1430: EFFECT OF A MACHINE LEARNING-BASED SEVERE SEPSIS PREDICTION ALGORITHM ON PATIENT SURVIVAL

Objectives: We validate a machine learning-based sepsis prediction algorithm (InSight) for detection and prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customization to site-specific data using transfer learning, and generalizability to new settings. Design: A machine learning algorithm with gradient tree boosting. Features for prediction were created from combinations of only six vital sign measurements and their changes over time. Setting: A mixed-ward retrospective data set from the University of California, San Francisco (UCSF) Medical Center (San Francisco, CA) as the primary source, an intensive care unit data set from the Beth Israel Deaconess Medical Center (Boston, MA) as a transfer learning source, and four additional institutions' datasets to evaluate generalizability. Participants: 684,443 total encounters, with 90,353 encounters from June 2011 to March 2016 at UCSF. Interventions: none Primary and secondary outcome measures: Area under the receiver operating characteristic curve (AUROC) for detection and prediction of sepsis, severe sepsis, and septic shock. Results: For detection of sepsis and severe sepsis, InSight achieves an area under the receiver operating characteristic (AUROC) curve of 0.92 (95% CI 0.90 - 0.93) and 0.87 (95% CI 0.86 - 0.88), respectively. Four hours before onset, InSight predicts septic shock with an AUROC of 0.96 (95% CI 0.94 - 0.98), and severe sepsis with an AUROC of 0.85 (95% CI 0.79 - 0.91). Conclusions: InSight outperforms existing sepsis scoring systems in identifying and predicting sepsis, severe sepsis, and septic shock. This is the first sepsis screening system to exceed an AUROC of 0.90 using only vital sign inputs. InSight is robust to missing data, can be customized to novel hospital data using a small fraction of site data, and retained strong discrimination across all institutions.

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Pediatric Severe Sepsis Prediction Using Machine Learning

10.1101/223289 ◽

2017 ◽

Cited By ~ 1

Author(s):

Thomas Desautels ◽

Jana Hoffman ◽

Christopher Barton ◽

Qingqing Mao ◽

Melissa Jay ◽

...

Keyword(s):

Machine Learning ◽

Severe Sepsis ◽

San Francisco ◽

High Performance ◽

Cross Validation ◽

Learning Algorithm ◽

Medical Center ◽

Prediction Algorithm ◽

Machine Learning Algorithm ◽

Logistic Organ Dysfunction

Early detection of pediatric severe sepsis is necessary in order to administer effective treatment. In this study, we assessed the efficacy of a machine-learning-based prediction algorithm applied to electronic healthcare record (EHR) data for the prediction of severe sepsis onset. The resulting prediction performance was compared with the Pediatric Logistic Organ Dysfunction score (PELOD-2) and pediatric Systemic Inflammatory Response Syndrome score (SIRS) using cross-validation and pairwise t-tests. EHR data were collected from a retrospective set of de-identified pediatric inpatient and emergency encounters drawn from the University of California San Francisco (UCSF) Medical Center, with encounter dates between June 2011 and March 2016. Patients (n = 11,127) were 2-17 years of age and 103 [0.93%] were labeled severely septic. In four-fold cross-validation evaluations, the machine learning algorithm achieved an AUROC of 0.912 for discrimination between severely septic and control pediatric patients at onset and AUROC of 0.727 four hours before onset. Under the same measure, the prediction algorithm also significantly outperformed PELOD-2 (p < 0.05) and SIRS (p < 0.05) in the prediction of severe sepsis four hours before onset. This machine learning algorithm has the potential to deliver high-performance severe sepsis detection and prediction for pediatric inpatients.

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Sleep prediction algorithm based on machine learning technology

10.26226/morressier.5b681764b56e9b005965c5a1 ◽

2018 ◽

Author(s):

Kyungmee Park

Keyword(s):

Machine Learning ◽

Prediction Algorithm ◽

Learning Technology

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Precipitation Modeling for Extreme Weather Based on Sparse Hybrid Machine Learning and Markov Chain Random Field in a Multi-Scale Subspace

Water ◽

10.3390/w13091241 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1241

Author(s):

Ming-Hsi Lee ◽

Yenming J. Chen

Keyword(s):

Machine Learning ◽

Markov Chain ◽

Random Field ◽

Long Range ◽

Weather Conditions ◽

Extreme Weather ◽

Prediction Algorithm ◽

Learning Method ◽

Multi Scale ◽

Hybrid Machine

This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for a specific year. This paper proposes a hybrid (ensemble) learning method to perform forecasting on a multi-scaled, conditioned functional time series over a sparse l1 space. Therefore, on the basis of this method, a long-range prediction algorithm is developed for applications, such as agriculture or construction works. Our findings show that the conditioning method and multi-scale decomposition in the parse space l1 are proved useful in resisting statistical variation due to increasingly extreme weather conditions. Because the predictions are year-specific, we verify our prediction accuracy for the year we are interested in, but not for other years.

