Artificial intelligence in the intensive care unit

Lovejoy, Christopher A.; Buch, Varun; Maruthappu, Mahiben

doi:10.1186/s13054-018-2301-9

Cited by 48 publications

(37 citation statements)

References 12 publications

(12 reference statements)

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“…First, having data of excellent quality is critical for the success of AI predictions. The ICU environment is data-rich, providing fertile soil for the development of accurate predictive models [24], but it is also a challenging environment with heterogeneous and complex data. In our study, the data that fueled the AI method were from a real-world data source.…”

Section: Discussionmentioning

confidence: 99%

“…Step 1: patient data collection Prospective data collection was conducted in a single center over an 18-month period. The study complied with French law for observational studies, was approved by the ethics committee of the French Intensive Care Society (CE SRLF [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], was approved by the Commission Nationale de l'Informatique et des Libertés (CNIL) for the treatment of personal health data. We gave written and oral information to patients or next-of-kin.…”

Section: Methodsmentioning

confidence: 99%

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Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

et al. 2020

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Background: Community-acquired pneumonia (CAP) requires urgent and specific antimicrobial therapy. However, the causal pathogen is typically unknown at the point when anti-infective therapeutics must be initiated. Physicians synthesize information from diverse data streams to make appropriate decisions. Artificial intelligence (AI) excels at finding complex relationships in large volumes of data. We aimed to evaluate the abilities of experienced physicians and AI to answer this question at patient admission: is it a viral or a bacterial pneumonia?Methods: We included patients hospitalized for CAP and recorded all data available in the first 3-h period of care (clinical, biological and radiological information). For this proof-of-concept investigation, we decided to study only CAP caused by a singular and identified pathogen. We built a machine learning model prediction using all collected data. Finally, an independent validation set of samples was used to test the pathogen prediction performance of: (i) a panel of three experts and (ii) the AI algorithm. Both were blinded regarding the final microbial diagnosis. Positive likelihood ratio (LR) values > 10 and negative LR values < 0.1 were considered clinically relevant. Results: We included 153 patients with CAP (70.6% men; 62 [51-73] years old; mean SAPSII, 37 [27-47]), 37% had viral pneumonia, 24% had bacterial pneumonia, 20% had a co-infection and 19% had no identified respiratory pathogen. We performed the analysis on 93 patients as co-pathogen and no-pathogen cases were excluded. The discriminant abilities of the AI approach were low to moderate (LR+ = 2.12 for viral and 6.29 for bacterial pneumonia), and the discriminant abilities of the experts were very low to low (LR+ = 3.81 for viral and 1.89 for bacterial pneumonia).

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

et al. 2020

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“…ML approaches have been applied to study this patient population in a manner that can overcome some of the limitations of traditional RCTs. For example, ML can supply the prediction of patient subsets who are more likely to benefit from (or who are more likely to be harmed by) a test or treatment-the holy grail not just of critical care medicine but all of healthcare [34].…”

Section: Critical Care Medicinementioning

confidence: 99%

“…In addition to MIMIC, the MIT group also maintains a public repository of reusable codes and queries to extract common ICU concepts in order to promote reproducibility and to allow investigators to build on each other's research [38]. MIMIC and other datasets have allowed clinicians, researchers, and data scientists from across the world to explore the use of ML for classification, prediction, and optimization to help improve the management of critically ill patients [34].…”

Section: Critical Care Medicinementioning

confidence: 99%

Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review

2020

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Machine learning (ML) is a discipline of computer science in which statistical methods are applied to data in order to classify, predict, or optimize, based on previously observed data. Pulmonary and critical care medicine have seen a surge in the application of this methodology, potentially delivering improvements in our ability to diagnose, treat, and better understand a multitude of disease states. Here we review the literature and provide a detailed overview of the recent advances in ML as applied to these areas of medicine. In addition, we discuss both the significant benefits of this work as well as the challenges in the implementation and acceptance of this non-traditional methodology for clinical purposes.

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“…Machine learning algorithms have successfully nullified inter clinician variability in the critical care, as well as development of sepsis and targeted therapy. [10][11][12] The aim of the present study is to develop a machine learning algorithm to predict the development of sepsis after 72 hours of ICU admission, based on a select set of predictive tests at admission.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in critical care: prediction of sepsis in patients in intensive care from first initial laboratory parameters

Mitra¹,

Sangwan²

2020

Int J Res Med Sci

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Background: Sepsis is a leading cause of morbidity and mortality in the critical care setting. The analysis of hemostatic parameters at admission have been proven to be a predictive marker for development of sepsis in the ICU. The present study aims to develop a machine learning model which can predict the development of sepsis after 72 hours of ICU admission, from initial assessment of hemostatic parameters. Methods: A total of 170 ICU admissions over six months (May 2018 -Dec 2018) period were included in the study. Hemostatic parameters including platelet counts, prothrombin time and Sonoclot assay were assayed at time of admission. The patients were followed up for development of sepsis. The data was split in two sets: training (100) and test (70). A machine learning model was developed using the linear discriminant analysis (LDA) model, in the R programming environment. The statistical parameters employed were sensitivity, specificity, positive and negative predictive value. Results: A comparison of incidence of development of clinical sepsis and predicted sepsis by the model showed 74.19% sensitivity and 84.61% specificity over the testing set. 06 false positives and 08 false negative predictions were encountered. Conclusions: The model shows potential to be used as a predictive tool for development of sepsis in the critical care ward. Moderate sensitivity and good specificity were achieved by the model, highlighting the role of hematologic assessment at admission in prediction of development of sepsis. However, further studies with larger datasets are required before implementation in clinical practice.

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Artificial intelligence in the intensive care unit

Cited by 48 publications

References 12 publications

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review

Artificial intelligence in critical care: prediction of sepsis in patients in intensive care from first initial laboratory parameters

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