Parallel Computing [alpha]

Gianluca Mittone

Ph.D. student at Computer Science Department, University of Turin
Parallel Computing group
Via Pessinetto 12, 10149 Torino – Italy 
Email: gianluca.mittone@unito.it

Short Bio

Gianluca Mittone received his Bachelor’s Degree in Computer Science in 2017 with a thesis on the handling of exceptions in Description Logics, proposing the implementation of an algorithm for the automatic revision of ontologies exploiting a Typicality operator. He also received the Master’s Degree in Computer Science in 2019 with a master thesis on a novel distributed approach for deep learning, named NNT (Nearest Neighbours Training), which takes advantage of a locally synchronous approach to achieve a better trade-off between computational time and learning results.

After eight months as a research engineer at the Computer Science Department of the University of Turin, he is now a Ph.D. student in Modeling and Data Science at the same university, working on different projects involving HPC and Machine Learning techniques.

Fields of interest:

  • High-Performance Computing
  • Deep learning
  • Machine Learning
  • Federated Learning
  • RISC-V
  • SGX

Publications

2023

  • G. Mittone and S. Fonio, “Benchmarking Federated Learning Scalability,” in Proceedings of the 2nd Italian Conference on Big Data and Data Science, ITADATA 2023, September 11-13, 2023, Naples, Italy, 2023.
    [BibTeX] [Abstract] [Download PDF]

    Federated Learning (FL) is a widespread Machine Learning paradigm handling distributed Big Data. In this work, we demonstrate that different FL frameworks expose different scaling performances despite adopting the same technologies, highlighting the need for a more comprehensive study on the topic.

    @inproceedings{23:itadata:extabstract:mittone:fonio,
abstract = {Federated Learning (FL) is a widespread Machine Learning paradigm handling distributed Big Data. In this work, we demonstrate that different FL frameworks expose different scaling performances despite adopting the same technologies, highlighting the need for a more comprehensive study on the topic.},
author = {Mittone, Gianluca and Fonio, Samuele},
booktitle = {Proceedings of the 2nd Italian Conference on Big Data and Data Science, {ITADATA} 2023, September 11-13, 2023},
address = {Naples, Italy},
keywords = {icsc, eupilot},
title = {Benchmarking Federated Learning Scalability},
year = {2023},
publisher = {{CEUR}},
note = {In press},
month = {sep},
url = {https://hdl.handle.net/2318/1933852}
}

  • G. Mittone, W. Riviera, I. Colonnelli, R. Birke, and M. Aldinucci, “Model-Agnostic Federated Learning,” in Euro-Par 2023: Parallel Processing, Limassol, Cyprus, 2023. doi:10.1007/978-3-031-39698-4_26
    [BibTeX] [Abstract] [Download PDF]

    Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs). On the one hand, this allowed its development and widespread use as DNNs proliferated. On the other hand, it neglected all those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only allow training DNNs reinforces this problem. To address the lack of FL solutions for non-DNN-based use cases, we propose MAFL (Model-Agnostic Federated Learning). MAFL marries a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grade FL framework: Intel OpenFL. MAFL is the first FL system not tied to any specific type of machine learning model, allowing exploration of FL scenarios beyond DNNs and trees. We test MAFL from multiple points of view, assessing its correctness, flexibility and scaling properties up to 64 nodes. We optimised the base software achieving a 5.5x speedup on a standard FL scenario. MAFL is compatible with x86-64, ARM-v8, Power and RISC-V.

    @inproceedings{23:mittone:mafl,
abstract = {Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs). On the one hand, this allowed its development and widespread use as DNNs proliferated. On the other hand, it neglected all those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only allow training DNNs reinforces this problem. To address the lack of FL solutions for non-DNN-based use cases, we propose MAFL (Model-Agnostic Federated Learning). MAFL marries a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grade FL framework: Intel OpenFL. MAFL is the first FL system not tied to any specific type of machine learning model, allowing exploration of FL scenarios beyond DNNs and trees. We test MAFL from multiple points of view, assessing its correctness, flexibility and scaling properties up to 64 nodes. We optimised the base software achieving a 5.5x speedup on a standard FL scenario. MAFL is compatible with x86-64, ARM-v8, Power and RISC-V.},
address = {Limassol, Cyprus},
author = {Mittone, Gianluca and Riviera, Walter and Colonnelli, Iacopo and Birke, Robert and Aldinucci, Marco},
booktitle = {Euro-Par 2023: Parallel Processing},
date-added = {2023-03-8 21:51:14 +0000},
doi = {10.1007/978-3-031-39698-4_26},
institution = {Computer Science Department, University of Torino},
keywords = {eupilot, icsc},
month = {Aug},
note = {https://arxiv.org/abs/2303.04906},
publisher = {{Springer}},
title = {Model-Agnostic Federated Learning},
url = {https://doi.org/10.1007/978-3-031-39698-4_26},
year = {2023},
bdsk-url-1 = {https://doi.org/10.48550/arXiv.2303.04906}
}

  • G. Mittone, N. Tonci, R. Birke, I. Colonnelli, D. Medić, A. Bartolini, R. Esposito, E. Parisi, F. Beneventi, M. Polato, M. Torquati, L. Benini, and M. Aldinucci, “Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning,” in 20th ACM International Conference on Computing Frontiers (CF ’23), Bologna, Italy, 2023. doi:10.1145/3587135.3592211
    [BibTeX] [Abstract] [Download PDF]

    Decentralised Machine Learning (DML) enables collaborative machine learning without centralised input data. Federated Learning (FL) and Edge Inference are examples of DML. While tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel systems (e.g., RISC-V), non-fully connected topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing to map DML schemes to an underlying middleware, i.e. the FastFlow parallel programming library. We experiment with it by generating different working DML schemes on two emerging architectures (ARM-v8, RISC-V) and the x86-64 platform. We characterise the performance and energy efficiency of the presented schemes and systems. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge.

