Comparing Artificial Intelligence and Traditional Methods to Identify Factors Associated With Pediatric Asthma Readmission

Hogan, Alexander H.; Brimacombe, Michael; Mosha, Maua; Flores, Glenn

doi:10.1016/j.acap.2021.07.015

Cited by 15 publications

(12 citation statements)

References 29 publications

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“…There was considerable heterogeneity in the definition of the prediction outcome used in the models, including asthma exacerbation [ 4 , 25 , 27 , 29 , 31 , 32 , 34 ], asthma-related hospitalization [ 11 , 24 , 26 , 30 , 33 , 35 ], asthma readmission [ 28 ], asthma prevalence [ 24 ], asthma-related mortality [ 22 ], and asthma relapse [ 21 ].…”

Section: Resultsmentioning

confidence: 99%

Machine Learning–Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review

Budiarto,

Tsang,

Wilson

et al. 2023

JMIR AI

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Background An early warning tool to predict attacks could enhance asthma management and reduce the likelihood of serious consequences. Electronic health records (EHRs) providing access to historical data about patients with asthma coupled with machine learning (ML) provide an opportunity to develop such a tool. Several studies have developed ML-based tools to predict asthma attacks. Objective This study aims to critically evaluate ML-based models derived using EHRs for the prediction of asthma attacks. Methods We systematically searched PubMed and Scopus (the search period was between January 1, 2012, and January 31, 2023) for papers meeting the following inclusion criteria: (1) used EHR data as the main data source, (2) used asthma attack as the outcome, and (3) compared ML-based prediction models’ performance. We excluded non-English papers and nonresearch papers, such as commentary and systematic review papers. In addition, we also excluded papers that did not provide any details about the respective ML approach and its result, including protocol papers. The selected studies were then summarized across multiple dimensions including data preprocessing methods, ML algorithms, model validation, model explainability, and model implementation. Results Overall, 17 papers were included at the end of the selection process. There was considerable heterogeneity in how asthma attacks were defined. Of the 17 studies, 8 (47%) studies used routinely collected data both from primary care and secondary care practices together. Extreme imbalanced data was a notable issue in most studies (13/17, 76%), but only 38% (5/13) of them explicitly dealt with it in their data preprocessing pipeline. The gradient boosting–based method was the best ML method in 59% (10/17) of the studies. Of the 17 studies, 14 (82%) studies used a model explanation method to identify the most important predictors. None of the studies followed the standard reporting guidelines, and none were prospectively validated. Conclusions Our review indicates that this research field is still underdeveloped, given the limited body of evidence, heterogeneity of methods, lack of external validation, and suboptimally reported models. We highlighted several technical challenges (class imbalance, external validation, model explanation, and adherence to reporting guidelines to aid reproducibility) that need to be addressed to make progress toward clinical adoption.

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Section: Resultsmentioning

confidence: 99%

Machine Learning–Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review

Budiarto,

Tsang,

Wilson

et al. 2023

JMIR AI

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“…There were more studies with data from North America (68%)21–37 than Europe (24%),38–43 Australia (4%),44 or the Middle East (4%) 45. Data collected during studies (ie, cross-sectional, longitudinal cohort) were the most common sources of data (56%),23 25 27 31 35 37–45 followed by registry data from either routine EHR (24%),26 28 30 32 34 36 regional or national databases (12%),21 22 33 or clinical records (4%) 24. One study did not report the source of data for model development (4%) 29…”

Section: Resultsmentioning

confidence: 99%

“…There were 18 studies (72%)21 22 24 26–30 32–37 40–43 that carried out an internal validation. This was most often accomplished by splitting the dataset into a test and training set.…”

