Registered Trials on Artificial Intelligence Conducted in Emergency Department and Intensive Care Unit: A Cross-Sectional Study on ClinicalTrials.gov

Liu, Guina; Li, Nian; Chen, Lingmin; Yang, Yi; Zhang, Yonggang

doi:10.3389/fmed.2021.634197

Cited by 10 publications

(14 citation statements)

References 44 publications

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“…AI is increasingly employed in critical care medicine with a growing number of registered clinical trials [ 19 , 20 ], and the majority of studies aimed to predict the outcome, mainly short-term mortality, in critically ill patients [ 21 ]. Pirracchio et al using a composite ML-based mortality prediction model, so-called Super ICU Learner Algorithm, among 24,508 patients in Medical Information Mart for Intensive Care (MIMIC) II database, reported the accuracy to predict hospital mortality was 0.85 (95% CI: 0.84–0.85), whereas the accuracy of SOFA, a widely used conventional scoring system, was merely 0.71 (95% CI: 0.71–0.72) [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

Explainable machine learning to predict long-term mortality in critically ill ventilated patients: a retrospective study in central Taiwan

Chan

Pai

et al. 2022

BMC Med Inform Decis Mak

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Background Machine learning (ML) model is increasingly used to predict short-term outcome in critically ill patients, but the study for long-term outcome is sparse. We used explainable ML approach to establish 30-day, 90-day and 1-year mortality prediction model in critically ill ventilated patients. Methods We retrospectively included patients who were admitted to intensive care units during 2015–2018 at a tertiary hospital in central Taiwan and linked with the Taiwanese nationwide death registration data. Three ML models, including extreme gradient boosting (XGBoost), random forest (RF) and logistic regression (LR), were used to establish mortality prediction model. Furthermore, we used feature importance, Shapley Additive exPlanations (SHAP) plot, partial dependence plot (PDP), and local interpretable model-agnostic explanations (LIME) to explain the established model. Results We enrolled 6994 patients and found the accuracy was similar among the three ML models, and the area under the curve value of using XGBoost to predict 30-day, 90-day and 1-year mortality were 0.858, 0.839 and 0.816, respectively. The calibration curve and decision curve analysis further demonstrated accuracy and applicability of models. SHAP summary plot and PDP plot illustrated the discriminative point of APACHE (acute physiology and chronic health exam) II score, haemoglobin and albumin to predict 1-year mortality. The application of LIME and SHAP force plots quantified the probability of 1-year mortality and algorithm of key features at individual patient level. Conclusions We used an explainable ML approach, mainly XGBoost, SHAP and LIME plots to establish an explainable 1-year mortality prediction ML model in critically ill ventilated patients.

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Section: Discussionmentioning

confidence: 99%

Explainable machine learning to predict long-term mortality in critically ill ventilated patients: a retrospective study in central Taiwan

Chan

Pai

et al. 2022

BMC Med Inform Decis Mak

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“…There were no trials for children only. On the one hand, NAFLD and HCC occur infrequently in children, and on the other hand, studies in children are very challenging given their ethical, scientific, and practical considerations [ 19 , 27 ]. The majority of trials (85%) were registered after 2017, which coincide with the fourth industrial revolution, that is, AI combined with big data to guide medical information [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

“…ClinicalTrials.gov is a database of private and public funded clinical research conducted worldwide [18]; the researches on this website are transparent and traceable, analyzing registered trials on that will provide the progress of one field. A variety of articles have been published on ClinicalTrials.gov to analyze registered trials [19][20][21][22][23]. AI has been widely applied in the diagnosis and treatment of CLD, and the current research is addressing a range of issues and will lead to greater achievement, but few trials registered with ClinicalTrials.gov are currently known to the academic community.…”

Section: Introductionmentioning

confidence: 99%

Registered Trials of Artificial Intelligence Conducted on Chronic Liver Disease: A Cross-Sectional Study on ClinicalTrials.gov