    @inproceedings{23:mittone:fl-riscv,
abstract = {Decentralised Machine Learning (DML) enables collaborative machine learning without centralised input data. Federated Learning (FL) and Edge Inference are examples of DML. While tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel systems (e.g., RISC-V), non-fully connected topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing to map DML schemes to an underlying middleware, i.e. the FastFlow parallel programming library. We experiment with it by generating different working DML schemes on two emerging architectures (ARM-v8, RISC-V) and the x86-64 platform. We characterise the performance and energy efficiency of the presented schemes and systems. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge.},
address = {Bologna, Italy},
author = {Mittone, Gianluca and Tonci, Nicol{\`o} and Birke, Robert and Colonnelli, Iacopo and Medi\'{c}, Doriana and Bartolini, Andrea and Esposito, Roberto and Parisi, Emanuele and Beneventi, Francesco and Polato, Mirko and Torquati, Massimo and Benini, Luca and Aldinucci, Marco},
booktitle = {20th {ACM} International Conference on Computing Frontiers ({CF} '23)},
date-added = {2023-03-14 15:34:00 +0000},
institution = {Computer Science Department, University of Torino},
isbn = {979-8-4007-0140-5/23/05},
keywords = {eupilot, icsc},
month = {May},
note = {https://arxiv.org/abs/2302.07946},
publisher = {{ACM}},
title = {Experimenting with Emerging {RISC-V} Systems for Decentralised Machine Learning},
url = {https://dl.acm.org/doi/pdf/10.1145/3587135.3592211},
year = {2023},
doi = {10.1145/3587135.3592211}
}

  • G. Mittone, F. Svoboda, M. Aldinucci, N. D. Lane, and P. Lio, “A Federated Learning Benchmark for Drug-Target Interaction,” in Companion Proceedings of the ACM Web Conference 2023 (WWW ’23 Companion), Austin, Texas, 2023. doi:10.1145/3543873.3587687
    [BibTeX] [Abstract] [Download PDF]

    Aggregating pharmaceutical data in the drug-target interaction (DTI) domain has the potential to deliver life-saving breakthroughs. It is, however, notoriously difficult due to regulatory constraints and commercial interests. This work proposes the application of federated learning, which we argue to be reconcilable with the industry’s constraints, as it does not require sharing of any information that would reveal the entities’ data or any other high-level summary of it. When used on a representative GraphDTA model and the KIBA dataset it achieves up to 15 percent improved performance relative to the best available non-privacy preserving alternative. Our extensive battery of experiments shows that, unlike in other domains, the non-IID data distribution in the DTI datasets does not deteriorate FL performance. Additionally, we identify a material trade-off between the benefits of adding new data, and the cost of adding more clients.

    @inproceedings{23:mittone:dti,
abstract = {Aggregating pharmaceutical data in the drug-target interaction (DTI) domain has the potential to deliver life-saving breakthroughs. It is, however, notoriously difficult due to regulatory constraints and commercial interests. This work proposes the application of federated learning, which we argue to be reconcilable with the industry's constraints, as it does not require sharing of any information that would reveal the entities' data or any other high-level summary of it. When used on a representative GraphDTA model and the KIBA dataset it achieves up to 15 percent improved performance relative to the best available non-privacy preserving alternative. Our extensive battery of experiments shows that, unlike in other domains, the non-IID data distribution in the DTI datasets does not deteriorate FL performance. Additionally, we identify a material trade-off between the benefits of adding new data, and the cost of adding more clients.},
address = {Austin, Texas},
author = {Mittone, Gianluca and Svoboda, Filip and Aldinucci, Marco and Lane, Nicholas D. and Lio, Pietro},
booktitle = {Companion Proceedings of the ACM Web Conference 2023 (WWW '23 Companion)},
date-added = {2023-03-14 15:34:00 +0000},
doi = {10.1145/3543873.3587687},
institution = {Computer Science Department, University of Torino},
isbn = {978-1-4503-9419-2/23/04},
keywords = {eupilot, icsc},
month = {Apr},
note = {https://arxiv.org/abs/2302.07684},
publisher = {{ACM}},
title = {A Federated Learning Benchmark for Drug-Target Interaction},
url = {https://hdl.handle.net/2318/1898472},
year = {2023},
bdsk-url-1 = {https://hdl.handle.net/2318/1898472},
bdsk-url-2 = {https://doi.org/10.1145/3543873.3587687}
}