Section: Resultsmentioning

confidence: 99%

“…It was often not clear whether large-scale studies included data of repeated measures, or if they were independent records. In handling missing values, 32% of studies used complete case analysis,21 22 26 27 31 32 34 39 28% imputed missing values,23–25 33 35 42 45 and one used a combination of both30; however, 36% of studies did not define any explicit methods 28 29 36–38 40 41 43 44. In defining outcomes, proxy measures were often used, for example exacerbation was often recorded as requirement of a medication, which can be biased towards clinician or centre treatment preferences.…”

Section: Resultsmentioning

confidence: 99%

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Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review

et al. 2022

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Machine learning (ML) holds great potential for predicting clinical outcomes in heterogeneous chronic respiratory diseases (CRD) affecting children, where timely individualised treatments offer opportunities for health optimisation. This paper identifies rate-limiting steps in ML prediction model development that impair clinical translation and discusses regulatory, clinical and ethical considerations for ML implementation. A scoping review of ML prediction models in paediatric CRDs was undertaken using the PRISMA extension scoping review guidelines. From 1209 results, 25 articles published between 2013 and 2021 were evaluated for features of a good clinical prediction model using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines.Most of the studies were in asthma (80%), with few in cystic fibrosis (12%), bronchiolitis (4%) and childhood wheeze (4%). There were inconsistencies in model reporting and studies were limited by a lack of validation, and absence of equations or code for replication. Clinician involvement during ML model development is essential and diversity, equity and inclusion should be assessed at each step of the ML pipeline to ensure algorithms do not promote or amplify health disparities among marginalised groups. As ML prediction studies become more frequent, it is important that models are rigorously developed using published guidelines and take account of regulatory frameworks which depend on model complexity, patient safety, accountability and liability.

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“…Scoring indices and conventional statistical models can only analyze simple and linear relationships between variables. Nevertheless, the unknown and multidimensional nature of COVID-19 requires innovative technologies such as artificial intelligence (AI) to analyze the nonlinear and complex relationships between variables [26][27][28][29][30][31][32][33][34][35]. Machine learning (ML), which is a major branch of AI, reveals new and practical patterns from huge raw datasets [36,37].…”

Section: Open Accessmentioning

confidence: 99%

Predictive modeling for COVID-19 readmission risk using machine learning algorithms

Shanbehzadeh

Yazdani

Shafiee

et al. 2022

BMC Med Inform Decis Mak

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Introduction The COVID-19 pandemic overwhelmed healthcare systems with severe shortages in hospital resources such as ICU beds, specialized doctors, and respiratory ventilators. In this situation, reducing COVID-19 readmissions could potentially maintain hospital capacity. By employing machine learning (ML), we can predict the likelihood of COVID-19 readmission risk, which can assist in the optimal allocation of restricted resources to seriously ill patients. Methods In this retrospective single-center study, the data of 1225 COVID-19 patients discharged between January 9, 2020, and October 20, 2021 were analyzed. First, the most important predictors were selected using the horse herd optimization algorithms. Then, three classical ML algorithms, including decision tree, support vector machine, and k-nearest neighbors, and a hybrid algorithm, namely water wave optimization (WWO) as a precise metaheuristic evolutionary algorithm combined with a neural network were used to construct predictive models for COVID-19 readmission. Finally, the performance of prediction models was measured, and the best-performing one was identified. Results The ML algorithms were trained using 17 validated features. Among the four selected ML algorithms, the WWO had the best average performance in tenfold cross-validation (accuracy: 0.9705, precision: 0.9729, recall: 0.9869, specificity: 0.9259, F-measure: 0.9795). Conclusions Our findings show that the WWO algorithm predicts the risk of readmission of COVID-19 patients more accurately than other ML algorithms. The models developed herein can inform frontline clinicians and healthcare policymakers to manage and optimally allocate limited hospital resources to seriously ill COVID-19 patients.

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Comparing Artificial Intelligence and Traditional Methods to Identify Factors Associated With Pediatric Asthma Readmission

Cited by 15 publications

References 29 publications

Machine Learning–Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review

Machine Learning–Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review

Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review

Predictive modeling for COVID-19 readmission risk using machine learning algorithms

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