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Milićević B, Milošević M, Simić V, Preveden A, Velicki L, Jakovljević Đ, Bosnić Z, Pičulin M, Žunkovič B, Kojić M, Filipović N. Machine learning and physical based modeling for cardiac hypertrophy. Heliyon 2023; 9:e16724. [PMID: 37313176 PMCID: PMC10258386 DOI: 10.1016/j.heliyon.2023.e16724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 05/23/2023] [Accepted: 05/25/2023] [Indexed: 06/15/2023] Open
Abstract
Background and objective Predicting the long-term expansion and remodeling of the left ventricle in patients is challenging task but it has the potential to be clinically very useful. Methods In our study, we present machine learning models based on random forests, gradient boosting, and neural networks, used to track cardiac hypertrophy. We collected data from multiple patients, and then the model was trained using the patient's medical history and present level of cardiac health. We also demonstrate a physical-based model, using the finite element procedure to simulate the development of cardiac hypertrophy. Results Our models were used to forecast the evolution of hypertrophy over six years. The machine learning model and finite element model provided similar results. Conclusions The finite element model is much slower, but it's more accurate compared to the machine learning model since it's based on physical laws guiding the hypertrophy process. On the other hand, the machine learning model is fast but the results can be less trustworthy in some cases. Both of our models, enable us to monitor the development of the disease. Because of its speed machine learning model is more likely to be used in clinical practice. Further improvements to our machine learning model could be achieved by collecting data from finite element simulations, adding them to the dataset, and retraining the model. This can result in a fast and more accurate model combining the advantages of physical-based and machine learning modeling.
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Affiliation(s)
- Bogdan Milićević
- Faculty of Engineering, University of Kragujevac, Kragujevac 34000, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac 34000, Serbia
| | - Miljan Milošević
- Bioengineering Research and Development Center (BioIRC), Kragujevac 34000, Serbia
- Institute for Information Technologies, University of Kragujevac, Kragujevac 34000, Serbia
- Belgrade Metropolitan University, Belgrade 11000, Serbia
| | - Vladimir Simić
- Bioengineering Research and Development Center (BioIRC), Kragujevac 34000, Serbia
- Institute for Information Technologies, University of Kragujevac, Kragujevac 34000, Serbia
| | - Andrej Preveden
- Faculty of Medicine, University of Novi Sad, Serbia and Institute of Cardiovascular Diseases Vojvodina, Sremska Kamenica, Serbia
| | - Lazar Velicki
- Faculty of Medicine, University of Novi Sad, Serbia and Institute of Cardiovascular Diseases Vojvodina, Sremska Kamenica, Serbia
| | - Đorđe Jakovljević
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
- Faculty of Health and Life Sciences, Coventry University, Coventry, UK
| | - Zoran Bosnić
- University of Ljubljana, Faculty of Computer and Information Science, Večna Pot 113, Ljubljana, Slovenia
| | - Matej Pičulin
- University of Ljubljana, Faculty of Computer and Information Science, Večna Pot 113, Ljubljana, Slovenia
| | - Bojan Žunkovič
- University of Ljubljana, Faculty of Computer and Information Science, Večna Pot 113, Ljubljana, Slovenia
| | - Miloš Kojić
- Bioengineering Research and Development Center (BioIRC), Kragujevac 34000, Serbia
- Serbian Academy of Sciences and Arts, Belgrade 11000, Serbia
- Houston Methodist Research Institute, Houston TX 77030, USA
| | - Nenad Filipović
- Faculty of Engineering, University of Kragujevac, Kragujevac 34000, Serbia
- Bioengineering Research and Development Center (BioIRC), Kragujevac 34000, Serbia
