Machine-learning-based predictions of direct-acting antiviral therapy duration for patients with hepatitis C

Feldman, Theodore C.; Dienstag, Jules L.; Mandl, Kenneth D; Tseng, Yi-Ju

doi:10.1016/j.ijmedinf.2021.104562

Cited by 7 publications

(7 citation statements)

References 62 publications

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“…It becomes challenging for medical professionals to depend entirely on all these outcomes. Utilizes fewer parameters in experimental analysis: some existing research operates limited variables 5 , 7 , 11 , 29 forecasting fibrosis inside a human liver, which can degrade the model’s performance. Utilizes limited data samples: some of the HCV research 2 , 5 , 7 utilizes fewer aspects in their HCV prediction research, which encounters accuracy issues and reduces the system’s performance.…”

Section: Existing Workmentioning

confidence: 99%

“…Utilizes fewer parameters in experimental analysis: some existing research operates limited variables 5 , 7 , 11 , 29 forecasting fibrosis inside a human liver, which can degrade the model’s performance.…”

Section: Existing Workmentioning

confidence: 99%

“…Utilizes limited data samples: some of the HCV research 2 , 5 , 7 utilizes fewer aspects in their HCV prediction research, which encounters accuracy issues and reduces the system’s performance.…”

Section: Existing Workmentioning

confidence: 99%

“…Early detection of Hepatitis via the correct diagnosis of blood samples, known as liver tests and appropriate medicine, can help cure the disease 6 . This liver test includes two primary serum biochemical markers named aminotransferase (ALT) and aspartate aminotransferase (AST) 7 . A patient with a higher level of ALT has more risk of being infected with the hepatitis virus.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid model for precise hepatitis-C classification using improved random forest and SVM method

Lilhore,

Manoharan,

Sandhu

et al. 2023

Sci Rep

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Hepatitis C Virus (HCV) is a viral infection that causes liver inflammation. Annually, approximately 3.4 million cases of HCV are reported worldwide. A diagnosis of HCV in earlier stages helps to save lives. In the HCV review, the authors used a single ML-based prediction model in the current research, which encounters several issues, i.e., poor accuracy, data imbalance, and overfitting. This research proposed a Hybrid Predictive Model (HPM) based on an improved random forest and support vector machine to overcome existing research limitations. The proposed model improves a random forest method by adding a bootstrapping approach. The existing RF method is enhanced by adding a bootstrapping process, which helps eliminate the tree’s minor features iteratively to build a strong forest. It improves the performance of the HPM model. The proposed HPM model utilizes a ‘Ranker method’ to rank the dataset features and applies an IRF with SVM, selecting higher-ranked feature elements to build the prediction model. This research uses the online HCV dataset from UCI to measure the proposed model’s performance. The dataset is highly imbalanced; to deal with this issue, we utilized the synthetic minority over-sampling technique (SMOTE). This research performs two experiments. The first experiment is based on data splitting methods, K-fold cross-validation, and training: testing-based splitting. The proposed method achieved an accuracy of 95.89% for k = 5 and 96.29% for k = 10; for the training and testing-based split, the proposed method achieved 91.24% for 80:20 and 92.39% for 70:30, which is the best compared to the existing SVM, MARS, RF, DT, and BGLM methods. In experiment 2, the analysis is performed using feature selection (with SMOTE and without SMOTE). The proposed method achieves an accuracy of 41.541% without SMOTE and 96.82% with SMOTE-based feature selection, which is better than existing ML methods. The experimental results prove the importance of feature selection to achieve higher accuracy in HCV research.

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Section: Existing Workmentioning

confidence: 99%

Section: Existing Workmentioning

confidence: 99%

Section: Existing Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid model for precise hepatitis-C classification using improved random forest and SVM method

Lilhore,

Manoharan,

Sandhu

et al. 2023

Sci Rep

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“…New methods to define this therapy duration are needed to optimize adherence and success. Feldman et al [ 91 ] studied the prediction of DAA treatment duration in hepatitis C patients using XGBoost, RF, and SVM models. They used the dataset of 240 patients with prolonged first course of DAA against another dataset of 3478 patients on standard duration.…”

Section: Approaches In Inflammatory and Liver-related Comorbidities I...mentioning

confidence: 99%

Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases

Martínez¹,

Alonso-Bernáldez²,

Martínez-Urbistondo³

et al. 2022

World J Gastroenterol

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The liver is a key organ involved in a wide range of functions, whose damage can lead to chronic liver disease (CLD). CLD accounts for more than two million deaths worldwide, becoming a social and economic burden for most countries. Among the different factors that can cause CLD, alcohol abuse, viruses, drug treatments, and unhealthy dietary patterns top the list. These conditions prompt and perpetuate an inflammatory environment and oxidative stress imbalance that favor the development of hepatic fibrogenesis. High stages of fibrosis can eventually lead to cirrhosis or hepatocellular carcinoma (HCC). Despite the advances achieved in this field, new approaches are needed for the prevention, diagnosis, treatment, and prognosis of CLD. In this context, the scientific com-munity is using machine learning (ML) algorithms to integrate and process vast amounts of data with unprecedented performance. ML techniques allow the integration of anthropometric, genetic, clinical, biochemical, dietary, lifestyle and omics data, giving new insights to tackle CLD and bringing personalized medicine a step closer. This review summarizes the investigations where ML techniques have been applied to study new approaches that could be used in inflammatory-related, hepatitis viruses-induced, and coronavirus disease 2019-induced liver damage and enlighten the factors involved in CLD development.

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Artificial intelligence applied to ‘omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment

Ghosh,

Zhao,

Alim

et al. 2024

Gut

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Advancements in omics technologies and artificial intelligence (AI) methodologies are fuelling our progress towards personalised diagnosis, prognosis and treatment strategies in hepatology. This review provides a comprehensive overview of the current landscape of AI methods used for analysis of omics data in liver diseases. We present an overview of the prevalence of different omics levels across various liver diseases, as well as categorise the AI methodology used across the studies. Specifically, we highlight the predominance of transcriptomic and genomic profiling and the relatively sparse exploration of other levels such as the proteome and methylome, which represent untapped potential for novel insights. Publicly available database initiatives such as The Cancer Genome Atlas and The International Cancer Genome Consortium have paved the way for advancements in the diagnosis and treatment of hepatocellular carcinoma. However, the same availability of large omics datasets remains limited for other liver diseases. Furthermore, the application of sophisticated AI methods to handle the complexities of multiomics datasets requires substantial data to train and validate the models and faces challenges in achieving bias-free results with clinical utility. Strategies to address the paucity of data and capitalise on opportunities are discussed. Given the substantial global burden of chronic liver diseases, it is imperative that multicentre collaborations be established to generate large-scale omics data for early disease recognition and intervention. Exploring advanced AI methods is also necessary to maximise the potential of these datasets and improve early detection and personalised treatment strategies.

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Machine-learning-based predictions of direct-acting antiviral therapy duration for patients with hepatitis C

Cited by 7 publications

References 62 publications

Hybrid model for precise hepatitis-C classification using improved random forest and SVM method

Hybrid model for precise hepatitis-C classification using improved random forest and SVM method

Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases

Artificial intelligence applied to ‘omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment

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