  • W. Fornaciari, F. Reghenzani, F. Terraneo, D. Baroffio, C. Metra, M. Omana, J. R. E. Condia, M. S. Reorda, R. Birke, I. Colonnelli, G. Mittone, M. Aldinucci, G. Mencagli, F. Iannone, F. Palombi, G. Zummo, D. Cesarini, and F. Tesser, “RISC-V-based Platforms for HPC: Analyzing Non-functional Properties for Future HPC and Big-Data Clusters,” in Embedded Computer Systems: Architectures, Modeling, and Simulation – 23rd International Conference, SAMOS 2023, Samos, Greece, 2023.
    [BibTeX] [Abstract] [Download PDF]

    High-PerformanceComputing(HPC)haveevolvedtobeused to perform simulations of systems where physical experimentation is pro- hibitively impractical, expensive, or dangerous. This paper provides a general overview and showcases the analysis of non-functional properties in RISC-V-based platforms for HPCs. In particular, our analyses target the evaluation of power and energy control, thermal management, and reliability assessment of promising systems, structures, and technologies devised for current and future generation of HPC machines. The main set of design methodologies and technologies developed within the activ- ities of the Future and HPC & Big Data spoke of the National Centre of HPC, Big Data and Quantum Computing project are described along with the description of the testbed for experimenting two-phase cooling approaches.

    @inproceedings{23:SAMOS,
abstract = {High-PerformanceComputing(HPC)haveevolvedtobeused to perform simulations of systems where physical experimentation is pro- hibitively impractical, expensive, or dangerous. This paper provides a general overview and showcases the analysis of non-functional properties in RISC-V-based platforms for HPCs. In particular, our analyses target the evaluation of power and energy control, thermal management, and reliability assessment of promising systems, structures, and technologies devised for current and future generation of HPC machines. The main set of design methodologies and technologies developed within the activ- ities of the Future and HPC & Big Data spoke of the National Centre of HPC, Big Data and Quantum Computing project are described along with the description of the testbed for experimenting two-phase cooling approaches.},
address = {Samos, Greece},
author = {William Fornaciari and Federico Reghenzani and Federico Terraneo and Davide Baroffio and Cecilia Metra and Martin Omana and Josie E. Rodriguez Condia and Matteo Sonza Reorda and Robert Birke and Iacopo Colonnelli and Gianluca Mittone and Marco Aldinucci and Gabriele Mencagli and Francesco Iannone and Filippo Palombi and Giuseppe Zummo and Daniele Cesarini and Federico Tesser},
booktitle = {{Embedded Computer Systems: Architectures, Modeling, and Simulation - 23rd International Conference, {SAMOS} 2023}},
keywords = {icsc},
title = {{RISC-V}-based Platforms for {HPC}: Analyzing Non-functional Properties for Future {HPC} and {Big-Data} Clusters},
url = {https://iris.unito.it/retrieve/b627eab0-3aa1-4fd7-8685-f47c62c792b3/SAMOS_2023_CN_HPC_FL1.pdf},
year = {2023},
bdsk-url-1 = {https://iris.unito.it/retrieve/b627eab0-3aa1-4fd7-8685-f47c62c792b3/SAMOS_2023_CN_HPC_FL1.pdf}
}

  • I. Colonnelli, B. Casella, G. Mittone, Y. Arfat, B. Cantalupo, R. Esposito, A. R. Martinelli, D. Medić, and M. Aldinucci, “Federated Learning meets HPC and cloud,” in Astrophysics and Space Science Proceedings, Catania, Italy, 2023, p. 193–199. doi:10.1007/978-3-031-34167-0_39
    [BibTeX] [Abstract] [Download PDF]

    HPC and AI are fated to meet for several reasons. This article will discuss some of them and argue why this will happen through the set of methods and technologies that underpin cloud computing. As a paradigmatic example, we present a new federated learning system that collaboratively trains a deep learning model in different supercomputing centers. The system is based on the StreamFlow workflow manager designed for hybrid cloud-HPC infrastructures.

    @inproceedings{22:ml4astro,
abstract = {HPC and AI are fated to meet for several reasons. This article will discuss some of them and argue why this will happen through the set of methods and technologies that underpin cloud computing. As a paradigmatic example, we present a new federated learning system that collaboratively trains a deep learning model in different supercomputing centers. The system is based on the StreamFlow workflow manager designed for hybrid cloud-HPC infrastructures.},
address = {Catania, Italy},
author = {Iacopo Colonnelli and
Bruno Casella and
Gianluca Mittone and
Yasir Arfat and
Barbara Cantalupo and
Roberto Esposito and
Alberto Riccardo Martinelli and
Doriana Medi\'{c} and
Marco Aldinucci},
booktitle = {Astrophysics and Space Science Proceedings},
volume = {60},
pages = {193--199},
editor = {Bufano, Filomena and Riggi, Simone and Sciacca, Eva and Schilliro, Francesco},
keywords = {across, eupilot, streamflow},
publisher = {Springer},
title = {Federated Learning meets {HPC} and cloud},
url = {https://iris.unito.it/retrieve/5631da1c-96a0-48c0-a48e-2cdf6b84841d/main.pdf},
year = {2023},
doi = {10.1007/978-3-031-34167-0_39},
isbn = {978-3-031-34167-0},
bdsk-url-1 = {https://iris.unito.it/retrieve/5631da1c-96a0-48c0-a48e-2cdf6b84841d/main.pdf}
}

