Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review

Adamidi, Eleni S.; Mitsis, Konstantinos; Nikita, Konstantina S.

doi:10.1016/j.csbj.2021.05.010

Cited by 75 publications

(79 citation statements)

References 112 publications

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“…Our results help reconcile and summarize findings that demographics, clinical events, laboratory data, and comorbidities can help predict mortality in COVID-19 inpatients. To date, work on artificial intelligence modeling in COVID-19 includes several methodologies, the most frequent being LR, XGBoost, support vector machine, RF, among others [ 7 ]. Although current artificial intelligence models have exhibited promising mortality predictive ability, it is unclear which of these methodologies might provide a better prediction compared to others.…”

Section: Discussionmentioning

confidence: 99%

“…Process mining approaches have been shown to be valuable in the healthcare industry by enhancing healthcare processes [ 5 , 6 ]. However, process mining has not yet been used to predict mortality after hospital admission for COVID-19 patients [ 7 , 8 ] though providing significant advantages over static models. In general, process mining algorithms take a sequential perspective on data points that have been observed over time to derive a single semantic-rich graph structure like a Petri Net.…”

Section: Introductionmentioning

confidence: 99%

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A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients

Pishgar

Harford

Theis

et al. 2022

BMC Med Inform Decis Mak

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Background Various machine learning and artificial intelligence methods have been used to predict outcomes of hospitalized COVID-19 patients. However, process mining has not yet been used for COVID-19 prediction. We developed a process mining/deep learning approach to predict mortality among COVID-19 patients and updated the prediction in 6-h intervals during the first 72 h after hospital admission. Methods The process mining/deep learning model produced temporal information related to the variables and incorporated demographic and clinical data to predict mortality. The mortality prediction was updated in 6-h intervals during the first 72 h after hospital admission. Moreover, the performance of the model was compared with published and self-developed traditional machine learning models that did not use time as a variable. The performance was compared using the Area Under the Receiver Operator Curve (AUROC), accuracy, sensitivity, and specificity. Results The proposed process mining/deep learning model outperformed the comparison models in almost all time intervals with a robust AUROC above 80% on a dataset that was imbalanced. Conclusions Our proposed process mining/deep learning model performed significantly better than commonly used machine learning approaches that ignore time information. Thus, time information should be incorporated in models to predict outcomes more accurately.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients

Pishgar

Harford

Theis

et al. 2022

BMC Med Inform Decis Mak

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“…Evidence-based, data-generated, and automated AI support is expected to help ICU clinicians and antimicrobial stewardship teams take the next step in tackling these problems. Although the main focus of AI research in the ICU has been occurrence of sepsis and its outcome prediction as well as, more recently, almost every aspect of coronavirus disease 2019 (COVID-19), important progress has been made in the infection management field as well [ 2 – 4 ]. In this chapter, we provide an overview of the current stance of AI/machine learning research in different areas of antimicrobial infection management, the barriers that hinder clinical adaptation, and pitfalls for bedside use.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Infection Management in the ICU

2022

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This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2022. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2022 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901 .

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“…Wynants et al [8] performed a systematic review of COVID-19-related prediction models up to July 1, 2020, covering 169 studies describing 232 prediction models. Several recent reviews have also summarized the applications of ML methods in the study of COVID-19 (eg, [8][9][10][11]).…”

Section: Introductionmentioning

confidence: 99%

Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach (Preprint)

Wong¹,

Xiang²,

Yin³

et al. 2021

Preprint

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BACKGROUND COVID-19 is a major public health concern. Given the extent of the pandemic, it is urgent to identify risk factors associated with disease severity. More accurate prediction of those at risk of developing severe infections is of high clinical importance. OBJECTIVE Based on the UK Biobank (UKBB), we aimed to build machine learning models to predict the risk of developing severe or fatal infections, and uncover major risk factors involved. METHODS We first restricted the analysis to infected individuals (n=7846), then performed analysis at a population level, considering those with no known infection as controls (ncontrols=465,728). Hospitalization was used as a proxy for severity. A total of 97 clinical variables (collected prior to the COVID-19 outbreak) covering demographic variables, comorbidities, blood measurements (eg, hematological/liver/renal function/metabolic parameters), anthropometric measures, and other risk factors (eg, smoking/drinking) were included as predictors. We also constructed a simplified (lite) prediction model using 27 covariates that can be more easily obtained (demographic and comorbidity data). XGboost (gradient-boosted trees) was used for prediction and predictive performance was assessed by cross-validation. Variable importance was quantified by Shapley values (ShapVal), permutation importance (PermImp), and accuracy gain. Shapley dependency and interaction plots were used to evaluate the pattern of relationships between risk factors and outcomes. RESULTS A total of 2386 severe and 477 fatal cases were identified. For analyses within infected individuals (n=7846), our prediction model achieved area under the receiving-operating characteristic curve (AUC–ROC) of 0.723 (95% CI 0.711-0.736) and 0.814 (95% CI 0.791-0.838) for severe and fatal infections, respectively. The top 5 contributing factors (sorted by ShapVal) for severity were age, number of drugs taken (cnt_tx), cystatin C (reflecting renal function), waist-to-hip ratio (WHR), and Townsend deprivation index (TDI). For mortality, the top features were age, testosterone, cnt_tx, waist circumference (WC), and red cell distribution width. For analyses involving the whole UKBB population, AUCs for severity and fatality were 0.696 (95% CI 0.684-0.708) and 0.825 (95% CI 0.802-0.848), respectively. The same top 5 risk factors were identified for both outcomes, namely, age, cnt_tx, WC, WHR, and TDI. Apart from the above, age, cystatin C, TDI, and cnt_tx were among the top 10 across all 4 analyses. Other diseases top ranked by ShapVal or PermImp were type 2 diabetes mellitus (T2DM), coronary artery disease, atrial fibrillation, and dementia, among others. For the “lite” models, predictive performances were broadly similar, with estimated AUCs of 0.716, 0.818, 0.696, and 0.830, respectively. The top ranked variables were similar to above, including age, cnt_tx, WC, sex (male), and T2DM. CONCLUSIONS We identified numerous baseline clinical risk factors for severe/fatal infection by XGboost. For example, age, central obesity, impaired renal function, multiple comorbidities, and cardiometabolic abnormalities may predispose to poorer outcomes. The prediction models may be useful at a population level to identify those susceptible to developing severe/fatal infections, facilitating targeted prevention strategies. A risk-prediction tool is also available online. Further replications in independent cohorts are required to verify our findings.

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Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review

Abstract: Graphical abstract

Cited by 75 publications

References 112 publications

A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients

A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients

Artificial Intelligence in Infection Management in the ICU

Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach (Preprint)

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