Benchmarking machine learning models on multi-centre eICU critical care dataset

Sheikhalishahi, Seyedmostafa; Balaraman, Vevake; Osmani, Venet

doi:10.1371/journal.pone.0235424

Cited by 56 publications

(49 citation statements)

References 34 publications

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“…We identified 26 studies regarding the prediction of the hospital length of stay that used data science methods. Twenty-three studies used a retrospective cohort design, 126 133 159 173 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 while three were prospective cohort studies. 129 195 196 Data sources mostly used administrative databases 126 133 179 180 182 186 191 192 194 and EHRs, 129 133 176 183 184 188 190 192 196 while other studies used publicly available datasets, 159 173 178 187 189 data warehouses and registries, 133 177 180 195 paper clinical notes, 193 paper patient records, 185 research electronic data capture systems, 188 trial datasets, 181 questionnaires, 196 and routine bedside monitors.…”

Section: Resultsmentioning

confidence: 99%

“…176 Sample sizes ranged from 143 to 2,997,249 patients. Study populations included surgical patients, 133 159 177 179 181 182 183 195 196 ICU patients, 173 176 178 187 189 190 medical-surgical patients, 126 129 180 191 patients presenting to the ED, 184 188 193 194 and psychiatric patients. 185 186 192 Most studies were conducted using U.S. patient data, 129 133 159 173 176 177 178 181 182 183 187 189 191 while other studies used patient data from Australia, 126 179 193 194 Brazil, 186 188 Canada, 195 196 China, 180 England, 190 Germany, 192 Switzerland, 185 and Taiwan.…”

Section: Resultsmentioning

confidence: 99%

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Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

et al. 2022

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Background The term “data science” encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications. Objectives This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature. Methods We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care–acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture. Results Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing. Conclusion This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

et al. 2022

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“…More specifically, ARDS diagnosis increases total ICU and hospital costs for mechanically ventilated ICU patients, suggesting higher total costs due to more days on a ventilator, although there is no clear severity-dependent relationship between ARDS severity and incurred costs [35]. The benchmarking of ML algorithms is possible through publicly available databases such as MIMIC-III [19,27] or eICU [19,36].…”

Section: Discussionmentioning

confidence: 99%

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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“…In critical care medicine, the concept of ML for analysing complex and often highly heterogeneous patient collectives seems reasonable under various circumstances [5]. Different studies have evaluated the use of ML for the treatment of sepsis, assessing patient prognosis and/ or risk for prolonged clinical courses and several other applications [6].…”

Section: Introductionmentioning

confidence: 99%

“…Austria 4. Medizinische Hochschule Hannover, Hannover, Germany 5. Unidad de Bioestadística Clinica Hospital Ramón y Cajal, Instituto Ramón y Cajal de Investigaciones Sanitarias (IRYCIS) & Centro de Investigación en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain 6.…”

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Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation

Mamandipoor

Frutos‐Vivar

Peñuelas

et al. 2021

BMC Med Inform Decis Mak

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Background Mechanical Ventilation (MV) is a complex and central treatment process in the care of critically ill patients. It influences acid–base balance and can also cause prognostically relevant biotrauma by generating forces and liberating reactive oxygen species, negatively affecting outcomes. In this work we evaluate the use of a Recurrent Neural Network (RNN) modelling to predict outcomes of mechanically ventilated patients, using standard mechanical ventilation parameters. Methods We performed our analysis on VENTILA dataset, an observational, prospective, international, multi-centre study, performed to investigate the effect of baseline characteristics and management changes over time on the all-cause mortality rate in mechanically ventilated patients in ICU. Our cohort includes 12,596 adult patients older than 18, associated with 12,755 distinct admissions in ICUs across 37 countries and receiving invasive and non-invasive mechanical ventilation. We carry out four different analysis. Initially we select typical mechanical ventilation parameters and evaluate the machine learning model on both, the overall cohort and a subgroup of patients admitted with respiratory disorders. Furthermore, we carry out sensitivity analysis to evaluate whether inclusion of variables related to the function of other organs, improve the predictive performance of the model for both the overall cohort as well as the subgroup of patients with respiratory disorders. Results Predictive performance of RNN-based model was higher with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.72 (± 0.01) and Average Precision (AP) of 0.57 (± 0.01) in comparison to RF and LR for the overall patient dataset. Higher predictive performance was recorded in the subgroup of patients admitted with respiratory disorders with AUC of 0.75 (± 0.02) and AP of 0.65 (± 0.03). Inclusion of function of other organs further improved the performance to AUC of 0.79 (± 0.01) and AP 0.68 (± 0.02) for the overall patient dataset and AUC of 0.79 (± 0.01) and AP 0.72 (± 0.02) for the subgroup with respiratory disorders. Conclusion The RNN-based model demonstrated better performance than RF and LR in patients in mechanical ventilation and its subgroup admitted with respiratory disorders. Clinical studies are needed to evaluate whether it impacts decision-making and patient outcomes. Trial registration: NCT02731898 (https://clinicaltrials.gov/ct2/show/NCT02731898), prospectively registered on April 8, 2016.

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Benchmarking machine learning models on multi-centre eICU critical care dataset

Cited by 56 publications

References 34 publications

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

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

Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation

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