  • Y. Arfat, G. Mittone, I. Colonnelli, F. D’Ascenzo, R. Esposito, and M. Aldinucci, “Pooling critical datasets with Federated Learning,” in 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023, Napoli, Italy, 2023. doi:10.1109/PDP59025.2023.00057
    [BibTeX] [Abstract] [Download PDF]

    Federated Learning (FL) is becoming popular in different industrial sectors where data access is critical for security, privacy and the economic value of data itself. Unlike traditional machine learning, where all the data must be globally gathered for analysis, FL makes it possible to extract knowledge from data distributed across different organizations that can be coupled with different Machine Learning paradigms. In this work, we replicate, using Federated Learning, the analysis of a pooled dataset (with AdaBoost) that has been used to define the PRAISE score, which is today among the most accurate scores to evaluate the risk of a second acute myocardial infarction. We show that thanks to the extended-OpenFL framework, which implements AdaBoost.F, we can train a federated PRAISE model that exhibits comparable accuracy and recall as the centralised model. We achieved F1 and F2 scores which are consistently comparable to the PRAISE score study of a 16- parties federation but within an order of magnitude less time.

    @inproceedings{23:praise-fl:pdp,
abstract = {Federated Learning (FL) is becoming popular in different industrial sectors where data access is critical for security, privacy and the economic value of data itself. Unlike traditional machine learning, where all the data must be globally gathered for analysis, FL makes it possible to extract knowledge from data distributed across different organizations that can be coupled with different Machine Learning paradigms. In this work, we replicate, using Federated Learning, the analysis of a pooled dataset (with AdaBoost) that has been used to define the PRAISE score, which is today among the most accurate scores to evaluate the risk of a second acute myocardial infarction. We show that thanks to the extended-OpenFL framework, which implements AdaBoost.F, we can train a federated PRAISE model that exhibits comparable accuracy and recall as the centralised model. We achieved F1 and F2 scores which are consistently comparable to the PRAISE score study of a 16- parties federation but within an order of magnitude less time.},
address = {Napoli, Italy},
author = {Yasir Arfat and Gianluca Mittone and Iacopo Colonnelli and Fabrizio D'Ascenzo and Roberto Esposito and Marco Aldinucci},
booktitle = {31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, {PDP} 2023},
date-added = {2023-02-04 18:16:36 +0100},
date-modified = {2023-02-04 18:34:25 +0100},
keywords = {admire, hpc4ai, c3s},
publisher = {IEEE},
doi = {10.1109/PDP59025.2023.00057},
title = {Pooling critical datasets with Federated Learning},
url = {https://iris.unito.it/retrieve/491e22ec-3db5-4989-a063-085a199edd20/23_pdp_fl.pdf},
year = {2023},
bdsk-url-1 = {https://iris.unito.it/retrieve/491e22ec-3db5-4989-a063-085a199edd20/23_pdp_fl.pdf}
}

2022

  • G. Agosta, M. Aldinucci, C. Alvarez, R. Ammendola, Y. Arfat, O. Beaumont, M. Bernaschi, A. Biagioni, T. Boccali, B. Bramas, C. Brandolese, B. Cantalupo, M. Carrozzo, D. Cattaneo, A. Celestini, M. Celino, I. Colonnelli, P. Cretaro, P. D’Ambra, M. Danelutto, R. Esposito, L. Eyraud-Dubois, A. Filgueras, W. Fornaciari, O. Frezza, A. Galimberti, F. Giacomini, B. Goglin, D. Gregori, A. Guermouche, F. Iannone, M. Kulczewski, F. Lo Cicero, A. Lonardo, A. R. Martinelli, M. Martinelli, X. Martorell, G. Massari, S. Montangero, G. Mittone, R. Namyst, A. Oleksiak, P. Palazzari, P. S. Paolucci, F. Reghenzani, C. Rossi, S. Saponara, F. Simula, F. Terraneo, S. Thibault, M. Torquati, M. Turisini, P. Vicini, M. Vidal, D. Zoni, and G. Zummo, “Towards EXtreme scale technologies and accelerators for euROhpc hw/Sw supercomputing applications for exascale: The TEXTAROSSA approach,” Microprocessors and Microsystems, vol. 95, p. 104679, 2022. doi:10.1016/j.micpro.2022.104679
    [BibTeX] [Abstract]

    In the near future, Exascale systems will need to bridge three technology gaps to achieve high performance while remaining under tight power constraints: energy efficiency and thermal control; extreme computation efficiency via HW acceleration and new arithmetic; methods and tools for seamless integration of reconfigurable accelerators in heterogeneous HPC multi-node platforms. TEXTAROSSA addresses these gaps through a co-design approach to heterogeneous HPC solutions, supported by the integration and extension of HW and SW IPs, programming models, and tools derived from European research.

