A Deep Learning Approach for Classifying Nonalcoholic Steatohepatitis Patients from Nonalcoholic Fatty Liver Disease Patients Using Electronic Medical Records

Suresha, Pradyumna Byappanahalli; Wang, Yunlong; Xiao, Cao; Glass, Lucas M.; Yuan, Yilian; Clifford, Gari D.

doi:10.1007/978-3-030-53352-6_10

Cited by 8 publications

(6 citation statements)

References 11 publications

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“…Suresha et al [ 24 ] compared several models (logistic regression, RF, and XGBoost), but, according to their results, recurrent neural networks (RNN) achieved the best performance and highest accuracy. Yip et al [ 25 ] included 922 patients to compare logistic regression, AdaBoost, and ridge regression.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest

García-Carretero

Holgado-Cuadrado

Barquero-Pérez

2021

Entropy

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Nonalcoholic fatty liver disease (NAFLD) is the hepatic manifestation of metabolic syndrome and is the most common cause of chronic liver disease in developed countries. Certain conditions, including mild inflammation biomarkers, dyslipidemia, and insulin resistance, can trigger a progression to nonalcoholic steatohepatitis (NASH), a condition characterized by inflammation and liver cell damage. We demonstrate the usefulness of machine learning with a case study to analyze the most important features in random forest (RF) models for predicting patients at risk of developing NASH. We collected data from patients who attended the Cardiovascular Risk Unit of Mostoles University Hospital (Madrid, Spain) from 2005 to 2021. We reviewed electronic health records to assess the presence of NASH, which was used as the outcome. We chose RF as the algorithm to develop six models using different pre-processing strategies. The performance metrics was evaluated to choose an optimized model. Finally, several interpretability techniques, such as feature importance, contribution of each feature to predictions, and partial dependence plots, were used to understand and explain the model to help obtain a better understanding of machine learning-based predictions. In total, 1525 patients met the inclusion criteria. The mean age was 57.3 years, and 507 patients had NASH (prevalence of 33.2%). Filter methods (the chi-square and Mann–Whitney–Wilcoxon tests) did not produce additional insight in terms of interactions, contributions, or relationships among variables and their outcomes. The random forest model correctly classified patients with NASH to an accuracy of 0.87 in the best model and to 0.79 in the worst one. Four features were the most relevant: insulin resistance, ferritin, serum levels of insulin, and triglycerides. The contribution of each feature was assessed via partial dependence plots. Random forest-based modeling demonstrated that machine learning can be used to improve interpretability, produce understanding of the modeled behavior, and demonstrate how far certain features can contribute to predictions.

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Section: Introductionmentioning

confidence: 99%

Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest

García-Carretero

Holgado-Cuadrado

Barquero-Pérez

2021

Entropy

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“…Deep learning approaches based on LSTM are utilized to identify patients at risk of developing NASH, and show better performance compared with other competing methods like XGBoost. 68 Considering there is a large amount of EHRs & AUROC in an independent validation cohort.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

“…Long short-term memory (LSTM) is a representative method of RNN, and its gating mechanism within each LSTM cell is effective to avoid the long-term dependency problem in standard RNNs. Deep learning approaches based on LSTM are utilized to identify patients at risk of developing NASH, and they have shown better performance compared to other competing methods, such as XGBoost [ 68 ]. Considering there is a large amount of EHRs available in hospitals, RNN-based methods can work as powerful tools to analyze these existing valuable data for NASH diagnosis.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Non-invasive biomarkers for liver inflammation in non-alcoholic fatty liver disease: present and future

Yip¹,

Lyu²,

Lin³

et al. 2023

Clin Mol Hepatol

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Terry Yip has served as a speaker and an advisory committee member for Gilead Sciences.Vincent Wong has served as an advisory committee member for AbbVie, Allergan, Echosens, Gilead Sciences, Janssen, Perspectum Diagnostics, Pfizer and Terns, and a speaker for Bristol-Myers Squibb, Echosens, Gilead Sciences and Merck.Grace Wong has served as an advisory committee member for Gilead Sciences and Janssen, as a speaker for Abbott,

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“…Although mean values of EHRs over time could be computed to train traditional machine learning models for detection/prediction, such an approach has not taken full advantages of temporal characteristics for probable better performance. Results in a recent study 13 show that recurrent neural networks (RNNs) for sequential data processing achieve higher accuracy for detecting NASH from NAFLD patients than traditional machine learning models including logistic regression, random forest, and XGBoost. RNNs model temporal dependence over time by using not only the current observation but also the previous state for the current state updating as shown in Figure 1b.…”

Section: Principles Of Artificial Intelligencementioning

confidence: 99%

Artificial intelligence in prediction of non‐alcoholic fatty liver disease and fibrosis

Wong

Yuen

Andy

et al. 2021

J of Gastro and Hepatol

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Artificial intelligence (AI) has become increasingly widespread in our daily lives, including healthcare applications. AI has brought many new insights into better ways we care for our patients with chronic liver disease, including non‐alcoholic fatty liver disease and liver fibrosis. There are multiple ways to apply the AI technology on top of the conventional invasive (liver biopsy) and noninvasive (transient elastography, serum biomarkers, or clinical prediction models) approaches. In this review article, we discuss the principles of applying AI on electronic health records, liver biopsy, and liver images. A few common AI approaches include logistic regression, decision tree, random forest, and XGBoost for data at a single time stamp, recurrent neural networks for sequential data, and deep neural networks for histology and images.

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A Deep Learning Approach for Classifying Nonalcoholic Steatohepatitis Patients from Nonalcoholic Fatty Liver Disease Patients Using Electronic Medical Records

Cited by 8 publications

References 11 publications

Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest

Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest

Non-invasive biomarkers for liver inflammation in non-alcoholic fatty liver disease: present and future

Artificial intelligence in prediction of non‐alcoholic fatty liver disease and fibrosis

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