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Kirdeev A, Burkin K, Vorobev A, Zbirovskaya E, Lifshits G, Nikolaev K, Zelenskaya E, Donnikov M, Kovalenko L, Urvantseva I, Poptsova M. Machine learning models for predicting risks of MACEs for myocardial infarction patients with different VEGFR2 genotypes. Front Med (Lausanne) 2024; 11:1452239. [PMID: 39301488 PMCID: PMC11410707 DOI: 10.3389/fmed.2024.1452239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Accepted: 08/19/2024] [Indexed: 09/22/2024] Open
Abstract
Background The development of prognostic models for the identification of high-risk myocardial infarction (MI) patients is a crucial step toward personalized medicine. Genetic factors are known to be associated with an increased risk of cardiovascular diseases; however, little is known about whether they can be used to predict major adverse cardiac events (MACEs) for MI patients. This study aimed to build a machine learning (ML) model to predict MACEs in MI patients based on clinical, imaging, laboratory, and genetic features and to assess the influence of genetics on the prognostic power of the model. Methods We analyzed the data from 218 MI patients admitted to the emergency department at the Surgut District Center for Diagnostics and Cardiovascular Surgery, Russia. Upon admission, standard clinical measurements and imaging data were collected for each patient. Additionally, patients were genotyped for VEGFR-2 variation rs2305948 (C/C, C/T, T/T genotypes with T being the minor risk allele). The study included a 9-year follow-up period during which major ischemic events were recorded. We trained and evaluated various ML models, including Gradient Boosting, Random Forest, Logistic Regression, and AutoML. For feature importance analysis, we applied the sequential feature selection (SFS) and Shapley's scheme of additive explanation (SHAP) methods. Results The CatBoost algorithm, with features selected using the SFS method, showed the best performance on the test cohort, achieving a ROC AUC of 0.813. Feature importance analysis identified the dose of statins as the most important factor, with the VEGFR-2 genotype among the top 5. The other important features are coronary artery lesions (coronary artery stenoses ≥70%), left ventricular (LV) parameters such as lateral LV wall and LV mass, diabetes, type of revascularization (CABG or PCI), and age. We also showed that contributions are additive and that high risk can be determined by cumulative negative effects from different prognostic factors. Conclusion Our ML-based approach demonstrated that the VEGFR-2 genotype is associated with an increased risk of MACEs in MI patients. However, the risk can be significantly reduced by high-dose statins and positive factors such as the absence of coronary artery lesions, absence of diabetes, and younger age.
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Affiliation(s)
- Alexander Kirdeev
- Faculty of Computer Science, AI and Digital Science Institute, International Laboratory of Bioinformatics, Higher School of Economics University, Moscow, Russia
| | - Konstantin Burkin
- Faculty of Computer Science, AI and Digital Science Institute, International Laboratory of Bioinformatics, Higher School of Economics University, Moscow, Russia
| | - Anton Vorobev
- Department of Cardiology, Surgut State University, Surgut, Russia
| | - Elena Zbirovskaya
- Faculty of Computer Science, AI and Digital Science Institute, International Laboratory of Bioinformatics, Higher School of Economics University, Moscow, Russia
| | - Galina Lifshits
- Institute of Chemical Biology and Fundamental Medicine, Novosibirsk, Russia
| | - Konstantin Nikolaev
- Federal Research Center Institute of Cytology and Genetics, Novosibirsk, Russia
| | - Elena Zelenskaya
- Department of Cardiology, Surgut State University, Surgut, Russia
| | - Maxim Donnikov
- Department of Cardiology, Surgut State University, Surgut, Russia
| | - Lyudmila Kovalenko
- Department of General Pathology and Pathophysiology, Surgut State University, Surgut, Russia
| | - Irina Urvantseva
- Department of Cardiology, Surgut State University, Surgut, Russia
- Ugra Center for Diagnostics and Cardiovascular Surgery, Surgut, Russia
| | - Maria Poptsova
- Faculty of Computer Science, AI and Digital Science Institute, International Laboratory of Bioinformatics, Higher School of Economics University, Moscow, Russia
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Kufel J, Bargieł-Łączek K, Kocot S, Koźlik M, Bartnikowska W, Janik M, Czogalik Ł, Dudek P, Magiera M, Lis A, Paszkiewicz I, Nawrat Z, Cebula M, Gruszczyńska K. What Is Machine Learning, Artificial Neural Networks and Deep Learning?-Examples of Practical Applications in Medicine. Diagnostics (Basel) 2023; 13:2582. [PMID: 37568945 PMCID: PMC10417718 DOI: 10.3390/diagnostics13152582] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 07/19/2023] [Accepted: 08/01/2023] [Indexed: 08/13/2023] Open
Abstract
Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that have not been manually programmed but have been trained on data. ANNs that are a part of ML aim to simulate the structure and function of the human brain. DL, on the other hand, uses multiple layers of interconnected neurons. This enables the processing and analysis of large and complex databases. In medicine, these techniques are being introduced to improve the speed and efficiency of disease diagnosis and treatment. Each of the AI techniques presented in the paper is supported with an example of a possible medical application. Given the rapid development of technology, the use of AI in medicine shows promising results in the context of patient care. It is particularly important to keep a close eye on this issue and conduct further research in order to fully explore the potential of ML, ANNs, and DL, and bring further applications into clinical use in the future.