    @article{textarossa2022micpro:,
abstract = {In the near future, Exascale systems will need to bridge three technology gaps to achieve high performance while remaining under tight power constraints: energy efficiency and thermal control; extreme computation efficiency via HW acceleration and new arithmetic; methods and tools for seamless integration of reconfigurable accelerators in heterogeneous HPC multi-node platforms. TEXTAROSSA addresses these gaps through a co-design approach to heterogeneous HPC solutions, supported by the integration and extension of HW and SW IPs, programming models, and tools derived from European research.},
author = {Giovanni Agosta and Marco Aldinucci and Carlos Alvarez and Roberto Ammendola and Yasir Arfat and Olivier Beaumont and Massimo Bernaschi and Andrea Biagioni and Tommaso Boccali and Berenger Bramas and Carlo Brandolese and Barbara Cantalupo and Mauro Carrozzo and Daniele Cattaneo and Alessandro Celestini and Massimo Celino and Iacopo Colonnelli and Paolo Cretaro and Pasqua D'Ambra and Marco Danelutto and Roberto Esposito and Lionel Eyraud-Dubois and Antonio Filgueras and William Fornaciari and Ottorino Frezza and Andrea Galimberti and Francesco Giacomini and Brice Goglin and Daniele Gregori and Abdou Guermouche and Francesco Iannone and Michal Kulczewski and Francesca {Lo Cicero} and Alessandro Lonardo and Alberto R. Martinelli and Michele Martinelli and Xavier Martorell and Giuseppe Massari and Simone Montangero and Gianluca Mittone and Raymond Namyst and Ariel Oleksiak and Paolo Palazzari and Pier Stanislao Paolucci and Federico Reghenzani and Cristian Rossi and Sergio Saponara and Francesco Simula and Federico Terraneo and Samuel Thibault and Massimo Torquati and Matteo Turisini and Piero Vicini and Miquel Vidal and Davide Zoni and Giuseppe Zummo},
doi = {10.1016/j.micpro.2022.104679},
issn = {0141-9331},
journal = {Microprocessors and Microsystems},
keywords = {textrossa},
pages = {104679},
title = {Towards EXtreme scale technologies and accelerators for euROhpc hw/Sw supercomputing applications for exascale: The TEXTAROSSA approach},
volume = {95},
year = {2022},
bdsk-url-1 = {https://doi.org/10.1016/j.micpro.2022.104679}
}

  • G. Gallone, J. Kang, F. Bruno, J. Han, O. De Filippo, H. Yang, M. Doronzo, K. Park, G. Mittone, H. Kang, R. Parma, H. Gwon, E. Cerrato, W. J. Chun, G. Smolka, S. Hur, G. Helft, S. H. Han, S. Muscoli, Y. B. Song, F. Figini, K. H. Choi, G. Boccuzzi, S. Hong, D. Trabattoni, C. Nam, M. Giammaria, H. Kim, F. Conrotto, J. Escaned, C. Di Mario, F. D’Ascenzo, B. Koo, and G. M. de Ferrari, “Impact of Left Ventricular Ejection Fraction on Procedural and Long-Term Outcomes of Bifurcation Percutaneous Coronary Intervention,” The American Journal of Cardiology, vol. 172, pp. 18-25, 2022. doi:https://doi.org/10.1016/j.amjcard.2022.02.015
    [BibTeX] [Abstract] [Download PDF]

    {The association of left ventricular ejection fraction (LVEF) with procedural and long-term outcomes after state-of-the-art percutaneous coronary intervention (PCI) of bifurcation lesions remains unsettled. A total of 5,333 patients who underwent contemporary coronary bifurcation PCI were included in the intercontinental retrospective combined insights from the unified RAIN (veRy thin stents for patients with left mAIn or bifurcatioN in real life) and COBIS (COronary BIfurcation Stenting) III bifurcation registries. Of 5,003 patients (93.8%) with known baseline LVEF, 244 (4.9%) had LVEF <40% (bifurcation with reduced ejection fraction [BIFrEF] group), 430 (8.6%) had LVEF 40% to 49% (bifurcation with mildly reduced ejection fraction [BIFmEF] group) and 4,329 (86.5%) had ejection fraction (EF) ≥50% (bifurcation with preserved ejection fraction [BIFpEF] group). The primary end point was the Kaplan-Meier estimate of major adverse cardiac events (MACEs) (a composite of all-cause death, myocardial infarction, and target vessel revascularization). Patients with BIFrEF had a more complex clinical profile and coronary anatomy. No difference in procedural (30 days) MACE was observed across EF categories, also after adjustment for in-study outcome predictors (BIFrEF vs BIFmEF: adjusted hazard ratio [adj-HR] 1.39, 95% confidence interval [CI] 0.37 to 5.21

