Machine Learning For Surgical Time Prediction

Abstract Real-time prediction of storm longevity is a critical challenge for National Weather Service (NWS) forecasters. These predictions can guide forecasters when they issue warnings and implicitly inform them about the potential severity of a storm. This paper presents a machine-learning (ML) system that was used for real-time prediction of storm longevity in the Probabilistic Hazard Information (PHI) tool, making it a Research-to-Operations (R2O) project. Currently, PHI provides forecasters with real-time storm variables and severity predictions from the ProbSevere system, but these predictions do not include storm longevity. We specifically designed our system to be tested in PHI during the 2016 and 2017 Hazardous Weather Testbed (HWT) experiments, which are a quasi-operational naturalistic environment. We considered three ML methods that have proven in prior work to be strong predictors for many weather prediction tasks: elastic nets, random forests, and gradient-boosted regression trees. We present experiments comparing the three ML methods with different types of input data, discuss trade-offs between forecast quality and requirements for real-time deployment, and present both subjective (human-based) and objective evaluation of real-time deployment in the HWT. Results demonstrate that the ML system has lower error than human forecasters, which suggests that it could be used to guide future storm-based warnings, enabling forecasters to focus on other aspects of the warning system.

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A Use Case to Implement Machine Learning for Life Time Prediction of Manufacturing Tools

Procedia CIRP ◽

10.1016/j.procir.2020.04.056 ◽

2020 ◽

Vol 93 ◽

pp. 1484-1489 ◽

Cited By ~ 1

Author(s):

Robin Oberlé ◽

Sebastian Schorr ◽

Li Yi ◽

Moritz Glatt ◽

Dirk Bähre ◽

...

Keyword(s):

Machine Learning ◽

Life Time ◽

Use Case ◽

Time Prediction

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The METLIN small molecule dataset for machine learning-based retention time prediction

Nature Communications ◽

10.1038/s41467-019-13680-7 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 19

Author(s):

Xavier Domingo-Almenara ◽

Carlos Guijas ◽

Elizabeth Billings ◽

J. Rafael Montenegro-Burke ◽

Winnie Uritboonthai ◽

...

Keyword(s):

Machine Learning ◽

Small Molecule ◽

Retention Time ◽

Chromatographic Retention ◽

Retention Time Prediction ◽

Molecular Identity ◽

Time Prediction ◽

Wide Range ◽

Small Molecule Analysis ◽

Deep Learning Model

AbstractMachine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retention time prediction lack sufficient accuracy due to limited available experimental data. Here we introduce the METLIN small molecule retention time (SMRT) dataset, an experimentally acquired reverse-phase chromatography retention time dataset covering up to 80,038 small molecules. To demonstrate the utility of this dataset, we deployed a deep learning model for retention time prediction applied to small molecule annotation. Results showed that in 70$$\%$$% of the cases, the correct molecular identity was ranked among the top 3 candidates based on their predicted retention time. We anticipate that this dataset will enable the community to apply machine learning or first principles strategies to generate better models for retention time prediction.

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Comparison of Machine Learning Algorithms for Software Project Time Prediction

International Journal of Multimedia and Ubiquitous Engineering ◽

10.14257/ijmue.2015.10.9.01 ◽

2015 ◽

Vol 10 (9) ◽

pp. 1-8 ◽

Cited By ~ 3

Author(s):

Wan Jiang Han ◽

Li Xin Jiang ◽

Tian Bo Lu ◽

Xiao Yan Zhang

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Software Project ◽

Time Prediction

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A Machine Learning Approach to Predict Hypotensive Events in ICU Settings

10.1101/794768 ◽

2019 ◽

Author(s):

Mina Chookhachizadeh Moghadam ◽

Ehsan Masoumi ◽

Nader Bagherzadeh ◽

Davinder Ramsingh ◽

Guann-Pyng Li ◽

...

Keyword(s):

Machine Learning ◽

Real Time ◽

Evaluation Method ◽

Learning Algorithm ◽

Machine Learning Algorithms ◽

Physiological Status ◽

Time Prediction ◽

Evaluation Approach ◽

High Positive Predictive Value ◽

Data Points

AbstractPurposePredicting hypotension well in advance provides physicians with enough time to respond with proper therapeutic measures. However, the real-time prediction of hypotension with high positive predictive value (PPV) is a challenge due to the dynamic changes in patients’ physiological status under the drug administration which is limiting the amount of useful data available for the algorithm.MethodsTo mimic real-time monitoring, we developed a machine learning algorithm that uses most of the available data points from patients’ record to train and test the algorithm. The algorithm predicts hypotension up to 30 minutes in advance based on only 5 minutes of patient’s physiological history. A novel evaluation method is proposed to assess the algorithm performance as a function of time at every timestamp within 30 minutes prior to hypotension. This evaluation approach provides statistical tools to find the best possible prediction window.ResultsDuring 181,000 minutes of monitoring of about 400 patients, the algorithm demonstrated 94% accuracy, 85% sensitivity and 96% specificity in predicting hypotension within 30 minutes of the events. A high PPV of 81% obtained and the algorithm predicted 80% of the events 25 minutes prior to their onsets. It was shown that choosing a classification threshold that maximizes the F1 score during the training phase contributes to a high PPV and sensitivity.ConclusionThis study reveals the promising potential of the machine learning algorithms in real-time prediction of hypotensive events in ICU setting based on short-term physiological history.

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