Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis
Background: Given the absence of consolidated and standardized international guidelines for managing pediatric appendicitis and the few strictly data-driven studies in this specific, we investigated the use of machine learning (ML) classifiers for predicting the diagnosis, management and severity of...
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2021-04-01
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doaj-df055698ef0e49f5919cbdc5a0ed00922021-04-29T04:53:30ZengFrontiers Media S.A.Frontiers in Pediatrics2296-23602021-04-01910.3389/fped.2021.662183662183Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric AppendicitisRicards Marcinkevics0Patricia Reis Wolfertstetter1Sven Wellmann2Christian Knorr3Julia E. Vogt4Department of Computer Science, ETH Zurich, Zurich, SwitzerlandDepartment of Pediatric Surgery and Pediatric Orthopedics, Hospital St. Hedwig of the Order of St. John of God, University Children's Hospital Regensburg (KUNO), Regensburg, GermanyDivision of Neonatology, Hospital St. Hedwig of the Order of St. John of God, University Children's Hospital Regensburg (KUNO), University of Regensburg, Regensburg, GermanyDepartment of Pediatric Surgery and Pediatric Orthopedics, Hospital St. Hedwig of the Order of St. John of God, University Children's Hospital Regensburg (KUNO), Regensburg, GermanyDepartment of Computer Science, ETH Zurich, Zurich, SwitzerlandBackground: Given the absence of consolidated and standardized international guidelines for managing pediatric appendicitis and the few strictly data-driven studies in this specific, we investigated the use of machine learning (ML) classifiers for predicting the diagnosis, management and severity of appendicitis in children.Materials and Methods: Predictive models were developed and validated on a dataset acquired from 430 children and adolescents aged 0-18 years, based on a range of information encompassing history, clinical examination, laboratory parameters, and abdominal ultrasonography. Logistic regression, random forests, and gradient boosting machines were used for predicting the three target variables.Results: A random forest classifier achieved areas under the precision-recall curve of 0.94, 0.92, and 0.70, respectively, for the diagnosis, management, and severity of appendicitis. We identified smaller subsets of 6, 17, and 18 predictors for each of targets that sufficed to achieve the same performance as the model based on the full set of 38 variables. We used these findings to develop the user-friendly online Appendicitis Prediction Tool for children with suspected appendicitis.Discussion: This pilot study considered the most extensive set of predictor and target variables to date and is the first to simultaneously predict all three targets in children: diagnosis, management, and severity. Moreover, this study presents the first ML model for appendicitis that was deployed as an open access easy-to-use online tool.Conclusion: ML algorithms help to overcome the diagnostic and management challenges posed by appendicitis in children and pave the way toward a more personalized approach to medical decision-making. Further validation studies are needed to develop a finished clinical decision support system.https://www.frontiersin.org/articles/10.3389/fped.2021.662183/fullappendicitispediatricspredictive medicinemachine learningclassification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ricards Marcinkevics Patricia Reis Wolfertstetter Sven Wellmann Christian Knorr Julia E. Vogt |
spellingShingle |
Ricards Marcinkevics Patricia Reis Wolfertstetter Sven Wellmann Christian Knorr Julia E. Vogt Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis Frontiers in Pediatrics appendicitis pediatrics predictive medicine machine learning classification |
author_facet |
Ricards Marcinkevics Patricia Reis Wolfertstetter Sven Wellmann Christian Knorr Julia E. Vogt |
author_sort |
Ricards Marcinkevics |
title |
Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis |
title_short |
Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis |
title_full |
Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis |
title_fullStr |
Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis |
title_full_unstemmed |
Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis |
title_sort |
using machine learning to predict the diagnosis, management and severity of pediatric appendicitis |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Pediatrics |
issn |
2296-2360 |
publishDate |
2021-04-01 |
description |
Background: Given the absence of consolidated and standardized international guidelines for managing pediatric appendicitis and the few strictly data-driven studies in this specific, we investigated the use of machine learning (ML) classifiers for predicting the diagnosis, management and severity of appendicitis in children.Materials and Methods: Predictive models were developed and validated on a dataset acquired from 430 children and adolescents aged 0-18 years, based on a range of information encompassing history, clinical examination, laboratory parameters, and abdominal ultrasonography. Logistic regression, random forests, and gradient boosting machines were used for predicting the three target variables.Results: A random forest classifier achieved areas under the precision-recall curve of 0.94, 0.92, and 0.70, respectively, for the diagnosis, management, and severity of appendicitis. We identified smaller subsets of 6, 17, and 18 predictors for each of targets that sufficed to achieve the same performance as the model based on the full set of 38 variables. We used these findings to develop the user-friendly online Appendicitis Prediction Tool for children with suspected appendicitis.Discussion: This pilot study considered the most extensive set of predictor and target variables to date and is the first to simultaneously predict all three targets in children: diagnosis, management, and severity. Moreover, this study presents the first ML model for appendicitis that was deployed as an open access easy-to-use online tool.Conclusion: ML algorithms help to overcome the diagnostic and management challenges posed by appendicitis in children and pave the way toward a more personalized approach to medical decision-making. Further validation studies are needed to develop a finished clinical decision support system. |
topic |
appendicitis pediatrics predictive medicine machine learning classification |
url |
https://www.frontiersin.org/articles/10.3389/fped.2021.662183/full |
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