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Hypernatremia subgroups among hospitalized patients by machine learning consensus clustering with different patient survival

Journal of Nephrology ◽

10.1007/s40620-021-01163-2 ◽

2021 ◽

Author(s):

Charat Thongprayoon ◽

Michael A. Mao ◽

Mira T. Keddis ◽

Andrea G. Kattah ◽

Grace Y. Chong ◽

...

Keyword(s):

Machine Learning ◽

Patient Survival ◽

Hospitalized Patients ◽

Consensus Clustering

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Hydrogen Safety Prediction and Analysis of Hydrogen Refueling Station Leakage Accidents and Process Using Multi-Relevance Machine Learning

World Electric Vehicle Journal ◽

10.3390/wevj12040185 ◽

2021 ◽

Vol 12 (4) ◽

pp. 185

Author(s):

Wujian Yang ◽

Jianghao Dong ◽

Yuke Ren

Keyword(s):

Machine Learning ◽

Gradient Descent ◽

Prediction Algorithm ◽

Hydrogen Energy ◽

Neural Network Optimization ◽

Gradient Descent Algorithm ◽

Source Data ◽

Using Data ◽

Internal Relationships ◽

Hydrogen Safety

Hydrogen energy vehicles are being increasingly widely used. To ensure the safety of hydrogenation stations, research into the detection of hydrogen leaks is required. Offline analysis using data machine learning is achieved using Spark SQL and Spark MLlib technology. In this study, to determine the safety status of a hydrogen refueling station, we used multiple algorithm models to perform calculation and analysis: a multi-source data association prediction algorithm, a random gradient descent algorithm, a deep neural network optimization algorithm, and other algorithm models. We successfully analyzed the data, including the potential relationships, internal relationships, and operation laws between the data, to detect the safety statuses of hydrogen refueling stations.

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A Novel Machine Learning Sepsis Prediction Algorithm for Intended ICU Use (NAVOY Sepsis): A Proof-of-Concept Study (Preprint)

10.2196/preprints.28000 ◽

2021 ◽

Author(s):

Inger Persson ◽

Andreas Östling ◽

Martin Arlbrandt ◽

Joakim Söderberg ◽

David Becedas

Keyword(s):

Machine Learning ◽

High Performance ◽

Learning Algorithm ◽

Scoring Systems ◽

High Accuracy ◽

Prediction Algorithm ◽

Massachusetts Institute Of Technology ◽

Operating Characteristics ◽

Mortality And Morbidity ◽

Institute Of Technology

BACKGROUND Despite decades of research, sepsis remains a leading cause of mortality and morbidity in ICUs worldwide. The key to effective management and patient outcome is early detection, where no prospectively validated machine learning prediction algorithm is available for clinical use in Europe today. OBJECTIVE To develop a high-performance machine learning sepsis prediction algorithm based on routinely collected ICU data, designed to be implemented in Europe. METHODS The machine learning algorithm is developed using Convolutional Neural Network, based on the Massachusetts Institute of Technology Lab for Computational Physiology MIMIC-III Clinical Database, focusing on ICU patients aged 18 years or older. Twenty variables are used for prediction, on an hourly basis. Onset of sepsis is defined in accordance with the international Sepsis-3 criteria. RESULTS The developed algorithm NAVOY Sepsis uses 4 hours of input and can with high accuracy predict patients with high risk of developing sepsis in the coming hours. The prediction performance is superior to that of existing sepsis early warning scoring systems, and competes well with previously published prediction algorithms designed to predict sepsis onset in accordance with the Sepsis-3 criteria, as measured by the area under the receiver operating characteristics curve (AUROC) and the area under the precision-recall curve (AUPRC). NAVOY Sepsis yields AUROC = 0.90 and AUPRC = 0.62 for predictions up to 3 hours before sepsis onset. The predictive performance is externally validated on hold-out test data, where NAVOY Sepsis is confirmed to predict sepsis with high accuracy. CONCLUSIONS An algorithm with excellent predictive properties has been developed, based on variables routinely collected at ICUs. This algorithm is to be further validated in an ongoing prospective randomized clinical trial and will be CE marked as Software as a Medical Device, designed for commercial use in European ICUs.

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