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Pičulin M, Smole T, Žunkovič B, Kokalj E, Robnik-Šikonja M, Kukar M, Fotiadis DI, Pezoulas VC, Tachos NS, Barlocco F, Mazzarotto F, Popović D, Maier LS, Velicki L, Olivotto I, MacGowan GA, Jakovljević DG, Filipović N, Bosnić Z. Disease Progression of Hypertrophic Cardiomyopathy: Modeling Using Machine Learning. JMIR Med Inform 2022; 10:e30483. [PMID: 35107432 PMCID: PMC8851344 DOI: 10.2196/30483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 10/27/2021] [Accepted: 12/04/2021] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Cardiovascular disorders in general are responsible for 30% of deaths worldwide. Among them, hypertrophic cardiomyopathy (HCM) is a genetic cardiac disease that is present in about 1 of 500 young adults and can cause sudden cardiac death (SCD). OBJECTIVE Although the current state-of-the-art methods model the risk of SCD for patients, to the best of our knowledge, no methods are available for modeling the patient's clinical status up to 10 years ahead. In this paper, we propose a novel machine learning (ML)-based tool for predicting disease progression for patients diagnosed with HCM in terms of adverse remodeling of the heart during a 10-year period. METHODS The method consisted of 6 predictive regression models that independently predict future values of 6 clinical characteristics: left atrial size, left atrial volume, left ventricular ejection fraction, New York Heart Association functional classification, left ventricular internal diastolic diameter, and left ventricular internal systolic diameter. We supplemented each prediction with the explanation that is generated using the Shapley additive explanation method. RESULTS The final experiments showed that predictive error is lower on 5 of the 6 constructed models in comparison to experts (on average, by 0.34) or a consortium of experts (on average, by 0.22). The experiments revealed that semisupervised learning and the artificial data from virtual patients help improve predictive accuracies. The best-performing random forest model improved R2 from 0.3 to 0.6. CONCLUSIONS By engaging medical experts to provide interpretation and validation of the results, we determined the models' favorable performance compared to the performance of experts for 5 of 6 targets.
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Affiliation(s)
- Matej Pičulin
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Tim Smole
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Bojan Žunkovič
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Enja Kokalj
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Marko Robnik-Šikonja
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Matjaž Kukar
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
| | - Vasileios C Pezoulas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
| | - Nikolaos S Tachos
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
| | - Fausto Barlocco
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.,Cardiomyopathy Unit, Careggi University Hospital, University of Florence, Florence, Italy
| | - Francesco Mazzarotto
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.,Cardiomyopathy Unit, Careggi University Hospital, University of Florence, Florence, Italy.,National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Dejana Popović
- Clinic for Cardiology, Clinical Center of Serbia, University of Belgrade, Belgrade, Serbia
| | - Lars S Maier
- Department of Internal Medicine II (Cardiology, Pneumology, Intensive Care Medicine), University Hospital Regensburg, Regensburg, Germany
| | - Lazar Velicki
- Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia.,Institute of Cardiovascular Diseases Vojvodina, Sremska Kamenica, Serbia
| | - Iacopo Olivotto
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.,Cardiomyopathy Unit, Careggi University Hospital, University of Florence, Florence, Italy
| | - Guy A MacGowan
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Djordje G Jakovljević
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom.,Faculty of Health and Life Sciences, Coventry University, Coventry, United Kingdom
| | - Nenad Filipović
- Bioengineering Research and Development Center, Kragujevac, Serbia
| | - Zoran Bosnić
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
