A deep learning approach for sepsis monitoring via severity score estimation

Asuroglu, Tunc; Ogul, Hasan

doi:10.1016/j.cmpb.2020.105816

Cited by 36 publications

(29 citation statements)

References 34 publications

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“…As mentioned above, sepsis prediction on the ICU is an important and timely problem and an active area of research. Under these circumstances, it is not surprising that some approaches have been developed in parallel to this work [33][34][35][36][37][38][39][40][41][42][43]. It will be an exciting and important avenue for future work to benchmark all these approaches (including ours) against each other and to compare their performances on a unified and realistic set of sepsis labels, for instance, the ones we propose in this work.…”

Section: Parallel Work On Sepsis Predictionmentioning

confidence: 97%

MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis

Rosnati

Fortuin

2021

PLoS ONE

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With a mortality rate of 5.4 million lives worldwide every year and a healthcare cost of more than 16 billion dollars in the USA alone, sepsis is one of the leading causes of hospital mortality and an increasing concern in the ageing western world. Recently, medical and technological advances have helped re-define the illness criteria of this disease, which is otherwise poorly understood by the medical society. Together with the rise of widely accessible Electronic Health Records, the advances in data mining and complex nonlinear algorithms are a promising avenue for the early detection of sepsis. This work contributes to the research effort in the field of automated sepsis detection with an open-access labelling of the medical MIMIC-III data set. Moreover, we propose MGP-AttTCN: a joint multitask Gaussian Process and attention-based deep learning model to early predict the occurrence of sepsis in an interpretable manner. We show that our model outperforms the current state-of-the-art and present evidence that different labelling heuristics lead to discrepancies in task difficulty. For instance, when predicting sepsis five hours prior to onset on our new realistic labels, our proposed model achieves an area under the ROC curve of 0.660 and an area under the PR curve of 0.483, whereas the (less interpretable) previous state-of-the-art model (MGP-TCN) achieves 0.635 AUROC and 0.460 AUPR and the popular commercial InSight model achieves 0.490 AUROC and 0.359 AUPR.

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Section: Parallel Work On Sepsis Predictionmentioning

confidence: 97%

MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis

Rosnati

Fortuin

2021

PLoS ONE

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“…Machine learning methods have been considered a promising method for early warning of sepsis in the ICU. [6][7][8][9][10][11][12][13][14][15][16][17] Early diagnosis and timely management of septic patients can effectively improve prognosis. 29 However, sepsis may not be diagnosed in time in the clinic due to the doctor's shift and day-night shift of the medical staff.…”

Section: Discussionmentioning

confidence: 99%

“…Although the GBM structure is relatively simple, it outperforms artificial neural network models. We compared our models with other models trained on the same open-source database, MIMIC, using the Sepsis-3 criteria, and reported prediction results within 5 h before the onset of sepsis, including InSight, 8 AISE, 9 MGP-TCN, DTW-KNN, 10 MLA, 11 DSPA, 12 and MGP-AttTCN. 16 Table 1 shows that our models basically outperform the others.…”

Section: Performance On the Mimic-iii Datasetmentioning

confidence: 99%

“…2 According to this, many recent studies on sepsis prediction defined sepsis by Sequential Organ Failure Assessment (SOFA) and infection instead of Systemic Inflammatory Response Syndrome (SIRS). [8][9][10][11][12] Prospective studies have shown that implementation of machine learningbased sepsis prediction algorithms in hospitals can reduce in-hospital mortality and length of stay. 13,14 In addition, many machine learning models provide superior model performance at the cost of transparency and interpretability, which has become a barrier to clinical application.…”

Section: Introductionmentioning

confidence: 99%

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Transferability and interpretability of the sepsis prediction models in the intensive care unit

Chen

Lin

et al. 2021

Preprint

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Sepsis is an essential issue in critical care medicine, and early detection and intervention are key for survival. We established the sepsis early warning system based on a data integration platform that can be implemented in ICU. The sepsis early warning module can detect the onset of sepsis 5 hours proceeding, and the data integration platform integrates, standardizes, and stores information from different medical devices, making the inference of the early warning module possible. Our best early warning model got an AUC of 0.9833 in the task of detect sepsis in 4 hours proceeding on the open-source database. Our data integration platform has already been operational in a hospital for months.

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“…The better interpretability provided by the SHAP model can potentially further guide treatment and facilitate clinical decision-making. Furthermore, our model derived from the impacts of each variable could be used for individual prediction, which would be of clinical use for risk strati cation in ICU settings, which was impossible with previously proposed modi ed SOFA models[6, 13,[21][22][23].…”

Section: Discussionmentioning

confidence: 99%

A Time-Incorporated SOFA-Based Explainable Machine Learning Model for Mortality Prediction in Critically Ill Patients

Liu

Gao

Deng

et al. 2021

Preprint

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Background: Organ dysfunction (OD) assessment is essential in intensive care units (ICUs). However, no OD scoring system has so far considered the duration of OD, which is clinically relevant. This study aimed to develop and validate an ICU mortality prediction model based on the Sequential Organ Failure Assessment (SOFA) score, incorporating the time dimension with machine learning methods.Methods: Data from the eICU Collaborative Research Database and Medical Information Mart for Intensive Care (MIMIC) -III were mixed for model development, and the MIMIC-IV dataset and Nanjing Jinling Hospital Surgery ICU (SICU-JL) dataset were used for external testing. Adult patients in the ICUs for more than 72 hours were deemed eligible. The total SOFA score and individual scores were calculated every 12 hours for the first three days of ICU admission. Time-dimensional variables were derived from the consecutively recorded SOFA scores and individual scores for each organ. A modified SOFA model incorporating the time dimension (T-SOFA) was stepwise constructed to predict ICU mortality using multiple machine learning algorithms. The predictive performance was assessed with the area under the receiver operating characteristic curves (AUROC). Also, we utilized the SHapley Additive exPlanations (SHAP) algorithm for data visualization and model explainability.Results: We extracted a total of 66,709 ICU patients from the mixed datasets for model development and 15,423 patients for validation. The T-SOFA M3 that incorporated the time dimension features and age, using the XGBoost algorithm, significantly outperformed the original SOFA scores (AUROC 0.800 95% CI [0.787-0.813] vs. 0.693 95% CI [0.678-0.709], p<0.01) in the validation set. Good prediction performance was maintained for the T-SOFA M3 in test Set A and test Set B, with AUROC of 0.803, 95% CI[0.791-0.815], and 0.830, 95%CI [0.789-0.870], respectively. Significant contributors demonstrated by the SHAP analysis included total SOFA score, Respiration-score, CNS-score, age, Cardiovascular-score, and SOFA Organ dysfunction Unalleviated Time Index.Conclusions: A SOFA-based, time-incorporated prediction model was developed and validated by machine learning algorithms, showing satisfactory predictability and medical interpretability.

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A deep learning approach for sepsis monitoring via severity score estimation

Cited by 36 publications

References 34 publications

MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis

MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis

Transferability and interpretability of the sepsis prediction models in the intensive care unit

A Time-Incorporated SOFA-Based Explainable Machine Learning Model for Mortality Prediction in Critically Ill Patients

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