scholarly journals Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS)

2020 ◽  
Vol 60 ◽  
pp. 96-102 ◽  
Author(s):  
Sidney Le ◽  
Emily Pellegrini ◽  
Abigail Green-Saxena ◽  
Charlotte Summers ◽  
Jana Hoffman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Distress Syndrome ◽  
Supervised Machine Learning ◽  
Early Prediction

scholarly journals Supervised Machine Learning for the Early Prediction of Acute Respiratory Distress Syndrome (ARDS)

2020 ◽  
Author(s):  
Sidney Le ◽  
Emily Pellegrini ◽  
Abigail Green-Saxena ◽  
Charlotte Summers ◽  
Jana Hoffman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Distress Syndrome ◽  
Supervised Machine Learning ◽  
Early Prediction ◽  
Radiology Reports ◽  
Tree Models ◽  
Boosted Tree

ABSTRACTPurposeAcute respiratory distress syndrome (ARDS) is a serious respiratory condition with high mortality and associated morbidity. The objective of this study is to develop and evaluate a novel application of gradient boosted tree models trained on patient health record data for the early prediction of ARDS.Materials and Methods9919 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) data base. XGBoost gradient boosted tree models for early ARDS prediction were created using routinely collected clinical variables and numerical representations of radiology reports as inputs. XGBoost models were iteratively trained and validated using 10-fold cross validation.ResultsOn a hold-out test set, algorithm classifiers attained area under the receiver operating characteristic curve (AUROC) values of 0.905, 0.827, 0.810, and 0.790 when tested for the prediction of ARDS at 0-, 12-, 24-, and 48-hour windows prior to onset, respectively.ConclusionSupervised machine learning predictions may help predict patients with ARDS up to 48 hours prior to onset.

scholarly journals Radiomics score predicts acute respiratory distress syndrome based on the initial CT scan after trauma

2021 ◽  
Author(s):  
Sebastian Röhrich ◽  
Johannes Hofmanninger ◽  
Lukas Negrin ◽  
Georg Langs ◽  
Helmut Prosch
Keyword(s):  
Machine Learning ◽  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Injury Severity ◽  
Distress Syndrome ◽  
Area Under The Curve ◽  
Early Prediction ◽  
Injury Score ◽  
Abbreviated Injury Score

Abstract Objectives Acute respiratory distress syndrome (ARDS) constitutes a major factor determining the clinical outcome in polytraumatized patients. Early prediction of ARDS is crucial for timely supportive therapy to reduce morbidity and mortality. The objective of this study was to develop and test a machine learning–based method for the early prediction of ARDS derived from the first computed tomography scan of polytraumatized patients after admission to the hospital. Materials and methods One hundred twenty-three patients (86 male and 37 female, age 41.2 ± 16.4) with an injury severity score (ISS) of 16 or higher (31.9 ± 10.9) were prospectively included and received a CT scan within 1 h after the accident. The lungs, including air pockets and pleural effusions, were automatically segmented using a deep learning–based algorithm. Subsequently, we extracted radiomics features from within the lung and trained an ensemble of gradient boosted trees (GBT) to predict future ARDS. Results Cross-validated ARDS prediction resulted in an area under the curve (AUC) of 0.79 for the radiomics score compared to 0.66 for ISS, and 0.68 for the abbreviated injury score of the thorax (AIS-thorax). Prediction using the radiomics score yielded an f1-score of 0.70 compared to 0.53 for ISS and 0.57 for AIS-thorax. The radiomics score achieved a sensitivity and specificity of 0.80 and 0.76. Conclusions This study proposes a radiomics-based algorithm for the prediction of ARDS in polytraumatized patients at the time of admission to hospital with an accuracy that competes and surpasses conventional scores despite the heterogeneous, and therefore more realistic, scanning protocols. Key Points • Early prediction of acute respiratory distress syndrome in polytraumatized patients is possible, even when using heterogenous data. • Radiomics-based prediction resulted in an area under the curve of 0.79 compared to 0.66 for the injury severity score, and 0.68 for the abbreviated injury score of the thorax. • Highlighting the most relevant lung regions for prediction facilitates the understanding of machine learning–based prediction.

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

2021 ◽  
Vol 10 (17) ◽  
pp. 3824
Author(s):  
Mohammed Sayed ◽  
David Riaño ◽  
Jesús Villar
Keyword(s):  
Machine Learning ◽  
Mechanical Ventilation ◽  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Distress Syndrome ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Mimic Iii ◽  
Over Time

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.

scholarly journals Early prediction of cause-specific acute respiratory distress syndrome via interpretable machine-learning.