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Smole T, Žunkovič B, Pičulin M, Kokalj E, Robnik-Šikonja M, Kukar M, Fotiadis DI, Pezoulas VC, Tachos NS, Barlocco F, Mazzarotto F, Popović D, Maier L, Velicki L, MacGowan GA, Olivotto I, Filipović N, Jakovljević DG, Bosnić Z. A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy. Comput Biol Med 2021; 135:104648. [PMID: 34280775 DOI: 10.1016/j.compbiomed.2021.104648] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 07/08/2021] [Accepted: 07/08/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND Machine learning (ML) and artificial intelligence are emerging as important components of precision medicine that enhance diagnosis and risk stratification. Risk stratification tools for hypertrophic cardiomyopathy (HCM) exist, but they are based on traditional statistical methods. The aim was to develop a novel machine learning risk stratification tool for the prediction of 5-year risk in HCM. The goal was to determine if its predictive accuracy is higher than the accuracy of the state-of-the-art tools. METHOD Data from a total of 2302 patients were used. The data were comprised of demographic characteristics, genetic data, clinical investigations, medications, and disease-related events. Four classification models were applied to model the risk level, and their decisions were explained using the SHAP (SHapley Additive exPlanations) method. Unwanted cardiac events were defined as sustained ventricular tachycardia occurrence (VT), heart failure (HF), ICD activation, sudden cardiac death (SCD), cardiac death, and all-cause death. RESULTS The proposed machine learning approach outperformed the similar existing risk-stratification models for SCD, cardiac death, and all-cause death risk-stratification: it achieved higher AUC by 17%, 9%, and 1%, respectively. The boosted trees achieved the best performing AUC of 0.82. The resulting model most accurately predicts VT, HF, and ICD with AUCs of 0.90, 0.88, and 0.87, respectively. CONCLUSIONS The proposed risk-stratification model demonstrates high accuracy in predicting events in patients with hypertrophic cardiomyopathy. The use of a machine-learning risk stratification model may improve patient management, clinical practice, and outcomes in general.
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Affiliation(s)
- Tim Smole
- University of Ljubljana, Faculty of Computer and Information Science, Večna Pot 113, Ljubljana, Slovenia
| | - Bojan Žunkovič
- University of Ljubljana, Faculty of Computer and Information Science, Večna Pot 113, Ljubljana, Slovenia
| | - Matej Pičulin
- University of Ljubljana, Faculty of Computer and Information Science, Večna Pot 113, Ljubljana, Slovenia
| | - Enja Kokalj
- University of Ljubljana, Faculty of Computer and Information Science, Večna Pot 113, Ljubljana, Slovenia
| | - Marko Robnik-Šikonja
- University of Ljubljana, Faculty of Computer and Information Science, Večna Pot 113, Ljubljana, Slovenia
| | - Matjaž Kukar
- University of Ljubljana, Faculty of Computer and Information Science, Večna Pot 113, Ljubljana, Slovenia
| | - Dimitrios I Fotiadis
- University of Ioannina, Dept. of Materials Science and Engineering, Unit of Medical Technology and Intelligent Information Systems, Greece
| | - Vasileios C Pezoulas
- University of Ioannina, Dept. of Materials Science and Engineering, Unit of Medical Technology and Intelligent Information Systems, Greece
| | - Nikolaos S Tachos
- University of Ioannina, Dept. of Materials Science and Engineering, Unit of Medical Technology and Intelligent Information Systems, Greece
| | - Fausto Barlocco
- Cardiomyopathy Unit, Careggi University Hospital, University of Florence, Italy
| | | | - Dejana Popović
- University of Belgrade, Clinic for Cardiology, Clinical Center of Serbia, Faculty of Pharmacy, Belgrade, Serbia
| | - Lars Maier
- University Hospital Regensburg, Dept. of Internal Medicine II (Cardiology, Pneumology, Intensive Care Medicine), Germany
| | - Lazar Velicki
- Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia and Institute of Cardiovascular Diseases Vojvodina, Sremska Kamenica, Serbia
| | - Guy A MacGowan
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
| | - Iacopo Olivotto
- Cardiomyopathy Unit, Careggi University Hospital, University of Florence, Italy
| | - Nenad Filipović
- BIOIRC - Bioengineering Research and Development Center, Kragujevac, Serbia
| | - Djordje G Jakovljević
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK; Faculty of Health and Life Sciences, Coventry University, Coventry, UK
| | - Zoran Bosnić
- University of Ljubljana, Faculty of Computer and Information Science, Večna Pot 113, Ljubljana, Slovenia.
