Decision support by machine learning systems for acute management of severely injured patients: A systematic review

Baur, David; Gehlen, Tobias; Scherer, Julian; Back, David Alexander; Tsitsilonis, Serafeim; Kabir, K.; Osterhoff, Georg

doi:10.3389/fsurg.2022.924810

Cited by 8 publications

(7 citation statements)

References 39 publications

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“…Two recent systematic reviews compared machine learning models for predicting mortality and decision support [ 23 , 24 ]. Zhang et al [ 24 ] discussed six studies using machine learning models based on the national database.…”

Section: Discussionmentioning

confidence: 99%

An Artificial Intelligence Model for Predicting Trauma Mortality Among Emergency Department Patients in South Korea: Retrospective Cohort Study

Lee¹,

Kang²,

Kim³

et al. 2023

J Med Internet Res

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Background Within the trauma system, the emergency department (ED) is the hospital’s first contact and is vital for allocating medical resources. However, there is generally limited information about patients that die in the ED. Objective The aim of this study was to develop an artificial intelligence (AI) model to predict trauma mortality and analyze pertinent mortality factors for all patients visiting the ED. Methods We used the Korean National Emergency Department Information System (NEDIS) data set (N=6,536,306), incorporating over 400 hospitals between 2016 and 2019. We included the International Classification of Disease 10th Revision (ICD-10) codes and chose the following input features to predict ED patient mortality: age, sex, intentionality, injury, emergent symptom, Alert/Verbal/Painful/Unresponsive (AVPU) scale, Korean Triage and Acuity Scale (KTAS), and vital signs. We compared three different feature set performances for AI input: all features (n=921), ICD-10 features (n=878), and features excluding ICD-10 codes (n=43). We devised various machine learning models with an ensemble approach via 5-fold cross-validation and compared the performance of each model with that of traditional prediction models. Lastly, we investigated explainable AI feature effects and deployed our final AI model on a public website, providing access to our mortality prediction results among patients visiting the ED. Results Our proposed AI model with the all-feature set achieved the highest area under the receiver operating characteristic curve (AUROC) of 0.9974 (adaptive boosting [AdaBoost], AdaBoost + light gradient boosting machine [LightGBM]: Ensemble), outperforming other state-of-the-art machine learning and traditional prediction models, including extreme gradient boosting (AUROC=0.9972), LightGBM (AUROC=0.9973), ICD-based injury severity scores (AUC=0.9328 for the inclusive model and AUROC=0.9567 for the exclusive model), and KTAS (AUROC=0.9405). In addition, our proposed AI model outperformed a cutting-edge AI model designed for in-hospital mortality prediction (AUROC=0.7675) for all ED visitors. From the AI model, we also discovered that age and unresponsiveness (coma) were the top two mortality predictors among patients visiting the ED, followed by oxygen saturation, multiple rib fractures (ICD-10 code S224), painful response (stupor, semicoma), and lumbar vertebra fracture (ICD-10 code S320). Conclusions Our proposed AI model exhibits remarkable accuracy in predicting ED mortality. Including the necessity for external validation, a large nationwide data set would provide a more accurate model and minimize overfitting. We anticipate that our AI-based risk calculator tool will substantially aid health care providers, particularly regarding triage and early diagnosis for trauma patients.

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Section: Discussionmentioning

confidence: 99%

An Artificial Intelligence Model for Predicting Trauma Mortality Among Emergency Department Patients in South Korea: Retrospective Cohort Study

Lee¹,

Kang²,

Kim³

et al. 2023

J Med Internet Res

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“…Approximately 8% of all deaths due to major (orthopaedic) trauma are considered potentially preventable ( 75 ). The connection of computer-generated stimulations through visual and auditory displays during the resuscitation can enhance trauma care professionals' interaction and might reduce faulty omission and miscommunication ( 75 ). Previous studies have shown effective tools for the prediction of injury pattern.…”

Section: Methodsmentioning

confidence: 99%

Digitalization in orthopaedics: a narrative review

Youssef,

De Wet,

Back

et al. 2024

Front. Surg.