    @article{GALLONE202218,
abstract = {The association of left ventricular ejection fraction (LVEF) with procedural and long-term outcomes after state-of-the-art percutaneous coronary intervention (PCI) of bifurcation lesions remains unsettled. A total of 5,333 patients who underwent contemporary coronary bifurcation PCI were included in the intercontinental retrospective combined insights from the unified RAIN (veRy thin stents for patients with left mAIn or bifurcatioN in real life) and COBIS (COronary BIfurcation Stenting) III bifurcation registries. Of 5,003 patients (93.8%) with known baseline LVEF, 244 (4.9%) had LVEF <40% (bifurcation with reduced ejection fraction [BIFrEF] group), 430 (8.6%) had LVEF 40% to 49% (bifurcation with mildly reduced ejection fraction [BIFmEF] group) and 4,329 (86.5%) had ejection fraction (EF) ≥50% (bifurcation with preserved ejection fraction [BIFpEF] group). The primary end point was the Kaplan-Meier estimate of major adverse cardiac events (MACEs) (a composite of all-cause death, myocardial infarction, and target vessel revascularization). Patients with BIFrEF had a more complex clinical profile and coronary anatomy. No difference in procedural (30 days) MACE was observed across EF categories, also after adjustment for in-study outcome predictors (BIFrEF vs BIFmEF: adjusted hazard ratio [adj-HR] 1.39, 95% confidence interval [CI] 0.37 to 5.21, p = 0.626; BIFrEF vs BIFpEF: adj-HR 1.11, 95% CI 0.25 to 2.87, p = 0.883; BIFmEF vs BIFpEF: adj-HR 0.81, 95% CI 0.29 to 2.27, p = 0.683). BIFrEF was independently associated with long-term MACE (median follow-up 21 months, interquartile range 10 to 21 months) than both BIFmEF (adj-HR 2.20, 95% CI 1.41 to 3.41, p <0.001) and BIFpEF (adj-HR 1.91, 95% CI 1.41 to 2.60, p <0.001) groups, although no difference was observed between BIFmEF and BIFpEF groups (adj-HR 0.87, 95% CI 0.61 to 1.24, p = 0.449). In conclusion, in patients who underwent PCI of a coronary bifurcation lesion according to contemporary clinical practice, reduced LVEF (<40%), although a strong predictor of long-term MACEs, does not affect procedural outcomes.},
author = {Guglielmo Gallone and Jeehoon Kang and Francesco Bruno and Jung-Kyu Han and Ovidio {De Filippo} and Han-Mo Yang and Mattia Doronzo and Kyung-Woo Park and Gianluca Mittone and Hyun-Jae Kang and Radoslaw Parma and Hyeon-Cheol Gwon and Enrico Cerrato and Woo Jung Chun and Grzegorz Smolka and Seung-Ho Hur and Gerard Helft and Seung Hwan Han and Saverio Muscoli and Young Bin Song and Filippo Figini and Ki Hong Choi and Giacomo Boccuzzi and Soon-Jun Hong and Daniela Trabattoni and Chang-Wook Nam and Massimo Giammaria and Hyo-Soo Kim and Federico Conrotto and Javier Escaned and Carlo {Di Mario} and Fabrizio D'Ascenzo and Bon-Kwon Koo and Gaetano Maria {de Ferrari}},
doi = {https://doi.org/10.1016/j.amjcard.2022.02.015},
issn = {0002-9149},
journal = {The American Journal of Cardiology},
pages = {18-25},
title = {Impact of Left Ventricular Ejection Fraction on Procedural and Long-Term Outcomes of Bifurcation Percutaneous Coronary Intervention},
url = {https://www.sciencedirect.com/science/article/pii/S0002914922001692},
volume = {172},
year = {2022},
bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S0002914922001692},
bdsk-url-2 = {https://doi.org/10.1016/j.amjcard.2022.02.015}
}

2021

  • G. Agosta, W. Fornaciari, A. Galimberti, G. Massari, F. Reghenzani, F. Terraneo, D. Zoni, C. Brandolese, M. Celino, F. Iannone, P. Palazzari, G. Zummo, M. Bernaschi, P. D'Ambra, S. Saponara, M. Danelutto, M. Torquati, M. Aldinucci, Y. Arfat, B. Cantalupo, I. Colonnelli, R. Esposito, A. R. Martinelli, G. Mittone, O. Beaumont, B. Bramas, L. Eyraud-Dubois, B. Goglin, A. Guermouche, R. Namyst, S. Thibault, A. Filgueras, M. Vidal, C. Alvarez, X. Martorell, A. Oleksiak, M. Kulczewski, A. Lonardo, P. Vicini, F. L. Cicero, F. Simula, A. Biagioni, P. Cretaro, O. Frezza, P. S. Paolucci, M. Turisini, F. Giacomini, T. Boccali, S. Montangero, and R. Ammendola, "TEXTAROSSA: Towards EXtreme scale Technologies and Accelerators for euROhpc hw/Sw Supercomputing Applications for exascale," in Proc. of the 24th Euromicro Conference on Digital System Design (DSD), Palermo, Italy, 2021. doi:10.1109/DSD53832.2021.00051
    [BibTeX] [Abstract]

    To achieve high performance and high energy effi- ciency on near-future exascale computing systems, three key technology gaps needs to be bridged. These gaps include: en- ergy efficiency and thermal control; extreme computation effi- ciency via HW acceleration and new arithmetics; methods and tools for seamless integration of reconfigurable accelerators in heterogeneous HPC multi-node platforms. TEXTAROSSA aims at tackling this gap through a co-design approach to heterogeneous HPC solutions, supported by the integration and extension of HW and SW IPs, programming models and tools derived from European research.

