ( n = 15,838) and was validated on the remaining patients. Its predictive accuracy was compared by area under the receiver operating characteristic curve. The score ranges between 0 and 17 points. The ARF frequency at each score level in the validation set fell within the 95 confidence intervals (CI) of the corresponding frequency in the test set. Four risk categories of increasing severity (scores 0 to 2, 3 to 5, 6 to 8, and 9 to 13) were formed arbitrarily. The frequency of ARF across these categories in the test set ranged between 0.5 and 22.1 . The score was also valid in predicting ARF across all risk categories. The area under the receiver operating characteristic curve for the score in the test set was 0.81 (95 CI 0.78 to 0.83) and was similar to that in the validation set (0.82; 95 CI 0.80 to 0.85; P = 0.39). In conclusion, a score is valid and accurate in predicting ARF after open-heart surgery; along with increasing its clinical utility, the score can help in planning future clinical trials of ARF. Background— Renal insufficiency after coronary artery bypass graft (CABG) surgery is associated with increased short-term and long-term mortality. We hypothesized that preoperative patient characteristics could be used to predict the patient-specific risk of developing postoperative renal insufficiency. @PARASPLIT Methods and Results— Data were prospectively collected on 11 301 patients in northern New England who underwent isolated CABG surgery between 2001 and 2005. Based on National Kidney Foundation definitions, moderate renal insufficiency was defined as a GFR 12 000, prior CABG, congestive heart failure, peripheral vascular disease, diabetes, hypertension, and preoperative intraaortic balloon pump. The predictive model was significant with χ2 150.8, probability value <0.0001. The model discriminated well, ROC 0.72 (95 CI: 0.68 to 0.75). The model was well calibrated according to the Hosmer-Lemeshow test. @PARASPLIT Conclusions— We developed a robust prediction rule to assist clinicians in identifying patients with normal, or near normal, preoperative renal function who are at high risk of developing severe renal insufficiency. Physicians may be able to take steps to limit this adverse outcome and its associated increase in morbidity and mortality.  The data contained within the electronic health record (EHR) is “big” from the standpoint of volume, velocity, and variety. These circumstances and the pervasive trend towards EHR adoption have sparked interest in applying big data predictive analytic techniques to EHR data. Acute kidney injury (AKI) is a condition well suited to prediction and risk forecasting; not only does the consensus definition for AKI allow temporal anchoring of events, but no treatments exist once AKI develops, underscoring the importance of early identification and prevention. The Acute Dialysis Quality Initiative (ADQI) convened a group of key opinion leaders and stakeholders to consider how best to approach AKI research and care in the “Big Data” era. This manuscript addresses the core elements of AKI risk prediction and outlines potential pathways and processes. We describe AKI prediction targets, feature selection, model development, and data display.