Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier (1706.04152v1)

Abstract: 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.

• The paper proposes a novel multitask Gaussian process RNN classifier to improve early detection of sepsis using patient physiological and medication data.
• Numerical results show the MGP-RNN framework outperforms conventional clinical scores, predicting sepsis about four hours earlier with higher sensitivity (0.85) and precision (0.64).
• This research has practical implications for enhancing clinical decision-making and patient outcomes by enabling timely intervention for sepsis and potential other adverse events.

Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier

The paper "Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier" explores a novel computational framework aimed at enhancing the early detection of sepsis in hospitalized patients. The authors propose a scalable end-to-end classifier utilizing sequence physiological and medication data to predict sepsis onset effectively, leveraging machine learning models, specifically multitask Gaussian processes (MGPs) and recurrent neural networks (RNNs).

Methodology and Model Architecture

The proposed model hinges on two primary components: the MGPs and RNNs. The MGPs are tasked with modeling the multivariate trajectories of continuous-valued physiological time series. The substantial uncertainty, frequent missing data, and irregular sampling rates associated with real clinical data are handled adeptly by the MGP, which interpolates the physiological variables to a uniform representation. This representation is then fed into the RNN, designed to accommodate the extreme variability in the length and complexity of patient encounters.

The RNN architecture is utilized to dynamically predict the probability of sepsis throughout the inpatient encounter. The integration of MGPs with RNNs allows for discriminative training of the model, enabling backpropagation through the Gaussian processes for efficient end-to-end learning.

Numerical Results and Comparison

The paper offers compelling numerical results illustrating the superiority of the MGP-RNN framework compared to conventional clinical scores such as NEWS, MEWS, and SIRS, as well as several machine learning baselines. Specifically, the proposed model shows a 19.4% improvement in the area under the Receiver Operating Characteristic (ROC) curve and a 55.5% enhancement in the area under the Precision-Recall (PR) curve against the existing NEWS score, highlighting its efficacy and robustness in predicting sepsis onset. Additionally, the MGP-RNN method can reliably anticipate sepsis approximately four hours before its clinical confirmation, with a sensitivity of 0.85 and precision of 0.64, thus providing actionable lead time for clinical intervention.

Implications and Future Prospects

The implications of this research are both practical and theoretical. Practically, deploying such a model in a clinical setting can lead to significant improvements in patient outcomes by enabling timely intervention, reducing false alarms, and minimizing clinical workloads associated with current scores. Theoretically, this work extends the utility of GP models in healthcare, specifically through effective representation learning from irregular clinical time series data.

Looking ahead, the model provides opportunities for extension and broader application. It may be adaptable for the prediction of other clinical adverse events by leveraging its strong representational capabilities. There are several avenues for future work, including incorporating dynamic response curves to drugs, refining the model’s covariance structure, and further optimization for reduced computational resources.

Overall, this paper contributes significantly to predictive healthcare applications, illustrating how sophisticated machine learning methods can enhance clinical decision-making processes and augment early warning systems with higher precision and lower false alarm rates.