    @inproceedings{21:DSD:textarossa,
abstract = {To achieve high performance and high energy effi- ciency on near-future exascale computing systems, three key technology gaps needs to be bridged. These gaps include: en- ergy efficiency and thermal control; extreme computation effi- ciency via HW acceleration and new arithmetics; methods and tools for seamless integration of reconfigurable accelerators in heterogeneous HPC multi-node platforms. TEXTAROSSA aims at tackling this gap through a co-design approach to heterogeneous HPC solutions, supported by the integration and extension of HW and SW IPs, programming models and tools derived from European research.},
address = {Palermo, Italy},
author = {Giovanni Agosta and William Fornaciari and Andrea Galimberti and Giuseppe Massari and Federico Reghenzani and Federico Terraneo and Davide Zoni and Carlo Brandolese and Massimo Celino and Francesco Iannone and Paolo Palazzari and Giuseppe Zummo and Massimo Bernaschi and Pasqua D'Ambra and Sergio Saponara and Marco Danelutto and Massimo Torquati and Marco Aldinucci and Yasir Arfat and Barbara Cantalupo and Iacopo Colonnelli and Roberto Esposito and Alberto Riccardo Martinelli and Gianluca Mittone and Olivier Beaumont and Berenger Bramas and Lionel Eyraud-Dubois and Brice Goglin and Abdou Guermouche and Raymond Namyst and Samuel Thibault and Antonio Filgueras and Miquel Vidal and Carlos Alvarez and Xavier Martorell and Ariel Oleksiak and Michal Kulczewski and Alessandro Lonardo and Piero Vicini and Francesco Lo Cicero and Francesco Simula and Andrea Biagioni and Paolo Cretaro and Ottorino Frezza and Pier Stanislao Paolucci and Matteo Turisini and Francesco Giacomini and Tommaso Boccali and Simone Montangero and Roberto Ammendola},
booktitle = {Proc. of the 24th Euromicro Conference on Digital System Design ({DSD})},
date-added = {2021-09-04 12:07:42 +0200},
date-modified = {2021-09-04 12:23:41 +0200},
doi = {10.1109/DSD53832.2021.00051},
keywords = {textarossa, streamflow},
month = aug,
publisher = {IEEE},
title = {{TEXTAROSSA}: Towards EXtreme scale Technologies and Accelerators for euROhpc hw/Sw Supercomputing Applications for exascale},
year = {2021},
bdsk-url-1 = {https://doi.org/10.1109/DSD53832.2021.00051}
}

  • M. Aldinucci, V. Cesare, I. Colonnelli, A. R. Martinelli, G. Mittone, and B. Cantalupo, "Practical Parallelizazion of a Laplace Solver with MPI," in ENEA CRESCO in the fight against COVID-19, 2021, p. 21–24.
    [BibTeX] [Abstract]

    This work exposes a practical methodology for the semi-automatic parallelization of existing code. We show how a scientific sequential code can be parallelized through our approach. The obtained parallel code is only slightly different from the starting sequential one, providing an example of how little re-designing our methodology involves. The performance of the parallelized code, executed on the CRESCO6 cluster, is then exposed and discussed. We also believe in the educational value of this approach and suggest its use as a teaching device for students.

    @inproceedings{21:laplace:enea,
abstract = {This work exposes a practical methodology for the semi-automatic parallelization of existing code. We show how a scientific sequential code can be parallelized through our approach. The obtained parallel code is only slightly different from the starting sequential one, providing an example of how little re-designing our methodology involves. The performance of the parallelized code, executed on the CRESCO6 cluster, is then exposed and discussed. We also believe in the educational value of this approach and suggest its use as a teaching device for students.},
author = {Aldinucci, Marco and Cesare, Valentina and Colonnelli, Iacopo and Martinelli, Alberto Riccardo and Mittone, Gianluca and Cantalupo, Barbara},
booktitle = {ENEA CRESCO in the fight against COVID-19},
editor = {Francesco Iannone},
keywords = {hpc4ai},
pages = {21--24},
publisher = {ENEA},
title = {Practical Parallelizazion of a {Laplace} Solver with {MPI}},
year = {2021}
}

  • Y. Arfat, G. Mittone, R. Esposito, B. Cantalupo, G. M. De Ferrari, and M. Aldinucci, "A Review of Machine Learning for Cardiology," Minerva cardiology and angiology, 2021. doi:10.23736/s2724-5683.21.05709-4
    [BibTeX] [Abstract] [Download PDF]

    This paper reviews recent cardiology literature and reports how Artificial Intelligence Tools (specifically, Machine Learning techniques) are being used by physicians in the field. Each technique is introduced with enough details to allow the understanding of how it works and its intent, but without delving into details that do not add immediate benefits and require expertise in the field. We specifically focus on the principal Machine Learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in the field and suggest why this is the case based on our expertise in Machine Learning. We complete the analysis by reviewing how corresponding statistical approaches compare with them. Finally, we discuss the main open issues in applying Machine Learning tools to cardiology tasks, also drafting possible future directions. Despite the growing interest in these tools, we argue that there are many still underutilized techniques: while Neural Networks are slowly being incorporated in cardiovascular research, other important techniques such as Semi-Supervised Learning and Federated Learning are still underutilized. The former would allow practitioners to harness the information contained in large datasets that are only partially labeled, while the latter would foster collaboration between institutions allowing building larger and better models.

