Artificial Intelligence in Critical Care Medicine

Yoon, Joo Heung; Pinsky, Michael R.; Clermont, Gilles

doi:10.1186/s13054-022-03915-3

Cited by 72 publications

(74 citation statements)

References 48 publications

(49 reference statements)

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“…This study did not focus on predicting adverse outcomes, but instead focused on prompt interventions, which can support major clinical decisions and potentially improve patients’ outcomes. Recent review in literatures demonstrated that major predictors for AI are patient's conditions as recognizing clinical conditions, predicting disease evolution, or predicting outcome 26 – 28 . In recent years, several AI-related studies have concentrated on clinical decision-making, action-based learning, such as using reinforcement learning 26 , 29 .…”

Section: Discussionmentioning

confidence: 99%

“…Recent review in literatures demonstrated that major predictors for AI are patient's conditions as recognizing clinical conditions, predicting disease evolution, or predicting outcome 26 – 28 . In recent years, several AI-related studies have concentrated on clinical decision-making, action-based learning, such as using reinforcement learning 26 , 29 . This study suggests actions, not only decisions, in which it is distinct from those of earlier research 30 – 33 .…”

Section: Discussionmentioning

confidence: 99%

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Machine learning-based suggestion for critical interventions in the management of potentially severe conditioned patients in emergency department triage

Chang

Yoon

et al. 2022

Sci Rep

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Providing timely intervention to critically ill patients is a challenging task in emergency departments (ED). Our study aimed to predict early critical interventions (CrIs), which can be used as clinical recommendations. This retrospective observational study was conducted in the ED of a tertiary hospital located in a Korean metropolitan city. Patient who visited ED from January 1, 2016, to December 31, 2018, were included. Need of six CrIs were selected as prediction outcomes, namely, arterial line (A-line) insertion, oxygen therapy, high-flow nasal cannula (HFNC), intubation, Massive Transfusion Protocol (MTP), and inotropes and vasopressor. Extreme gradient boosting (XGBoost) prediction model was built by using only data available at the initial stage of ED. Overall, 137,883 patients were included in the study. The areas under the receiver operating characteristic curve for the prediction of A-line insertion was 0·913, oxygen therapy was 0.909, HFNC was 0.962, intubation was 0.945, MTP was 0.920, and inotropes or vasopressor administration was 0.899 in the XGBoost method. In addition, an increase in the need for CrIs was associated with worse ED outcomes. The CrIs model was integrated into the study site's electronic medical record and could be used to suggest early interventions for emergency physicians.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Machine learning-based suggestion for critical interventions in the management of potentially severe conditioned patients in emergency department triage

Chang

Yoon

et al. 2022

Sci Rep

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“…There are several untapped areas and much needed to be explored fields in critical care such as research in extracorporeal life support, telemedicine and artificial intelligence algorithms which involve multicentre data sharing and applicability in real time. [ 19 ] The coronavirus disease 2019 pandemic has brought in huge opportunities along with unforeseen challenges for researchers in the critical care unit as evidenced by the continuing shower of research publications. [ 20 - 25 ]…”

Section: Improving the Quality Of Research In The Icu Patientmentioning

confidence: 99%

Optimising clinical outcomes with innovative research in the intensive care unit

Kurdi¹,

Patil²,

Radhakrishnan³

et al. 2022

Indian J Anaesth

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“…Given this dynamic and data-rich environment, the ICU is pre-eminently a place where artificial intelligence (AI) holds the promise to aid clinical decision making. [1][2][3] So far, however, most AI models developed for the ICU remain within the prototyping phase. 4,5 One explanation for this may be that most models in the ICU are built for the task of prediction, ie, mapping input data to (future) patient outcomes.…”

Section: Introductionmentioning

confidence: 99%

Causal inference using observational intensive care unit data: a systematic review and recommendations for future practice

Smit

Krijthe

Bommel

et al. 2022

Preprint

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Aim: To review and appraise the quality of studies that present models for causal inference of time-varying treatment effects in the adult intensive care unit (ICU) and give recommendations to improve future research practice. Data sources: Embase, MEDLINE ALL, Web of Science Core Collection, Google Scholar, medRxiv, and bioRxiv up to March 2, 2022. Study selection: Studies that present models for causal inference that deal with time-varying treatments in adult ICU patients. Data extraction: From the included studies, data was extracted about the study setting and applied methodology. Quality of reporting (QOR) of target trial components and causal assumptions (ie, conditional exchangeability, positivity and consistency) were assessed. Results: 1,714 titles were screened and 60 studies were included, of which 36 (60%) were published in the last 5 years. G methods were the most commonly used (n=40/60, 67%), further divided into inverse-probability-of-treatment weighting (n=36/40, 90%) and the parametric G formula (n=4/40, 10%). The remaining studies (n=20/60, 33%) used reinforcement learning methods. Overall, most studies (n=36/60, 60%) considered static treatment regimes. Only ten (17%) studies fully reported all five target trial components (ie, eligibility criteria, treatment strategies, follow-up period, outcome and analysis plan). The treatment strategies and analysis plan components were not (fully) reported in 38% and 48% of the studies, respectively. The causal assumptions (ie, conditional exchangeability, positivity and consistency) remained unmentioned in 35%, 68% and 88% of the studies, respectively. All three causal assumptions were mentioned (or a check for potential violations was reported) in only six (10%) studies. Sixteen studies (27%) estimated the treatment effect both by adjusting for baseline confounding and by adjusting for baseline and treatment-affected time-varying confounding, which often led to substantial changes in treatment effect estimates. Conclusions: Studies that present models for causal inference in the ICU were found to have incomplete or missing reporting of target trial components and causal assumptions. To achieve actionable artificial intelligence in the ICU, we advocate careful consideration of the causal question of interest, the use of target trial emulation, usage of appropriate causal inference methods and acknowledgement (and ideally examination of potential violations) of the causal assumptions. Systematic review registration: PROSPERO (CRD42022324014)

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Artificial Intelligence in Critical Care Medicine

Cited by 72 publications

References 48 publications

Machine learning-based suggestion for critical interventions in the management of potentially severe conditioned patients in emergency department triage

Machine learning-based suggestion for critical interventions in the management of potentially severe conditioned patients in emergency department triage

Optimising clinical outcomes with innovative research in the intensive care unit

Causal inference using observational intensive care unit data: a systematic review and recommendations for future practice

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