We present a scalable end-to-end classifier that uses streaming physiological and medication data to accurately predict the onset of sepsis, a life-threatening complication from infections that has high mortality and morbidity. Our proposed framework models the multivariate trajectories of continuous-valued physiological time series using multitask Gaussian processes, seamlessly accounting for the high uncertainty, frequent missingness, and irregular sampling rates typically associated with real clinical data. The Gaussian process is directly connected to a black-box classifier that predicts whether a patient will become septic, chosen in our case to be a recurrent neural network to account for the extreme variability in the length of patient encounters. We show how to scale the computations associated with the Gaussian process in a manner so that the entire system can be discriminatively trained end-to-end using backpropagation. In a large cohort of heterogeneous inpatient encounters at our university health system we find that it outperforms several baselines at predicting sepsis, and yields 19.4 and 55.5 improved areas under the Receiver Operating Characteristic and Precision Recall curves as compared to the NEWS score currently used by our hospital.
There is a large body of works on the development and validation of early warning scores to predict clinical deterioration and other related outcomes. For instance, the MEWS score @cite_23 and NEWS score @cite_15 are two of the more common scores used to assess overall clinical deterioration. In addition, the SIRS score for systemic inflammatory response syndrome was part of the original clinical definition of sepsis @cite_8 , although other scores designed for sepsis such as SOFA @cite_24 and qSOFA @cite_5 have been more popular in recent years. A more sophisticated regression-based approach called the Rothman Index @cite_17 is also in widespread use for detecting overall deterioration. Finally, @cite_19 used a Cox regression to predict sepsis using clinical time series data, although they do not account for temporal structure since they simply create feature and event-time pairs from raw data.
@cite_8 @cite_24 @cite_19 @cite_23 @cite_5 @cite_15 @cite_17
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Sepsis is a leading cause of death in the United States, with mortality highest among patients who develop septic shock. Early aggressive treatment decreases morbidity and mortality. Although automated screening tools can detect patients currently experiencing severe sepsis and septic shock, none predict those at greatest risk of developing shock. We analyzed routinely available physiological and laboratory data from intensive care unit patients and developed TREWScore, a targeted real-time early warning score that predicts which patients will develop septic shock. TREWScore identified patients before the onset of septic shock with an area under the ROC (receiver operating characteristic) curve (AUC) of 0.83 [95 confidence interval (CI), 0.81 to 0.85]. At a specificity of 0.67, TREWScore achieved a sensitivity of 0.85 and identified patients a median of 28.2 [interquartile range (IQR), 10.6 to 94.2] hours before onset. Of those identified, two-thirds were identified before any sepsis-related organ dysfunction. In comparison, the Modified Early Warning Score, which has been used clinically for septic shock prediction, achieved a lower AUC of 0.73 (95 CI, 0.71 to 0.76). A routine screening protocol based on the presence of two of the systemic inflammatory response syndrome criteria, suspicion of infection, and either hypotension or hyperlactatemia achieved a lower sensitivity of 0.74 at a comparable specificity of 0.64. Continuous sampling of data from the electronic health records and calculation of TREWScore may allow clinicians to identify patients at risk for septic shock and provide earlier interventions that would prevent or mitigate the associated morbidity and mortality. Objective. To develop customized versions of the Simplified Acute Physiology Score II (SAPS II) and the 24-hour Mortality Probability Model II (MPM II24) to estimate the probability of mortality for intensive care unit patients with early severe sepsis. Design and Setting. Logistic regression models developed for patients with severe sepsis in a database of adult medical and surgical intensive care units in 12 countries. Patients. Of 11 458 patients in the intensive care unit for at least 24 hours, 1130 had severe sepsis based on criteria of the American College of Chest Physicians and the Society of Critical Care Medicine (systemic inflammatory response syndrome in response to infection, plus hypotension, hypoperfusion, or multiple organ dysfunction). Results. In patients with severe sepsis, mortality was higher (48.0 vs 19.6 among other patients) and 28-day survival was lower. The customized SAPS II was well calibrated (P=.92 for the goodness-of-fit test) and discriminated well (area under the receiver operating characteristic [ROC] curve, 0.78). Performance in the validation sample was equally good (P=.85 for the goodness-of-fit test; area under the ROC curve, 0.79). The customized MPM II24was well calibrated (P=.92 for the goodness-of-fit test) and discriminated well (area under the ROC curve, 0.79). Performance in the validation sample was equally good (P=.52 for the goodness-of-fit test; area under the ROC curve, 0.75). The models are independent of each other; either can be used alone to estimate the probability of mortality of patients with severe sepsis. Conclusions. Customization provides a simple technique to apply existing models to a subgroup of patients. Accurately a An American College of Chest Physicians Society of Critical Care Medicine Consensus Conference was held in Northbrook in August 1991 with the goal of agreeing on a set of definitions that could be applied to patients with sepsis and its sequelae. New definitions were offered for some terms, while others were discarded. Broad definitions of sepsis and the systemic inflammatory response syndrome were proposed, along with detailed physiologic parameters by which a patient may be categorized. Definitions for severe sepsis, septic shock, hypotension, and multiple organ dysfunction syndrome were also offered. The use of severity scoring methods when dealing with septic patients was recommended as an adjunctive tool to assess mortality. Appropriate methods and applications for the use and testing of new therapies were recommended. The use of these terms and techniques should assist clinicians and researchers who deal with sepsis and its sequelae. Objective: To determine whether the MS Functional Composite (MSFC) can predict future disease progression in patients with relapsing remitting MS (RR-MS). Background: The MSFC was recommended by the Clinical Outcomes Assessment Task Force of the National MS Society as a new clinical outcome measure for clinical trials. The MSFC, which contains a test of walking speed, arm dexterity, and cognitive function, is expressed as a single score on a continuous scale. It was thought to offer improved reliability and responsiveness compared with traditional clinical MS outcome measures. The predictive value of MSFC scores in RR-MS has not been determined. Methods: The authors conducted a follow-up study of patients with RR-MS who participated in a phase III study of interferon -1a (AVONEX) to determine the predictive value of MSFC scores. MSFC scores were constructed from data obtained during the phase III trial. Patients were evaluated by neurologic and MRI examinations after an average interval of 8.1 years from the start of the clinical trial. The relationships between MSFC scores during the clinical trial and follow-up status were determined. Results: MSFC scores from the phase III clinical trial strongly predicted clinical and MRI status at the follow-up visit. Baseline MSFC scores, and change in MSFC score over 2 years correlated with both disability status and the severity of whole brain atrophy at follow-up. There were also significant correlations between MSFC scores during the clinical trial and patient-reported quality of life at follow-up. The correlation with whole brain atrophy at follow-up was stronger for baseline MSFC than for baseline EDSS. Conclusion: MSFC scores in patients 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. Purpose  The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach  ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets  a citation network based on an early paper on webometrics, and a selfcitation network based on the 19 most cited papers in the Journal of Documentation  using citation data taken from the Web of Knowledge database.Findings  ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... NNScore is a neural-network-based scoring function designed to aid the computational identification of small-molecule ligands. While the test cases included in the original NNScore article demonstrated the utility of the program, the application examples were limited. The purpose of the current work is to further confirm that neural-network scoring functions are effective, even when compared to the scoring functions of state-of-the-art docking programs, such as AutoDock, the most commonly cited program, and AutoDock Vina, thought to be two orders of magnitude faster. Aside from providing additional validation of the original NNScore function, we here present a second neural-network scoring function, NNScore 2.0. NNScore 2.0 considers many more binding characteristics when predicting affinity than does the original NNScore. The network output of NNScore 2.0 also differs from that of NNScore 1.0; rather than a binary classification of ligand potency, NNScore 2.0 provides a single estimate of the pKd. To fac...