What every intensivist should know about Big Data and targeted machine learning in
            the intensive care unit

Cherifa, Ményssa; Pirracchio, Romain

doi:10.5935/0103-507x.20190069

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…ML is a subset of AI in which machines extract knowledge from the data provided. ML is an exploratory process where there is no one-methodfits-all solution [15,16]. ML merges statistical analysis techniques with computer science to produce algorithms capable of "statistical learning" [17].…”

Section: Introductionmentioning

confidence: 99%

“…ML algorithms are divided into two categories: supervised and unsupervised [17]. Supervised learning algorithms, the ones used in our study, detect relationships between potential explanatory features and a known target outcome [16]. They are commonly used in ICUs to predict clinical outcomes [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…Supervised learning algorithms, the ones used in our study, detect relationships between potential explanatory features and a known target outcome [16]. They are commonly used in ICUs to predict clinical outcomes [16][17][18][19][20][21]. Troché and Moine addressed the critical question on whether MV duration is predictable [22].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Duration of Mechanical Ventilation in Acute Respiratory Distress Syndrome Using Supervised Machine Learning

Sayed

Riaño

Villar

2021

JCM

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Background: Acute respiratory distress syndrome (ARDS) is an intense inflammatory process of the lungs. Most ARDS patients require mechanical ventilation (MV). Few studies have investigated the prediction of MV duration over time. We aimed at characterizing the best early scenario during the first two days in the intensive care unit (ICU) to predict MV duration after ARDS onset using supervised machine learning (ML) approaches. Methods: For model description, we extracted data from the first 3 ICU days after ARDS diagnosis from patients included in the publicly available MIMIC-III database. Disease progression was tracked along those 3 ICU days to assess lung severity according to Berlin criteria. Three robust supervised ML techniques were implemented using Python 3.7 (Light Gradient Boosting Machine (LightGBM); Random Forest (RF); and eXtreme Gradient Boosting (XGBoost)) for predicting MV duration. For external validation, we used the publicly available multicenter database eICU. Results: A total of 2466 and 5153 patients in MIMIC-III and eICU databases, respectively, received MV for >48 h. Median MV duration of extracted patients was 6.5 days (IQR 4.4–9.8 days) in MIMIC-III and 5.0 days (IQR 3.0–9.0 days) in eICU. LightGBM was the best model in predicting MV duration after ARDS onset in MIMIC-III with a root mean square error (RMSE) of 6.10–6.41 days, and it was externally validated in eICU with RMSE of 5.87–6.08 days. The best early prediction model was obtained with data captured in the 2nd day. Conclusions: Supervised ML can make early and accurate predictions of MV duration in ARDS after onset over time across ICUs. Supervised ML models might have important implications for optimizing ICU resource utilization and high acute cost reduction of MV.

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Section: Introductionmentioning

confidence: 99%