Predictive analytics for step-up therapy: Supervised or semi-supervised learning?

Morid, Mohammad Amin; Lau, Michael; Fiol, Guilherme Del

doi:10.1016/j.jbi.2021.103842

Cited by 11 publications

(3 citation statements)

References 36 publications

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“…Among the three ML models, XGBoost, a scalable, distributed gradient-boosted decision tree ML library, achieved the best performance (AUC = 0.747). The model has gained much attention recently due to its superior performance 27 , 28 , which is compatible with the prediction results in this study. Because the decision tree-based model is adequate for data sets containing various features, Random Forest and XGBoost showed more accuracy than Logistic Regression for mixed data.…”

Section: Discussionsupporting

confidence: 88%

Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test

Matsuo

Kamada

Imamura

et al. 2022

Sci Rep

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Recent effective therapies enable most rheumatoid arthritis (RA) patients to achieve remission; however, some patients experience relapse. We aimed to predict relapse in RA patients through machine learning (ML) using data on ultrasound (US) examination and blood test. Overall, 210 patients with RA in remission at baseline were dichotomized into remission (n = 150) and relapse (n = 60) based on the disease activity at 2-year follow-up. Three ML classifiers [Logistic Regression, Random Forest, and extreme gradient boosting (XGBoost)] and data on 73 features (14 US examination data, 54 blood test data, and five data on patient information) at baseline were used for predicting relapse. The best performance was obtained using the XGBoost classifier (area under the receiver operator characteristic curve (AUC) = 0.747), compared with Random Forest and Logistic Regression (AUC = 0.719 and 0.701, respectively). In the XGBoost classifier prediction, ten important features, including wrist/metatarsophalangeal superb microvascular imaging scores, were selected using the recursive feature elimination method. The performance was superior to that predicted by researcher-selected features, which are conventional prognostic markers. These results suggest that ML can provide an accurate prediction of relapse in RA patients, and the use of predictive algorithms may facilitate personalized treatment options.

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Section: Discussionsupporting

confidence: 88%

Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test

Matsuo

Kamada

Imamura

et al. 2022

Sci Rep

View full text Add to dashboard Cite

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“…Among the evaluated ML models, XGBoost exhibited the most proficient performance. This model has garnered significant attention recently due to its superior performance, 14,15 which aligns with the predictive outcomes of our study.…”

Section: Discussionsupporting

confidence: 79%

Machine Learning-Based Prediction of Hashimoto’s Thyroiditis Development Risk

de Oliveira Andrade,

Matos de Oliveira,

Matos de Oliveira

et al. 2024

Preprint

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Introduction: Hashimoto Thyroiditis (HT) is a prevalent autoimmune disorder impacting thyroid function. Early detection allows for timely intervention and improved patient outcomes. Traditional diagnostic methods rely on clinical presentation and antibody testing, lacking a robust risk prediction tool. Objective: To develop a high-precision machine learning (ML) model for predicting the risk of HT development. Method: Data patients were acquired from PubMed. A binary classifier was constructed through data pre-processing, feature selection, and exploration of various ML models. Hyperparameter optimization and performance evaluation metrics (AUC-ROC, AUC-PR, sensitivity, specificity, precision, F1 score) were employed. Results: Out of a total of 9,173 individuals, 400 subjects within this cohort exhibited normal thyroid function, while 436 individuals were diagnosed with HT. The mean patient age was 45 years, and 90% were female. The best performing model achieved an AUC-ROC of 0.87 and AUC-PR of 0.85, indicating high predictive accuracy. Additionally, sensitivity, specificity, precision, and F1 score reached 85%, 90%, 80%, and 83% respectively, demonstrating the model's effectiveness in identifying individuals at risk of HT development. Hyperparameter tuning was optimized using a Random Search approach. Conclusion: This study demonstrates the feasibility of utilizing ML for accurate prediction of HT risk. The high performance metrics achieved highlight the potential for this approach to become a valuable clinical tool for early identification and risk stratification of patients susceptible to HT. Keywords: Hashimoto Thyroiditis, Machine Learning, Risk Prediction, Algorithms.

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“…Patients who do not respond to initial treatment should be stepped-up to more powerful medications. Morid et al [ 118 ] evaluated multiple supervised and semi-supervised ML techniques to find the most accurate one to forecast a need for treatment step-up within 1 year among 120,237 patients. One-class SVM showed the best performance with a sensitivity and specificity of 89% and 83%, respectively.…”

Section: Artificial Intelligence In Ramentioning

confidence: 99%

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

2022

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Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of Sara Momtazmanesh and Ali Nowroozi have contributed equally to this work and share first authorship.

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Predictive analytics for step-up therapy: Supervised or semi-supervised learning?

Cited by 11 publications

References 36 publications

Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test

Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test

Machine Learning-Based Prediction of Hashimoto’s Thyroiditis Development Risk

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

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