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Pezoulas VC, Grigoriadis GI, Gkois G, Tachos NS, Smole T, Bosnić Z, Pičulin M, Olivotto I, Barlocco F, Robnik-Šikonja M, Jakovljevic DG, Goules A, Tzioufas AG, Fotiadis DI. A computational pipeline for data augmentation towards the improvement of disease classification and risk stratification models: A case study in two clinical domains. Comput Biol Med 2021; 134:104520. [PMID: 34118751 DOI: 10.1016/j.compbiomed.2021.104520] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 05/13/2021] [Accepted: 05/24/2021] [Indexed: 11/20/2022]
Abstract
Virtual population generation is an emerging field in data science with numerous applications in healthcare towards the augmentation of clinical research databases with significant lack of population size. However, the impact of data augmentation on the development of AI (artificial intelligence) models to address clinical unmet needs has not yet been investigated. In this work, we assess whether the aggregation of real with virtual patient data can improve the performance of the existing risk stratification and disease classification models in two rare clinical domains, namely the primary Sjögren's Syndrome (pSS) and the hypertrophic cardiomyopathy (HCM), for the first time in the literature. To do so, multivariate approaches, such as, the multivariate normal distribution (MVND), and straightforward ones, such as, the Bayesian networks, the artificial neural networks (ANNs), and the tree ensembles are compared against their performance towards the generation of high-quality virtual data. Both boosting and bagging algorithms, such as, the Gradient boosting trees (XGBoost), the AdaBoost and the Random Forests (RFs) were trained on the augmented data to evaluate the performance improvement for lymphoma classification and HCM risk stratification. Our results revealed the favorable performance of the tree ensemble generators, in both domains, yielding virtual data with goodness-of-fit 0.021 and KL-divergence 0.029 in pSS and 0.029, 0.027 in HCM, respectively. The application of the XGBoost on the augmented data revealed an increase by 10.9% in accuracy, 10.7% in sensitivity, 11.5% in specificity for lymphoma classification and 16.1% in accuracy, 16.9% in sensitivity, 13.7% in specificity in HCM risk stratification.
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Affiliation(s)
- Vasileios C Pezoulas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece
| | - Grigoris I Grigoriadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece
| | - George Gkois
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece
| | - Nikolaos S Tachos
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece
| | - Tim Smole
- Faculty of Computer and Information Science, University of Ljubljana, Večna Pot 113, 1000, Ljubljana, Slovenia
| | - Zoran Bosnić
- Faculty of Computer and Information Science, University of Ljubljana, Večna Pot 113, 1000, Ljubljana, Slovenia
| | - Matej Pičulin
- Faculty of Computer and Information Science, University of Ljubljana, Večna Pot 113, 1000, Ljubljana, Slovenia
| | - Iacopo Olivotto
- Department of Experimental and Clinical Medicine, University of Florence and Cardiomyopathies Unit, Azienda Ospedaliera Careggi, Florence, Italy
| | - Fausto Barlocco
- Department of Experimental and Clinical Medicine, University of Florence and Cardiomyopathies Unit, Azienda Ospedaliera Careggi, Florence, Italy
| | - Marko Robnik-Šikonja
- Faculty of Computer and Information Science, University of Ljubljana, Večna Pot 113, 1000, Ljubljana, Slovenia
| | - Djordje G Jakovljevic
- Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK and with the Faculty of Health and Life Sciences, Coventry University, Coventry, UK
| | - Andreas Goules
- Department of Pathophysiology, Faculty of Medicine, National and Kapodistrian University of Athens (NKUA), GR 15772, Athens, Greece
| | - Athanasios G Tzioufas
- Department of Pathophysiology, Faculty of Medicine, National and Kapodistrian University of Athens (NKUA), GR 15772, Athens, Greece
| | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece; Department of Biomedical Research, FORTH-IMBB, Ioannina, GR45110, Greece.
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