A new method for identifying the acute respiratory distress syndrome disease based on noninvasive physiological parameters

Yang, Po-Chun; Wu, Taihu; Yu, Ming; Chen, Feng; Wang, Chunchen; Yuan, Jing; Xu, Jiameng; Zhang, Guang

doi:10.1371/journal.pone.0226962

Cited by 26 publications

(23 citation statements)

References 32 publications

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“…We derived four machine learning algorithms, including SVM and three tree-based ensemble algorithms (decision tree, AdaBoost, and XGBoost). We selected three decision tree–based algorithms because they have previously been applied to predict clinical events in patients with respiratory diseases based on EHR data [ 16 , 26 , 27 ]. We included models that were frequently applied for clinical prediction of severe patient outcomes [ 16 , 26 , 28 ].…”

Section: Methodsmentioning

confidence: 99%

Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study

Sang¹,

Sun²,

Coquet³

et al. 2021

J Med Internet Res

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Background For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, artificial intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particularly in preparation for an impending crisis or pandemic. Objective This study aimed to develop and test the feasibility of a “patients-like-me” framework to predict the deterioration of patients with COVID-19 using a retrospective cohort of patients with similar respiratory diseases. Methods Our framework used COVID-19–like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-19–like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acute respiratory distress syndrome (ARDS) at an academic medical center from 2008 to 2019. In total, 15 training cohorts were created using different combinations of the COVID-19–like cohorts with the ARDS cohort for exploratory purposes. In this study, two machine learning models were developed: one to predict invasive mechanical ventilation (IMV) within 48 hours for each hospitalized day, and one to predict all-cause mortality at the time of admission. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value. We established model interpretability by calculating SHapley Additive exPlanations (SHAP) scores to identify important features. Results Compared to the COVID-19–like cohorts (n=16,509), the patients hospitalized with COVID-19 (n=159) were significantly younger, with a higher proportion of patients of Hispanic ethnicity, a lower proportion of patients with smoking history, and fewer patients with comorbidities (P<.001). Patients with COVID-19 had a lower IMV rate (15.1 versus 23.2, P=.02) and shorter time to IMV (2.9 versus 4.1 days, P<.001) compared to the COVID-19–like patients. In the COVID-19–like training data, the top models achieved excellent performance (AUROC>0.90). Validating in the COVID-19 cohort, the top-performing model for predicting IMV was the XGBoost model (AUROC=0.826) trained on the viral pneumonia cohort. Similarly, the XGBoost model trained on all 4 COVID-19–like cohorts without ARDS achieved the best performance (AUROC=0.928) in predicting mortality. Important predictors included demographic information (age), vital signs (oxygen saturation), and laboratory values (white blood cell count, cardiac troponin, albumin, etc). Our models had class imbalance, which resulted in high negative predictive values and low positive predictive values. Conclusions We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic.

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Section: Methodsmentioning

confidence: 99%

Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study

Sang¹,

Sun²,

Coquet³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…The most widespread use of big data and ML algorithms in critical care has been in the development of prediction models [ 8 ]. Models for predicting ARDS have been created either using the Electronic Health Record (EHR) from the hospitals [ 15 ], available data from datasets like MIMIC III [ 13 , 16 ] and ARDS network trials [ 17 ]. Rehm et al developed a relatively cheap method to acquire large amounts of ventilator waveform data (vWD) using a low-cost microcomputer, Raspberry Pi, attached to the ventilator [ 18 ].…”

Section: Reviewmentioning

confidence: 99%

Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome?

Bhattarai

Gupta

Ali

et al. 2021

Cureus

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Acute respiratory distress syndrome (ARDS) accounts for 10% of all diagnoses in the Intensive Care Unit, and about 40% of the patients succumb to the disease. Clinical methods alone can result in the under-recognition of this heterogeneous syndrome. The purpose of this study is to evaluate the role that big data and machine learning (ML) have played in understanding the heterogeneity of the disease and the development of various prediction algorithms. Most of the work in the field of ML in ARDS has been in the development of prediction models that have comparable efficacies to that of traditional models. Prediction algorithms have been useful in identifying new variables that may be important to consider in the future, supplementing the unknown information with the help of available noninvasive parameters, as well as predicting mortality. Phenotype identification using an unsupervised ML algorithm has been pivotal in classifying the heterogeneous population into more homogenous classes. Big data generated from ventilators in the form of ventilator waveform analysis and images in the form of radiomics have also been leveraged for the identification of the syndrome and can be incorporated into a clinical decision support system. Although the results are promising, lack of generalizability, “black box” nature of algorithms and concerns about “alarm fatigue” should be addressed for more mainstream adoption of these models.

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“…13,76 In supervised machine learning, the outcome from machine judgment is constantly prognostication of ARDS/ALI. [77][78][79] Machine learning algorithms have also been successfully used to estimate lung mechanics during mechanical ventilation. 80 In using machine learning techniques, investigators need to have clinical expertise and the ability to understand machine learning algorithms.…”

Section: Ai For Clinical Studies Involving Ards/alimentioning

confidence: 99%

Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome

Zhang

Navarese

Zheng

et al. 2020

J Evidence Based Medicine

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Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that encompasses a heterogeneous population. Management of such heterogeneous patient population is a big challenge for clinicians. With accumulating ALI datasets being publicly available, more knowledge could be discovered with sophisticated analytics. We reviewed literatures with big data analytics to understand the role of AI for improving the caring of patients with ALI/ARDS. Many studies have utilized the electronic medical records (EMR) data for the identification and prognostication of ARDS patients. As increasing number of ARDS clinical trials data is open to public, secondary analysis on these combined datasets provide a powerful way of finding solution to clinical questions with a new perspective. AI techniques such as Classification and Regression Tree (CART) and artificial neural networks (ANN) have also been successfully used in the investigation of ARDS problems. Individualized treatment of ARDS could be implemented with a support from AI as we are now able to classify ARDS into many subphenotypes by unsupervised machine learning algorithms. Interestingly, these subphenotypes show different responses to a certain intervention. However, current analytics involving ARDS have not fully incorporated information from omics such as transcriptome, proteomics, daily activities and environmental conditions. AI technology is assisting us to interpret complex data of ARDS patients and enable us to

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A new method for identifying the acute respiratory distress syndrome disease based on noninvasive physiological parameters

Cited by 26 publications

References 32 publications

Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study

Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study

Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome?

Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome

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