A Machine Learning-Based Mortality Prediction Model for Patients with Chronic Hepatitis C Infection: An Exploratory Study

被引:0
作者
Al Alawi, Abdullah M. [1 ,2 ]
Al Shuaili, Halima H. [3 ]
Al-Naamani, Khalid [3 ]
Al Naamani, Zakariya [1 ]
Al-Busafi, Said A. [4 ]
机构
[1] Sultan Qaboos Univ Hosp, Dept Med, Muscat 123, Oman
[2] Oman Med Specialty Board, Internal Med Program, Muscat 130, Oman
[3] Armed Forces Hosp, Dept Med, Muscat 112, Oman
[4] Sultan Qaboos Univ, Coll Med & Hlth Sci, Dept Med, Muscat 123, Oman
关键词
Chronic hepatitis C; machine learning; prediction model; mortality; ARTIFICIAL-INTELLIGENCE; VIRUS; CIRRHOSIS; SURVIVAL; OUTCOMES;
D O I
10.3390/jcm13102939
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: Chronic hepatitis C (HCV) infection presents global health challenges with significant morbidity and mortality implications. Successfully treating patients with cirrhosis may lead to mortality rates comparable to the general population. This study aims to utilize machine learning techniques to create predictive mortality models for individuals with chronic HCV infections. Methods: Data from chronic HCV patients at Sultan Qaboos University Hospital (2009-2017) underwent analysis. Data pre-processing handled missing values and scaled features using Python via Anaconda. Model training involved SelectKBest feature selection and algorithms such as logistic regression, random forest, gradient boosting, and SVM. The evaluation included diverse metrics, with 5-fold cross-validation, ensuring consistent performance assessment. Results: A cohort of 702 patients meeting the eligibility criteria, predominantly male, with a median age of 47, was analyzed across a follow-up period of 97.4 months. Survival probabilities at 12, 36, and 120 months were 90.0%, 84.0%, and 73.0%, respectively. Ten key features selected for mortality prediction included hemoglobin levels, alanine aminotransferase, comorbidities, HCV genotype, coinfections, follow-up duration, and treatment response. Machine learning models, including the logistic regression, random forest, gradient boosting, and support vector machine models, showed high discriminatory power, with logistic regression consistently achieving an AUC value of 0.929. Factors associated with increased mortality risk included cardiovascular diseases, coinfections, and failure to achieve a SVR, while lower ALT levels and specific HCV genotypes were linked to better survival outcomes. Conclusions: This study presents the use of machine learning models to predict mortality in chronic HCV patients, providing crucial insights for risk assessment and tailored treatments. Further validation and refinement of these models are essential to enhance their clinical utility, optimize patient care, and improve outcomes for individuals with chronic HCV infections.
引用
收藏
页数:10
相关论文
共 38 条
[1]  
Al-Yarabi Ahmed, 2023, Sultan Qaboos Univ Med J, V23, P174, DOI 10.18295/squmj.6.2022.045
[2]   Machine Learning Prediction of Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction [J].
Angraal, Suveen ;
Mortazavi, Bobak J. ;
Gupta, Aakriti ;
Khera, Rohan ;
Ahmad, Tariq ;
Desai, Nihar R. ;
Jacoby, Daniel L. ;
Masoudi, Frederick A. ;
Spertus, John A. ;
Krumholz, Harlan M. .
JACC-HEART FAILURE, 2020, 8 (01) :12-21
[3]   The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare [J].
Aung, Yuri Y. M. ;
Wong, David C. S. ;
Ting, Daniel S. W. .
BRITISH MEDICAL BULLETIN, 2021, 139 (01) :4-15
[4]   Artificial intelligence, machine learning, and deep learning in liver transplantation [J].
Bhat, Mamatha ;
Rabindranath, Madhumitha ;
Chara, Beatriz Sordi ;
Simonetto, Douglas A. .
JOURNAL OF HEPATOLOGY, 2023, 78 (06) :1216-1233
[5]   A Guide to Cross-Validation for Artificial Intelligence in Medical Imaging [J].
Bradshaw, Tyler J. ;
Huemann, Zachary ;
Hu, Junjie ;
Rahmim, Arman .
RADIOLOGY-ARTIFICIAL INTELLIGENCE, 2023, 5 (04)
[6]   Survival of patients with HCV cirrhosis and sustained virologic response is similar to the general population [J].
Bruno, Savino ;
Di Marco, Vito ;
Iavarone, Massimo ;
Roffi, Luigi ;
Crosignani, Andrea ;
Calvaruso, Vincenza ;
Aghemo, Alessio ;
Cabibbo, Giuseppe ;
Vigano, Mauro ;
Boccaccio, Vincenzo ;
Craxi, Antonio ;
Colombo, Massimo ;
Maisonneuve, Patrick .
JOURNAL OF HEPATOLOGY, 2016, 64 (06) :1217-1223
[7]   Effect of Hepatitis C Virus and Its Treatment on Survival [J].
Butt, Adeel A. ;
Wang, Xiaoqiang ;
Moore, Charity G. .
HEPATOLOGY, 2009, 50 (02) :387-392
[8]   Treatment of HCV reduces viral hepatitis-associated liver-related mortality in patients: An ERCHIVES study [J].
Butt, Adeel Ajwad ;
Yan, Peng ;
Shaikh, Obaid S. ;
Lo Re, Vincent, III ;
Abou-Samra, Abdul-Badi ;
Sherman, Kenneth E. .
JOURNAL OF HEPATOLOGY, 2020, 73 (02) :277-284
[9]   Concurrent Hepatitis C and B Virus and Human Immunodeficiency Virus Infections Are Associated With Higher Mortality Risk Illustrating the Impact of Syndemics on Health Outcomes [J].
Butt, Zahid A. ;
Wong, Stanley ;
Rossi, Carmine ;
Binka, Mawuena ;
Wong, Jason ;
Yu, Amanda ;
Darvishian, Maryam ;
Alvarez, Maria ;
Chapinal, Nuria ;
Mckee, Geoff ;
Gilbert, Mark ;
Tyndall, Mark W. ;
Krajden, Mel ;
Janjua, Naveed Z. .
OPEN FORUM INFECTIOUS DISEASES, 2020, 7 (09)
[10]  
C Pak Stella, 2017, Euroasian J Hepatogastroenterol, V7, P163, DOI 10.5005/jp-journals-10018-1240