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Affiliation(s)
- Jakub Kufel
- Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 41-808 Zabrze, Poland;
| | - Katarzyna Bargieł-Łączek
- Paediatric Radiology Students’ Scientific Association at the Division of Diagnostic Imaging, Department of Radiology and Nuclear Medicine, Faculty of Medical Science in Katowice, Medical University of Silesia, 40-752 Katowice, Poland; (K.B.-Ł.); (W.B.)
| | - Szymon Kocot
- Bright Coders’ Factory, Technologiczna 2, 45-839 Opole, Poland
| | - Maciej Koźlik
- Division of Cardiology and Structural Heart Disease, Medical University of Silesia, 40-635 Katowice, Poland;
| | - Wiktoria Bartnikowska
- Paediatric Radiology Students’ Scientific Association at the Division of Diagnostic Imaging, Department of Radiology and Nuclear Medicine, Faculty of Medical Science in Katowice, Medical University of Silesia, 40-752 Katowice, Poland; (K.B.-Ł.); (W.B.)
| | - Michał Janik
- Student Scientific Association Named after Professor Zbigniew Religa at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland; (M.J.); (Ł.C.); (P.D.); (M.M.); (I.P.)
| | - Łukasz Czogalik
- Student Scientific Association Named after Professor Zbigniew Religa at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland; (M.J.); (Ł.C.); (P.D.); (M.M.); (I.P.)
| | - Piotr Dudek
- Student Scientific Association Named after Professor Zbigniew Religa at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland; (M.J.); (Ł.C.); (P.D.); (M.M.); (I.P.)
| | - Mikołaj Magiera
- Student Scientific Association Named after Professor Zbigniew Religa at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland; (M.J.); (Ł.C.); (P.D.); (M.M.); (I.P.)
| | - Anna Lis
- Cardiology Students’ Scientific Association at the III Department of Cardiology, Faculty of Medical Sciences in Katowice, Medical University of Silesia, 40-635 Katowice, Poland;
| | - Iga Paszkiewicz
- Student Scientific Association Named after Professor Zbigniew Religa at the Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Jordana 19, 41-808 Zabrze, Poland; (M.J.); (Ł.C.); (P.D.); (M.M.); (I.P.)
| | - Zbigniew Nawrat
- Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 41-808 Zabrze, Poland;
| | - Maciej Cebula
- Individual Specialist Medical Practice Maciej Cebula, 40-754 Katowice, Poland;
| | - Katarzyna Gruszczyńska
- Department of Radiodiagnostics, Invasive Radiology and Nuclear Medicine, Department of Radiology and Nuclear Medicine, School of Medicine in Katowice, Medical University of Silesia, Medyków 14, 40-752 Katowice, Poland;
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Chen Z, Li T, Guo S, Zeng D, Wang K. Machine learning-based in-hospital mortality risk prediction tool for intensive care unit patients with heart failure. Front Cardiovasc Med 2023; 10:1119699. [PMID: 37077747 PMCID: PMC10106627 DOI: 10.3389/fcvm.2023.1119699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/21/2023] [Indexed: 04/05/2023] Open
Abstract
ObjectiveRisk stratification of patients with congestive heart failure (HF) is vital in clinical practice. The aim of this study was to construct a machine learning model to predict the in-hospital all-cause mortality for intensive care unit (ICU) patients with HF.MethodseXtreme Gradient Boosting algorithm (XGBoost) was used to construct a new prediction model (XGBoost model) from the Medical Information Mart for Intensive Care IV database (MIMIC-IV) (training set). The eICU Collaborative Research Database dataset (eICU-CRD) was used for the external validation (test set). The XGBoost model performance was compared with a logistic regression model and an existing model (Get with the guideline-Heart Failure model) for mortality in the test set. Area under the receiver operating characteristic cure and Brier score were employed to evaluate the discrimination and the calibration of the three models. The SHapley Additive exPlanations (SHAP) value was applied to explain XGBoost model and calculate the importance of its features.ResultsThe total of 11,156 and 9,837 patients with congestive HF from the training set and test set, respectively, were included in the study. In-hospital all-cause mortality occurred in 13.3% (1,484/11,156) and 13.4% (1,319/9,837) of patients, respectively. In the training set, of 17 features with the highest predictive value were selected into the models with LASSO regression. Acute Physiology Score III (APS III), age and Sequential Organ Failure Assessment (SOFA) were strongest predictors in SHAP. In the external validation, the XGBoost model performance was superior to that of conventional risk predictive methods, with an area under the curve of 0.771 (95% confidence interval, 0.757–0.784) and a Brier score of 0.100. In the evaluation of clinical effectiveness, the machine learning model brought a positive net benefit in the threshold probability of 0%–90%, prompting evident competitiveness compare to the other two models. This model has been translated into an online calculator which is accessible freely to the public (https://nkuwangkai-app-for-mortality-prediction-app-a8mhkf.streamlit.app).ConclusionThis study developed a valuable machine learning risk stratification tool to accurately assess and stratify the risk of in-hospital all-cause mortality in ICU patients with congestive HF. This model was translated into a web-based calculator which access freely.