et al. 2022

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Background. Artificial intelligence (AI) has been widely applied in the diagnosis and therapy of chronic liver disease (CLD), but there is currently little insight into the trials registered on ClinicalTrials.gov. Thus, this cross-sectional study was focused on analyzing the progress in the use of AI in CLD. Methods. Registered trials of AI applied in CLD on ClinicalTrials.gov were searched firstly. All available information was downloaded to Excel (Microsoft Excel, Rong, Rong, China), and duplicates were removed. We extracted the data of the included trials, then analyzed the characteristics of them finally. Results. Up to the 27th of May 2021, 6835 trials were identified following an initial search, and 20 registered trials were included after screening for inclusion and exclusion criteria. Among those trials, hepatocellular carcinoma (HCC, 40.0%) and nonalcoholic fatty liver disease (NAFLD, 20.0%) were the most widely applied CLDs for AI. Trials started in 2013 until 2021, with 17 trials (85%) registered after 2016. There was a large trend in trial enrolment, with 40% of them including samples more than 500. Five trials (25%) have been completed, but only one of these had available results. The most frequent sponsors and collaborators were both hospitals at 55%, followed by universities at 35% and institutes at 11%, respectively. Of the 20 trials included, 35% (7 trials) were interventional trials and 65% (13 trials) were observational trials. Among 7 interventional trials, most trials were for diagnosis purpose (42.86%, 3 trials); 4 trials (57.14%) were randomized; 3 trials (42.86%) applied behavioral intervention, 1 trial (14.29%) was in device intervention, 2 trials (28.57%) were in diagnostic test, and 1 trial intervention was unknown. Among 13 observational trials, 8 (61.54%) were cohort studies; 6 (46.15%) were prospective studies, 4 (30.77%) were retrospective studies, 2 (15.38%) were cross-sectional studies, and 1 (7.69%) did not involve a temporal perspective. Conclusion. The study is the first to focus on AI registration trials in CLD, which will aid relevant scholars in understanding the current state of the subject. This study demonstrates that additional research on AI used in the diagnosis and treatment of CLD is required, and timely publication of accessible results from registered trials is essential.

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“…Ccomparison, which indicated the difference between AI assistance and manual handling. O-outcome, which outlined the results of individualized treatment and diagnosis (14,15). Search strategy was adopted from previous work and experts' opinions (15,16).…”

Section: Data Sources and Search Strategymentioning

confidence: 99%

“…O-outcome, which outlined the results of individualized treatment and diagnosis (14,15). Search strategy was adopted from previous work and experts' opinions (15,16). We refined the query to include keywords related to critical illness ("critical care/treatment", "intensive care", "intensive care unit", "ICU", "emergency medicine/treatment/service/ care/department", and "EICU"), AI technologies ("artificial intelligence", "AI", "algorithmic prognostication", "computational intelligence", "machine learning"), and individualized treatment and diagnosis ("individual", "personalized") in both Medical Subject Headings (MESH) and titles.…”

Section: Data Sources and Search Strategymentioning

confidence: 99%

A bibliometric analysis of the application of artificial intelligence to advance individualized diagnosis and treatment of critical illness

Liu¹,

Zhu²,

Wu³

et al. 2022

Ann Transl Med

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Background: Artificial intelligence (AI) has been extensively applied in the individualized diagnosis and treatment of critical illness, and numerous studies have been published on this topic. Therefore, a bibliometric analysis of these publications should be performed to provide a direction of hot topics and future research trends.Methods: A bibliometric analysis was performed on the research articles to identify the hot topics and any unsolved issues regarding the use of AI in individualized diagnosis and treatment of critical illness. Articles published from January 2011 to December 2021 were retrieved from the Web of Science (WOS) core collection database for bibliometric analysis, and a cross-sectional analysis of the relevant studies that had been registered at ClinicalTrials.gov was also conducted. Results:The number of articles published showed an annually increasing trend, with a worldwide geographic distribution over the past decade. Ultimately, 427 research articles were included in the bibliometric analysis. The relevant articles were divided into four separate clusters that focused on AI application aspects, prediction model establishment, coronavirus disease 2019 (COVID-19) treatment and outcome assessments, respectively. "Machine learning" was the most frequent keyword (147 occurrences, 165 links, and 395 total link strengths) followed by "risk", "models", and "mortality". With 205 articles, the United States of America (USA) had interacted the most with other countries (20 links, and 94 total link strength), while the domestic research institutes in China had infrequently collaborated with others. Approximately 130 trials focusing on the application of AI in the intensive care unit (ICU) and emergency department (ED) had been registered at ClinicalTrial.gov, and most of them (n=71, 54.6%) were interventional. The main research objectives of these trials were to provide decision making assistance and establish prediction models. However, only 3.8% (5 trials) of them had reached exact conclusions which favored the application of AI. Conclusions:The application of AI has raised great interest in critical illness and has mainly been focused on decision making assistance and prediction model establishment. Cooperation between agencies engaged in AI research needs to be strengthened. An increasing number of trials have been registered at ClinicalTrial. gov, and the results of them are promising.

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Registered Trials on Artificial Intelligence Conducted in Emergency Department and Intensive Care Unit: A Cross-Sectional Study on ClinicalTrials.gov

Cited by 10 publications

References 44 publications

Explainable machine learning to predict long-term mortality in critically ill ventilated patients: a retrospective study in central Taiwan

Explainable machine learning to predict long-term mortality in critically ill ventilated patients: a retrospective study in central Taiwan

Registered Trials of Artificial Intelligence Conducted on Chronic Liver Disease: A Cross-Sectional Study on ClinicalTrials.gov

A bibliometric analysis of the application of artificial intelligence to advance individualized diagnosis and treatment of critical illness

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