Machine Learning–Based Anomaly Detection Techniques in Ophthalmology

Lu, Randy; Bagdasarova, Yelena; Lee, Aaron

doi:10.1001/jamaophthalmol.2021.5555

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Therefore, an accurate model for identifying TiPN with comprehensive clinical and genetic variables is required in patients with CD. In recent years, powerful data mining and computing have encouraged a growing use of machine learning in the medical field, including diagnosis, treatment, prognostic data classification, and regression[ 29 - 31 ]. Tao et al [ 32 ] generated a predictive model for clinical response in patients with rheumatoid arthritis using a multiomics approach and machine learning, with a predictive accuracy > 85%[ 32 ].…”

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confidence: 99%

Comparison and development of machine learning for thalidomide-induced peripheral neuropathy prediction of refractory Crohn’s disease in Chinese population

Mao¹,

Chao²,

Zhong³

et al. 2023

World J Gastroenterol

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BACKGROUND Thalidomide is an effective treatment for refractory Crohn’s disease (CD). However, thalidomide-induced peripheral neuropathy (TiPN), which has a large individual variation, is a major cause of treatment failure. TiPN is rarely predictable and recognized, especially in CD. It is necessary to develop a risk model to predict TiPN occurrence. AIM To develop and compare a predictive model of TiPN using machine learning based on comprehensive clinical and genetic variables. METHODS A retrospective cohort of 164 CD patients from January 2016 to June 2022 was used to establish the model. The National Cancer Institute Common Toxicity Criteria Sensory Scale (version 4.0) was used to assess TiPN. With 18 clinical features and 150 genetic variables, five predictive models were established and evaluated by the confusion matrix receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), specificity, sensitivity (recall rate), precision, accuracy, and F1 score. RESULTS The top-ranking five risk variables associated with TiPN were interleukin-12 rs1353248 [ P = 0.0004, odds ratio (OR): 8.983, 95% confidence interval (CI): 2.497-30.90], dose (mg/d, P = 0.002), brain-derived neurotrophic factor (BDNF) rs2030324 ( P = 0.001, OR: 3.164, 95%CI: 1.561-6.434), BDNF rs6265 ( P = 0.001, OR: 3.150, 95%CI: 1.546-6.073) and BDNF rs11030104 ( P = 0.001, OR: 3.091, 95%CI: 1.525-5.960). In the training set, gradient boosting decision tree (GBDT), extremely random trees (ET), random forest, logistic regression and extreme gradient boosting (XGBoost) obtained AUROC values > 0.90 and AUPRC > 0.87. Among these models, XGBoost and GBDT obtained the first two highest AUROC (0.90 and 1), AUPRC (0.98 and 1), accuracy (0.96 and 0.98), precision (0.90 and 0.95), F1 score (0.95 and 0.98), specificity (0.94 and 0.97), and sensitivity (1). In the validation set, XGBoost algorithm exhibited the best predictive performance with the highest specificity (0.857), accuracy (0.818), AUPRC (0.86) and AUROC (0.89). ET and GBDT obtained the highest sensitivity (1) and F1 score (0.8). Overall, compared with other state-of-the-art classifiers such as ET, GBDT and RF, XGBoost algorithm not only showed a more stable performance, but also yielded higher ROC-AUC and PRC-AUC scores, demonstrating its high accuracy in prediction of TiPN occurrence. CONCLUSION The powerful XGBoost algorithm accurately predicts TiPN using 18 clinical features and 14 genetic variables. With the ability to identify high-risk patients using single nucleotide polymorphisms, it offers a feasible option for improving thalidomide efficacy in CD patients.

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