2021 ◽  
Author(s):  
Junwei Wu ◽  
Chao Liu ◽  
Lixin Xie ◽  
Xiang Li ◽  
Kun Xiao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Respiratory Distress Syndrome ◽  
Random Forest ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Distress Syndrome ◽  
Time Window ◽  
Arterial Oxygen ◽  
Early Prediction ◽  
Derivation Cohort

Abstract Background: Several studies have investigated the correlation between physiological parameters and the risk of acute respiratory distress syndrome (ARDS); however, cause-specific ARDS and its early prediction have not been well-studied. We aimed to develop and validate a machine-learning model for the early prediction of inhalation-induced ARDS. Methods: Clinical expertise was applied with data-driven analysis. Using data from electronic intensive care units (retrospective derivation cohort) and the three most accessible vital signs (i.e. heart rate, temperature, and respiratory rate) together with feature engineering, we applied a random forest approach during the time window of 90 hours that ended 6 hours prior to the onset of moderate-to-severe respiratory failure (the ratio of partial pressure of arterial oxygen to fraction of inspired oxygen ≤200 mmHg). Results: The trained random forest classifier was validated using an independent validation cohort, with an area under the curve of 0.9026 (95% confidence interval 0.8075-1) and optimal threshold probability, the accuracy, sensitivity, and specificity values were 0.8947, 1, and 0.875, respectively. A Stable and Interpretable RUle Set (SIRUS) was used to extract rules from the RF to provide guidelines for clinicians. We identified several predictive factors, including resp_96h_6h_min < 9, resp_96h_6h_mean ≥ 16.1, HR_96h_6h_mean ≥ 102, and temp_96h_6h_max > 100, that could be used for predicting inhalation-induced ARDS (moderate-to-severe condition) 6 hours prior to onset in critical care units. (‘xxx_96h_6h_min/mean/max’: the minimum/mean/maximum values of the xxx vital sign collected during a 90 h time window beginning 96 h prior to the onset of ARDS and ending 6 h prior to the onset from every recorded blood gas test) Conclusions: This newly established random forest‑based interpretable model shows good predictive ability for inhalation-induced ARDS and may assist clinicians in decision-making, as well as facilitate the enrolment of patients in prevention programmes to improve their outcomes.

scholarly journals Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Xian-Fei Ding ◽  
Jin-Bo Li ◽  
Huo-Yan Liang ◽  
Zong-Yu Wang ◽  
Ting-Ting Jiao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Respiratory Distress Syndrome ◽  
Cohort Study ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Distress Syndrome ◽  
External Validation ◽  
Secondary Analysis ◽  
Machine Learning Algorithms ◽  
Chinese Patients

Abstract Background To develop a machine learning model for predicting acute respiratory distress syndrome (ARDS) events through commonly available parameters, including baseline characteristics and clinical and laboratory parameters. Methods A secondary analysis of a multi-centre prospective observational cohort study from five hospitals in Beijing, China, was conducted from January 1, 2011, to August 31, 2014. A total of 296 patients at risk for developing ARDS admitted to medical intensive care units (ICUs) were included. We applied a random forest approach to identify the best set of predictors out of 42 variables measured on day 1 of admission. Results All patients were randomly divided into training (80%) and testing (20%) sets. Additionally, these patients were followed daily and assessed according to the Berlin definition. The model obtained an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.82 and yielded a predictive accuracy of 83%. For the first time, four new biomarkers were included in the model: decreased minimum haematocrit, glucose, and sodium and increased minimum white blood cell (WBC) count. Conclusions This newly established machine learning-based model shows good predictive ability in Chinese patients with ARDS. External validation studies are necessary to confirm the generalisability of our approach across populations and treatment practices.

scholarly journals Utilizing machine learning to improve clinical trial design for acute respiratory distress syndrome

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
E. Schwager ◽  
K. Jansson ◽  
A. Rahman ◽  
S. Schiffer ◽  
Y. Chang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trial ◽  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Trial Design ◽  
Statistical Power ◽  
Distress Syndrome ◽  
Univariate Analysis ◽  
Clinical Trial Design

AbstractHeterogeneous patient populations, complex pharmacology and low recruitment rates in the Intensive Care Unit (ICU) have led to the failure of many clinical trials. Recently, machine learning (ML) emerged as a new technology to process and identify big data relationships, enabling a new era in clinical trial design. In this study, we designed a ML model for predictively stratifying acute respiratory distress syndrome (ARDS) patients, ultimately reducing the required number of patients by increasing statistical power through cohort homogeneity. From the Philips eICU Research Institute (eRI) database, no less than 51,555 ARDS patients were extracted. We defined three subpopulations by outcome: (1) rapid death, (2) spontaneous recovery, and (3) long-stay patients. A retrospective univariate analysis identified highly predictive variables for each outcome. All 220 variables were used to determine the most accurate and generalizable model to predict long-stay patients. Multiclass gradient boosting was identified as the best-performing ML model. Whereas alterations in pH, bicarbonate or lactate proved to be strong predictors for rapid death in the univariate analysis, only the multivariate ML model was able to reliably differentiate the disease course of the long-stay outcome population (AUC of 0.77). We demonstrate the feasibility of prospective patient stratification using ML algorithms in the by far largest ARDS cohort reported to date. Our algorithm can identify patients with sufficiently long ARDS episodes to allow time for patients to respond to therapy, increasing statistical power. Further, early enrollment alerts may increase recruitment rate.

Sign in / Sign up

Export Citation Format

Share Document