Development of a novel machine learning model to predict presence of nonalcoholic steatohepatitis

Docherty, Matt; Régnier, Stephane A.; Capkun, Gorana; Balp, Maria‐Magdalena; Ye, Qin; Janssens, Nico; Tietz, Andreas; Löffler, Jürgen; Cai, Jennifer; Pedrosa, Marcos; Schattenberg, Jörn M.

doi:10.1093/jamia/ocab003

Cited by 34 publications

(36 citation statements)

References 19 publications

(24 reference statements)

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“…When RF was used to rank feature importance, impurity decrease was calculated. It is worth mentioning the study by Doherty et al [ 29 ]. They compared filter methods, KNN, RF, and XGBoost to develop a model for classification of patients with NASH in a cohort of 704 individuals.…”

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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“…obese and hypertensive). 21 , 34 – 37 In this category, two studies integrated AI with imaging modalities 21 , 34 and three studies incorporated AI with clinical data sets. 35 – 37 Almost all studies selected liver biopsy as the diagnostic methods, except for one study which used ultrasonography findings in combination with elevated liver enzymes.…”

Section: Resultsmentioning

confidence: 99%

“… 21 , 34 – 37 In this category, two studies integrated AI with imaging modalities 21 , 34 and three studies incorporated AI with clinical data sets. 35 – 37 Almost all studies selected liver biopsy as the diagnostic methods, except for one study which used ultrasonography findings in combination with elevated liver enzymes. 36 The pooled sensitivity, specificity, PPV, NPV, and DOR for the diagnosis of NASH were 0.80 (95% CI: 0.75–0.85), 0.69 (95% CI: 0.53–0.82), 0.71 (95% CI: 0.36–0.91), 0.75 (95% CI: 0.35–0.94), and 8.27 (95% CI: 5.53–12.37), respectively.…”

Section: Resultsmentioning

confidence: 99%

Application of artificial intelligence in non-alcoholic fatty liver disease and liver fibrosis: a systematic review and meta-analysis

Decharatanachart

Chaiteerakij

Tiyarattanachai

et al. 2021

Therap Adv Gastroenterol

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Background: The global prevalence of non-alcoholic fatty liver disease (NAFLD) continues to rise. Non-invasive diagnostic modalities including ultrasonography and clinical scoring systems have been proposed as alternatives to liver biopsy but with limited performance. Artificial intelligence (AI) is currently being integrated with conventional diagnostic methods in the hopes of performance improvements. We aimed to estimate the performance of AI-assisted systems for diagnosing NAFLD, non-alcoholic steatohepatitis (NASH), and liver fibrosis. Methods: A systematic review was performed to identify studies integrating AI in the diagnosis of NAFLD, NASH, and liver fibrosis. Pooled sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and summary receiver operating characteristic curves were calculated. Results: Twenty-five studies were included in the systematic review. Meta-analysis of 13 studies showed that AI significantly improved the diagnosis of NAFLD, NASH and liver fibrosis. AI-assisted ultrasonography had excellent performance for diagnosing NAFLD, with a sensitivity, specificity, PPV, NPV of 0.97 (95% confidence interval (CI): 0.91–0.99), 0.98 (95% CI: 0.89–1.00), 0.98 (95% CI: 0.93–1.00), and 0.95 (95% CI: 0.88–0.98), respectively. The performance of AI-assisted ultrasonography was better than AI-assisted clinical data sets for the identification of NAFLD, which provided a sensitivity, specificity, PPV, NPV of 0.75 (95% CI: 0.66–0.82), 0.82 (95% CI: 0.74–0.88), 0.75 (95% CI: 0.60–0.86), and 0.82 (0.74–0.87), respectively. The area under the curves were 0.98 and 0.85 for AI-assisted ultrasonography and AI-assisted clinical data sets, respectively. AI-integrated clinical data sets had a pooled sensitivity, specificity of 0.80 (95%CI: 0.75–0.85), 0.69 (95%CI: 0.53–0.82) for identifying NASH, as well as 0.99–1.00 and 0.76–1.00 for diagnosing liver fibrosis stage F1–F4, respectively. Conclusion: AI-supported systems provide promising performance improvements for diagnosing NAFLD, NASH, and identifying liver fibrosis among NAFLD patients. Prospective trials with direct comparisons between AI-assisted modalities and conventional methods are warranted before real-world implementation. Protocol registration: PROSPERO (CRD42021230391)

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“…Third, currently, we merely experimented with a private Chinese dataset and a public Spanish dataset. According to related studies [ 10 , 13 ], the portability of machine learning models for automated ICD coding might not be guaranteed. In the future, we will test the robustness of our conclusions by experimenting on more public datasets.…”

Section: Discussionmentioning

confidence: 99%

Comparison of different feature extraction methods for applicable automated ICD coding

Zhao

Diao

Yuan

et al. 2022

BMC Med Inform Decis Mak

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Background Automated ICD coding on medical texts via machine learning has been a hot topic. Related studies from medical field heavily relies on conventional bag-of-words (BoW) as the feature extraction method, and do not commonly use more complicated methods, such as word2vec (W2V) and large pretrained models like BERT. This study aimed at uncovering the most effective feature extraction methods for coding models by comparing BoW, W2V and BERT variants. Methods We experimented with a Chinese dataset from Fuwai Hospital, which contains 6947 records and 1532 unique ICD codes, and a public Spanish dataset, which contains 1000 records and 2557 unique ICD codes. We designed coding tasks with different code frequency thresholds (denoted as $$f_s$$ f s ), with a lower threshold indicating a more complex task. Using traditional classifiers, we compared BoW, W2V and BERT variants on accomplishing these coding tasks. Results When $$f_s$$ f s was equal to or greater than 140 for Fuwai dataset, and 60 for the Spanish dataset, the BERT variants with the whole network fine-tuned was the best method, leading to a Micro-F1 of 93.9% for Fuwai data when $$f_s=200$$ f s = 200 , and a Micro-F1 of 85.41% for the Spanish dataset when $$f_s=180$$ f s = 180 . When $$f_s$$ f s fell below 140 for Fuwai dataset, and 60 for the Spanish dataset, BoW turned out to be the best, leading to a Micro-F1 of 83% for Fuwai dataset when $$f_s=20$$ f s = 20 , and a Micro-F1 of 39.1% for the Spanish dataset when $$f_s=20$$ f s = 20 . Our experiments also showed that both the BERT variants and BoW possessed good interpretability, which is important for medical applications of coding models. Conclusions This study shed light on building promising machine learning models for automated ICD coding by revealing the most effective feature extraction methods. Concretely, our results indicated that fine-tuning the whole network of the BERT variants was the optimal method for tasks covering only frequent codes, especially codes that represented unspecified diseases, while BoW was the best for tasks involving both frequent and infrequent codes. The frequency threshold where the best-performing method varied differed between different datasets due to factors like language and codeset.

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Development of a novel machine learning model to predict presence of nonalcoholic steatohepatitis

Cited by 34 publications

References 19 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

Application of artificial intelligence in non-alcoholic fatty liver disease and liver fibrosis: a systematic review and meta-analysis

Comparison of different feature extraction methods for applicable automated ICD coding

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