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Affiliation(s)
- Zijun Chen
- Department of Cardiology, The Yongchuan Hospital of Chongqing Medical University, Chongqing, China
- Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Tingming Li
- Department of Cardiology, The Yongchuan Hospital of Chongqing Medical University, Chongqing, China
| | - Sheng Guo
- Department of Cardiology, The People’s Hospital of Rongchang District, Chongqing, China
| | - Deli Zeng
- Department of Cardiology, The Yongchuan Hospital of Chongqing Medical University, Chongqing, China
| | - Kai Wang
- Department of Cardiology, The Yongchuan Hospital of Chongqing Medical University, Chongqing, China
- Correspondence: Kai Wang
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Aguirre U, Urrechaga E. Diagnostic performance of machine learning models using cell population data for the detection of sepsis: a comparative study. Clin Chem Lab Med 2023; 61:356-365. [PMID: 36351434 DOI: 10.1515/cclm-2022-0713] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 10/18/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVES To compare the artificial intelligence algorithms as powerful machine learning methods for evaluating patients with suspected sepsis using data from routinely available blood tests performed on arrival at the hospital. Results were compared with those obtained from the classical logistic regression method. METHODS The study group consisted of consecutive patients with fever and suspected infection admitted to the Emergency Department. The complete blood counts (CBC) were acquired using the Mindray BC-6800 Plus analyser (Mindray Diagnostics, Shenzhen, China). Cell Population Data (CPD) were also recorded. The ML and artificial intelligence (AI) models were developed; their performance was evaluated using several indicators, such as the area under the receiver operating curve (AUC), calibration plots and decision curve analysis (DCA). RESULTS Overall, all the tested approaches obtained an AUC>0.90. The logistic regression (LR) performed well compared to the ML/AI models. The naïve Bayes and the K-nearest neighbour (KNN) methods did not show good calibration properties. The multi-layer perceptron (MLP) model was the best in terms of discrimination, calibration and clinical usefulness. CONCLUSIONS The best performance in the early detection of sepsis was achieved using the ML and AI models. However, external validation studies are needed to strengthen model derivation and procedure updating.
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Affiliation(s)
- Urko Aguirre
- Research Unit, Osakidetza Basque Health Service, Barrualde-Galdakao Integrated Health Organisation, Galdakao-Usansolo Hospital, Galdakao, Spain
- Kronikgune Institute for Health Services Research, Barakaldo, Spain
- Research Network in Health Services in Chronic Diseases (Red de Investigación en Servicios de Salud en Enfermedades Crónicas, REDISSEC), Galdakao, Spain
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS), Galdakao, Spain
| | - Eloísa Urrechaga
- CORE Laboratory, Hospital Galdakao-Usansolo, Galdakao, Vizcaya, Spain
- Biocruces Bizkaia Health Research Institute, Barakaldo, Vizcaya, Spain
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Zhang W, Zheng X, Li R, Liu M, Xiao W, Huang L, Xu F, Dong N, Li Y. Research on nonstroke dementia screening and cognitive function prediction model for older people based on brain atrophy characteristics. Brain Behav 2022; 12:e2726. [PMID: 36278400 PMCID: PMC9660432 DOI: 10.1002/brb3.2726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 07/12/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND Brain atrophy is an important feature in dementia and is meaningful to explore a brain atrophy model to predict dementia. Using machine learning algorithm to establish a dementia model and cognitive function model based on brain atrophy characteristics is unstoppable. METHOD We acquired 157 dementia and 156 normal old people.s clinical information and MRI data, which contains 44 brain atrophy features, including visual scale assessment of brain atrophy and multiple linear measurement indexes and brain atrophy index. Five machine learning models were used to establish prediction models for dementia, general cognition, and subcognitive domains. RESULTS The extreme Gradient Boosting (XGBoost) model had the best effect in predicting dementia, with a sensitivity of 0.645, a specificity of 0.839, and the area under curve (AUC) of 0.784. In this model, the important brain atrophy features for predicting dementia were temporal horn ratio, cella media index, suprasellar cistern ratio, and the thickness of the corpus callosum genu. CONCLUSION For nonstroke elderly people, the machine learning model based on clinical head MRI brain atrophy features had good predictive value for dementia, general cognitive impairment, immediate memory impairment, word fluency disorder, executive dysfunction, and visualspatial disorder.
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Affiliation(s)
- Wei Zhang
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xiaoran Zheng
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Renren Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Meng Liu
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Weixin Xiao
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Lihe Huang
- Research Center for Ageing, Language and Care at Tongji University, Shanghai, China
| | - Feiyang Xu
- iFlytek Research, iFlytek Co. Ltd, Hefei, China
| | - Ningxin Dong
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yunxia Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
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