OnAI-Comp: An Online AI Experts Competing Framework for Early Sepsis Detection

Zhou, Anni; Raheem, Beyah; Kamaleswaran, Rishikesan

doi:10.1109/tcbb.2021.3122405

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…The ICU constitutes a data rich environment where monitoring of physiological parameters is performed continuously, and biomarkers are assessed with regular and close intervals in most patients. This can be exploited in model learning to improve predictions and physiological parameters have been shown to be both temporally and differentially expressed in septic ICU patients 27,28 . Yet only a small proportion of sepsis develop in the ICU and a major clinical benefit lies in identifying patients earlier in the disease trajectory before ICU admission 29,30 .…”

Section: Discussionmentioning

confidence: 99%

Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data

Valik

Ward

Tanushi

et al. 2023

Sci Rep

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Sepsis is a leading cause of mortality and early identification improves survival. With increasing digitalization of health care data automated sepsis prediction models hold promise to aid in prompt recognition. Most previous studies have focused on the intensive care unit (ICU) setting. Yet only a small proportion of sepsis develops in the ICU and there is an apparent clinical benefit to identify patients earlier in the disease trajectory. In this cohort of 82,852 hospital admissions and 8038 sepsis episodes classified according to the Sepsis-3 criteria, we demonstrate that a machine learned score can predict sepsis onset within 48 h using sparse routine electronic health record data outside the ICU. Our score was based on a causal probabilistic network model—SepsisFinder—which has similarities with clinical reasoning. A prediction was generated hourly on all admissions, providing a new variable was registered. Compared to the National Early Warning Score (NEWS2), which is an established method to identify sepsis, the SepsisFinder triggered earlier and had a higher area under receiver operating characteristic curve (AUROC) (0.950 vs. 0.872), as well as area under precision-recall curve (APR) (0.189 vs. 0.149). A machine learning comparator based on a gradient-boosting decision tree model had similar AUROC (0.949) and higher APR (0.239) than SepsisFinder but triggered later than both NEWS2 and SepsisFinder. The precision of SepsisFinder increased if screening was restricted to the earlier admission period and in episodes with bloodstream infection. Furthermore, the SepsisFinder signaled median 5.5 h prior to antibiotic administration. Identifying a high-risk population with this method could be used to tailor clinical interventions and improve patient care.

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Section: Discussionmentioning

confidence: 99%

Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data

Valik

Ward

Tanushi

et al. 2023

Sci Rep

View full text Add to dashboard Cite

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“…Machine Learning (ML) methods can reliably and robustly learn complex relationships between clinical data, and several research efforts towards using ML for the predictive modeling of medical diseases are underway [ 5 , 6 , 7 , 8 ]. Similar research efforts for predicting the early onset of ARDS are ongoing to improve clinical recognition of the syndrome [ 9 , 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome

et al. 2023

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Acute Respiratory Distress Syndrome (ARDS) is a severe lung injury with high mortality, primarily characterized by bilateral pulmonary opacities on chest radiographs and hypoxemia. In this work, we trained a convolutional neural network (CNN) model that can reliably identify bilateral opacities on routine chest X-ray images of critically ill patients. We propose this model as a tool to generate predictive alerts for possible ARDS cases, enabling early diagnosis. Our team created a unique dataset of 7800 single-view chest-X-ray images labeled for the presence of bilateral or unilateral pulmonary opacities, or ‘equivocal’ images, by three blinded clinicians. We used a novel training technique that enables the CNN to explicitly predict the ‘equivocal’ class using an uncertainty-aware label smoothing loss. We achieved an Area under the Receiver Operating Characteristic Curve (AUROC) of 0.82 (95% CI: 0.80, 0.85), a precision of 0.75 (95% CI: 0.73, 0.78), and a sensitivity of 0.76 (95% CI: 0.73, 0.78) on the internal test set while achieving an (AUROC) of 0.84 (95% CI: 0.81, 0.86), a precision of 0.73 (95% CI: 0.63, 0.69), and a sensitivity of 0.73 (95% CI: 0.70, 0.75) on an external validation set. Further, our results show that this approach improves the model calibration and diagnostic odds ratio of the hypothesized alert tool, making it ideal for clinical decision support systems.

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“…Furthermore, tremendous temporal variability has been previously suggested after acute injury (7); thus, the temporal stability of early biomarkers may be highly influenced by time to injury. To address this gap, a few studies have sampled the clinical and biological data at multiple time points to model the physiological manifestations early during sepsis to predict novel physiomarkers across the age range (8)(9)(10)(11)(12). Studies among pediatric patients have identified that temporal switching between endotypes is a common phenomenon, with an estimated 30% to 40% of patients demonstrating crossover across time.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Model Derived From Analysis of Time-Course Gene-Expression Datasets Reveals Temporally Stable Gene Markers Predictive of Sepsis Mortality

Huang,

Atreya,

Holder

et al. 2023

Shock

Self Cite

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Sepsis is associated with significant mortality and morbidity among critically ill patients admitted to intensive care units (ICU) and represents a major health challenge globally. Given the significant clinical and biological heterogeneity among patients and the dynamic nature of the host immune response, identifying those at high risk of poor outcomes remains a critical challenge. Here, we performed secondary analysis of publicly available time-series gene-expression datasets from peripheral blood of patients admitted to the ICU to elucidate temporally stable gene expression markers between sepsis survivors and non-survivors. Using a limited set of genes that were determined to be temporally stable, we derived a dynamical model using a Support Vector Machine (SVM) classifier to accurately predict the mortality of sepsis patients. Our model had robust performance in a test dataset, where patients’ transcriptome was sampled at alternate time points, with an area under the curve (AUC) of 0.89 (95% CI: 0.82-0.96) upon 5-fold cross-validation. We also identified 7 potential biomarkers of sepsis mortality (STAT5A, CX3CR1, LCP1, SNRPG, RPS27L, LSM5, SHCBP1 that require future validation. Pending prospective testing, our model may be used to identify sepsis patients with high risk of mortality accounting for the dynamic nature of the disease and with potential therapeutic implications.

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OnAI-Comp: An Online AI Experts Competing Framework for Early Sepsis Detection

Cited by 5 publications

References 32 publications

Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data

Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data

Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome

A Machine Learning Model Derived From Analysis of Time-Course Gene-Expression Datasets Reveals Temporally Stable Gene Markers Predictive of Sepsis Mortality

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