Lung-heart pressure index is a risk factor for acute respiratory distress syndrome (ARDS): A machine learning and propensity score-matching study.

X, Pan; Chen, Z; Hu, Yifei; J, Shu; Xu, Shumin; X, Zhang; Jiang, Haizhen; Chen, W; Y, Ye; Pan, Jue

doi:10.21203/rs.2.19093/v1

Cited by 1 publication

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Public and large data collections composed the rest of data usage. The most popular public data collection is The Medical Information Mart for Intensive Care (MIMIC) and was used 12 times in two versions 3 [34] and 4 [67] (23.1%). The eICU database [51] is also popular and was used in 9 studies (17.3%).…”

Section: Characteristics Of the Reviewed Studiesmentioning

confidence: 99%

A systematic review of machine learning models for management, prediction and classification of ARDS

Tran,

Joseph

et al. 2024

Respir Res

View full text Add to dashboard Cite

Aim Acute respiratory distress syndrome or ARDS is an acute, severe form of respiratory failure characterised by poor oxygenation and bilateral pulmonary infiltrates. Advancements in signal processing and machine learning have led to promising solutions for classification, event detection and predictive models in the management of ARDS. Method In this review, we provide systematic description of different studies in the application of Machine Learning (ML) and artificial intelligence for management, prediction, and classification of ARDS. We searched the following databases: Google Scholar, PubMed, and EBSCO from 2009 to 2023. A total of 243 studies was screened, in which, 52 studies were included for review and analysis. We integrated knowledge of previous work providing the state of art and overview of explainable decision models in machine learning and have identified areas for future research. Results Gradient boosting is the most common and successful method utilised in 12 (23.1%) of the studies. Due to limitation of data size available, neural network and its variation is used by only 8 (15.4%) studies. Whilst all studies used cross validating technique or separated database for validation, only 1 study validated the model with clinician input. Explainability methods were presented in 15 (28.8%) of studies with the most common method is feature importance which used 14 times. Conclusion For databases of 5000 or fewer samples, extreme gradient boosting has the highest probability of success. A large, multi-region, multi centre database is required to reduce bias and take advantage of neural network method. A framework for validating with and explaining ML model to clinicians involved in the management of ARDS would be very helpful for development and deployment of the ML model.

show abstract

Section: Characteristics Of the Reviewed Studiesmentioning

confidence: 99%