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Advances in technology and digital tools like the Internet of Things (IoT), artificial intelligence (AI), and sensors are shaping the field of orthopaedic surgery on all levels, from patient care to research and facilitation of logistic processes. Especially the COVID-19 pandemic, with the associated contact restrictions was an accelerator for the development and introduction of telemedical applications and digital alternatives to classical in-person patient care. Digital applications already used in orthopaedic surgery include telemedical support, online video consultations, monitoring of patients using wearables, smart devices, surgical navigation, robotic-assisted surgery, and applications of artificial intelligence in forms of medical image processing, three-dimensional (3D)-modelling, and simulations. In addition to that immersive technologies like virtual, augmented, and mixed reality are increasingly used in training but also rehabilitative and surgical settings. Digital advances can therefore increase the accessibility, efficiency and capabilities of orthopaedic services and facilitate more data-driven, personalized patient care, strengthening the self-responsibility of patients and supporting interdisciplinary healthcare providers to offer for the optimal care for their patients.

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“…Ensemble models often employ bagging, boosting or stacking approaches to combine multiple machine learners, maximising performance of the prediction model when little is known about which single learner best predicts a given outcome 32. Super Learner—a stacking-type ensemble prediction model—has been used in a variety of health outcome prediction models and is generally robust to model misspecification 33–36. Performance and architecture of Super Learner has been described elsewhere 37 38.…”

Section: Methodsmentioning

confidence: 99%

“…32 Super Learner-a stacking-type ensemble prediction modelhas been used in a variety of health outcome prediction models and is generally robust to model misspecification. [33][34][35][36] Performance and architecture of Super Learner has been described elsewhere. 37 38 Super Learner uses cross validation to evaluate and weight the performance of multiple machine learning algorithms to build one ensemble model, thus maximising efficiency while minimising predictive error.…”

Section: Prediction Modelmentioning

confidence: 99%

Implications of rapid population growth on survey design and HIV estimates in the Rakai Community Cohort Study (RCCS), Uganda

et al. 2023

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ObjectiveSince rapid population growth challenges longitudinal population-based HIV cohorts in Africa to maintain coverage of their target populations, this study evaluated whether the exclusion of some residents due to growing population size biases key HIV metrics like prevalence and population-level viremia.Design, setting and participantsData were obtained from the Rakai Community Cohort Study (RCCS) in south central Uganda, an open population-based cohort which began excluding some residents of newly constructed household structures within its surveillance boundaries in 2008. The study includes adults aged 15–49 years who were censused from 2019 to 2020.MeasuresWe fit ensemble machine learning models to RCCS census and survey data to predict HIV seroprevalence and viremia (prevalence of those with viral load >1000 copies/mL) in the excluded population and evaluated whether their inclusion would change overall estimates.ResultsOf the 24 729 census-eligible residents, 2920 (12%) residents were excluded from the RCCS because they were living in new households. The predicted seroprevalence for these excluded residents was 10.8% (95% CI: 9.6% to 11.8%)—somewhat lower than 11.7% (95% CI: 11.2% to 12.3%) in the observed sample. Predicted seroprevalence for younger excluded residents aged 15–24 years was 4.9% (95% CI: 3.6% to 6.1%)—significantly higher than that in the observed sample for the same age group (2.6% (95% CI: 2.2% to 3.1%)), while predicted seroprevalence for older excluded residents aged 25–49 years was 15.0% (95% CI: 13.3% to 16.4%)—significantly lower than their counterparts in the observed sample (17.2% (95% CI: 16.4% to 18.1%)). Over all ages, the predicted prevalence of viremia in excluded residents (3.7% (95% CI: 3.0% to 4.5%)) was significantly higher than that in the observed sample (1.7% (95% CI: 1.5% to 1.9%)), resulting in a higher overall population-level viremia estimate of 2.1% (95% CI: 1.8% to 2.4%).ConclusionsExclusion of residents in new households may modestly bias HIV viremia estimates and some age-specific seroprevalence estimates in the RCCS. Overall, HIV seroprevalence estimates were not significantly affected.

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Decision support by machine learning systems for acute management of severely injured patients: A systematic review

Cited by 8 publications

References 39 publications

An Artificial Intelligence Model for Predicting Trauma Mortality Among Emergency Department Patients in South Korea: Retrospective Cohort Study

An Artificial Intelligence Model for Predicting Trauma Mortality Among Emergency Department Patients in South Korea: Retrospective Cohort Study

Digitalization in orthopaedics: a narrative review

Implications of rapid population growth on survey design and HIV estimates in the Rakai Community Cohort Study (RCCS), Uganda

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