Data Mining and Machine Learning Algorithms Using &lt;i&gt;IL28B&lt;/i&gt; Genotype and Biochemical Markers Best Predicted Advanced Liver Fibrosis in Chronic Hepatitis C

Shousha, Hend Ibrahim; Awad, Abeer; Omran, Dalia; Elnegouly, Mayada; Mabrouk, Mahasen

doi:10.7883/yoken.jjid.2017.089

Cited by 39 publications

(26 citation statements)

References 48 publications

(53 reference statements)

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“…The results highlight that RF models are better at identifying the liver fibrosis index (LFI) degree than other statistical approaches [16]. A review of 427 patients on hepatitis-C produced better predictions through the decision trees [17], and multilayer perceptron (MLP) neural networks were best in predicting late-stage liver fibrosis.…”

Section: Predictive Models Applied In Diagnosis Of CDmentioning

confidence: 97%

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

Battineni

Sagaro

Chinatalapudi

et al. 2020

JPM

221

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This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future.

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Section: Predictive Models Applied In Diagnosis Of CDmentioning

confidence: 97%

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

Battineni

Sagaro

Chinatalapudi

et al. 2020

JPM

221

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“…A similar ML method was later applied to genetic analyses of different HCV strains and was subsequently able to identify relevant genetic markers associated with fast and slow rates of fibrosis progression in HCV . With these same principles, Shousha et al were able to combine data‐mining strategies and interleukin‐28B (IL28B) genotyping to predict advanced fibrosis (AF) in HCV patients using an NN algorithm that had higher performance than both APRI and FIB‐4 . These studies highlight the opportunity for genetic assay design through utilization of computational modeling.…”

Section: Viral Hepatitismentioning

confidence: 99%

“…(47) With these same principles, Shousha et al were able to combine data-mining strategies and interleukin-28B (IL28B) genotyping to predict advanced fibrosis (AF) in HCV patients using an NN algorithm that had higher performance than both APRI and FIB-4. (48) These studies highlight the opportunity for genetic assay design through utilization of computational modeling.…”

Section: Viral Hepatitismentioning

confidence: 99%

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

et al. 2020

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Machine learning (ML) utilizes artificial intelligence to generate predictive models efficiently and more effectively than conventional methods through detection of hidden patterns within large data sets. With this in mind, there are several areas within hepatology where these methods can be applied. In this review, we examine the literature pertaining to machine learning in hepatology and liver transplant medicine. We provide an overview of the strengths and limitations of ML tools and their potential applications to both clinical and molecular data in hepatology. ML has been applied to various types of data in liver disease research, including clinical, demographic, molecular, radiological, and pathological data. We anticipate that use of ML tools to generate predictive algorithms will change the face of clinical practice in hepatology and transplantation. This review will provide readers with the opportunity to learn about the ML tools available and potential applications to questions of interest in hepatology.

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“…There were 11 studies integrating AI with imaging modalities, i.e., ultrasonography (21)(22)(23)(24)(25) , elastography (26,27) , computed tomography (CT) (28,29) and magnetic resonance imaging (MRI) (30,31) , to facilitate the diagnosis of liver brosis and NAFLD. The other 6 studies developed AI models using clinical and laboratory data, such as the presence of other underlying diseases or ascites, liver chemistry tests, and platelet and white blood cell counts, to predict liver brosis stages (32)(33)(34)(35)(36)(37) . Regarding the types of AI, 6 studies used convolutional neural networks (CNNs) (21,23,(27)(28)(29)31) , 5 studies used arti cial neural networks (ANNs) (24,25,(34)(35)(36) , 5 studies used multiple AI models (22,26,32,33,37) and 1 study used a support vector machine (SVM) (30) .…”

Section: Literature Searchmentioning

confidence: 99%

“…The other 6 studies developed AI models using clinical and laboratory data, such as the presence of other underlying diseases or ascites, liver chemistry tests, and platelet and white blood cell counts, to predict liver brosis stages (32)(33)(34)(35)(36)(37) . Regarding the types of AI, 6 studies used convolutional neural networks (CNNs) (21,23,(27)(28)(29)31) , 5 studies used arti cial neural networks (ANNs) (24,25,(34)(35)(36) , 5 studies used multiple AI models (22,26,32,33,37) and 1 study used a support vector machine (SVM) (30) . The study characteristics, sensitivity, speci city, prevalence, validation methods and other extracted data from the included studies are shown in Table 1.…”

Section: Literature Searchmentioning

confidence: 99%

Application of Artificial Intelligence in Chronic Liver Diseases: A Systematic Review and Meta-analysis

Decharatanachart

Chaiteerakij

Tiyarattanachai

et al. 2020

Preprint

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Background: The gold standard for the diagnosis of liver fibrosis and nonalcoholic fatty liver disease (NAFLD) is liver biopsy. Various noninvasive modalities, e.g., ultrasonography, elastography and clinical predictive scores, have been used as alternatives to liver biopsy, with limited performance. Recently, artificial intelligence (AI) models have been developed and integrated into noninvasive diagnostic tools to improve their performance. Methods: We systematically searched for studies on AI-assisted diagnosis of liver fibrosis and NAFLD on MEDLINE, Scopus, Web of Science and Google Scholar. The pooled sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic odds ratio (DOR) with their 95% confidence intervals (95%CIs) were calculated using a random effects model. A summary receiver operating characteristic curve and the area under the curve was generated to determine the diagnostic accuracy of the AI-assisted system. Subgroup analyses by diagnostic modalities were performed.Results: We included 17 studies reporting the performances of AI-assisted ultrasonography, elastrography, computed tomography (CT), magnetic resonance imaging (MRI) and clinical parameters for the diagnosis of liver fibrosis and steatosis. For the diagnosis of liver fibrosis, the pooled sensitivity, specificity, PPV, NPV and DOR were 0.78 (0.70 – 0.84), 0.88 (0.79 – 0.94), 0.73 (0.58 – 0.84), 0.91 (0.88 – 0.94) and 29.57 (10.65 – 82.10), respectively, for cirrhosis; 0.86 (0.80 – 0.91), 0.87 (0.79 – 0.92), 0.85 (0.74 – 0.91), 0.88 (0.82 – 0.92) and 37.95 (15.52 – 92.77), respectively; for advanced fibrosis; and 0.86 (0.78 – 0.92), 0.81 (0.77 – 0.84), 0.88 (0.80 – 0.93), 0.77 (0.58 – 0.89) and 26.79 (14.47 – 49.62), respectively, for significant fibrosis. Subgroup analyses showed significant differences in performance for the diagnosis of fibrosis among different modalities. The pooled sensitivity, specificity, PPV, NPV and DOR were 0.97 (0.76 – 1.00), 0.91 (0.78 – 0.97), 0.95 (0.87 – 0.98), 0.93 (0.80 – 0.98) and 191.52 (38.82 – 944.81), respectively, for the diagnosis of liver steatosis. Conclusions: AI-assisted systems have promising potential for the diagnosis of liver fibrosis and NAFLD. Validations of their performances are warranted before implementing these AI-assisted systems in clinical practice.Trial registration: The protocol was registered with PROSPERO (CRD42020183295).

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Data Mining and Machine Learning Algorithms Using <i>IL28B</i> Genotype and Biochemical Markers Best Predicted Advanced Liver Fibrosis in Chronic Hepatitis C

Cited by 39 publications

References 48 publications

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

Application of Artificial Intelligence in Chronic Liver Diseases: A Systematic Review and Meta-analysis

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