    @article{21:ai4numbers:minerva,
abstract = {This paper reviews recent cardiology literature and reports how Artificial Intelligence Tools (specifically, Machine Learning techniques) are being used by physicians in the field. Each technique is introduced with enough details to allow the understanding of how it works and its intent, but without delving into details that do not add immediate benefits and require expertise in the field. We specifically focus on the principal Machine Learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in the field and suggest why this is the case based on our expertise in Machine Learning. We complete the analysis by reviewing how corresponding statistical approaches compare with them. Finally, we discuss the main open issues in applying Machine Learning tools to cardiology tasks, also drafting possible future directions. Despite the growing interest in these tools, we argue that there are many still underutilized techniques: while Neural Networks are slowly being incorporated in cardiovascular research, other important techniques such as Semi-Supervised Learning and Federated Learning are still underutilized. The former would allow practitioners to harness the information contained in large datasets that are only partially labeled, while the latter would foster collaboration between institutions allowing building larger and better models.},
author = {Yasir Arfat and Gianluca Mittone and Roberto Esposito and Barbara Cantalupo and Gaetano Maria {De Ferrari} and Marco Aldinucci},
date-added = {2021-08-09 23:00:12 +0200},
date-modified = {2021-08-09 23:05:36 +0200},
doi = {10.23736/s2724-5683.21.05709-4},
journal = {Minerva cardiology and angiology},
keywords = {deephealth, hpc4ai},
title = {A Review of Machine Learning for Cardiology},
url = {https://iris.unito.it/retrieve/handle/2318/1796298/780512/21_AI4numbers-preprint.pdf},
year = {2021},
bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1796298/780512/21_AI4numbers-preprint.pdf},
bdsk-url-2 = {https://doi.org/10.23736/s2724-5683.21.05709-4}
}

  • M. Aldinucci, V. Cesare, I. Colonnelli, A. R. Martinelli, G. Mittone, B. Cantalupo, C. Cavazzoni, and M. Drocco, "Practical Parallelization of Scientific Applications with OpenMP, OpenACC and MPI," Journal of Parallel and Distributed Computing, vol. 157, pp. 13-29, 2021. doi:10.1016/j.jpdc.2021.05.017
    [BibTeX] [Abstract] [Download PDF]

    This work aims at distilling a systematic methodology to modernize existing sequential scientific codes with a little re-designing effort, turning an old codebase into \emph{modern} code, i.e., parallel and robust code. We propose a semi-automatic methodology to parallelize scientific applications designed with a purely sequential programming mindset, possibly using global variables, aliasing, random number generators, and stateful functions. We demonstrate that the same methodology works for the parallelization in the shared memory model (via OpenMP), message passing model (via MPI), and General Purpose Computing on GPU model (via OpenACC). The method is demonstrated parallelizing four real-world sequential codes in the domain of physics and material science. The methodology itself has been distilled in collaboration with MSc students of the Parallel Computing course at the University of Torino, that applied it for the first time to the project works that they presented for the final exam of the course. Every year the course hosts some special lectures from industry representatives, who present how they use parallel computing and offer codes to be parallelized.

    @article{21:jpdc:loop,
abstract = {This work aims at distilling a systematic methodology to modernize existing sequential scientific codes with a little re-designing effort, turning an old codebase into \emph{modern} code, i.e., parallel and robust code. We propose a semi-automatic methodology to parallelize scientific applications designed with a purely sequential programming mindset, possibly using global variables, aliasing, random number generators, and stateful functions. We demonstrate that the same methodology works for the parallelization in the shared memory model (via OpenMP), message passing model (via MPI), and General Purpose Computing on GPU model (via OpenACC). The method is demonstrated parallelizing four real-world sequential codes in the domain of physics and material science. The methodology itself has been distilled in collaboration with MSc students of the Parallel Computing course at the University of Torino, that applied it for the first time to the project works that they presented for the final exam of the course. Every year the course hosts some special lectures from industry representatives, who present how they use parallel computing and offer codes to be parallelized. },
author = {Aldinucci, Marco and Cesare, Valentina and Colonnelli, Iacopo and Martinelli, Alberto Riccardo and Mittone, Gianluca and Cantalupo, Barbara and Cavazzoni, Carlo and Drocco, Maurizio},
date-added = {2021-06-10 22:05:54 +0200},
date-modified = {2021-06-10 22:30:05 +0200},
doi = {10.1016/j.jpdc.2021.05.017},
journal = {Journal of Parallel and Distributed Computing},
keywords = {saperi},
pages = {13-29},
title = {Practical Parallelization of Scientific Applications with {OpenMP, OpenACC and MPI}},
url = {https://iris.unito.it/retrieve/handle/2318/1792557/770851/Practical_Parallelization_JPDC_preprint.pdf},
volume = {157},
year = {2021},
bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1792557/770851/Practical_Parallelization_JPDC_preprint.pdf},
bdsk-url-2 = {https://doi.org/10.1016/j.jpdc.2021.05.017}
}

  • F. D'Ascenzo, O. De Filippo, G. Gallone, G. Mittone, M. A. Deriu, M. Iannaccone, A. Ariza-Solé, C. Liebetrau, S. Manzano-Fernández, G. Quadri, T. Kinnaird, G. Campo, J. P. Simao Henriques, J. M. Hughes, A. Dominguez-Rodriguez, M. Aldinucci, U. Morbiducci, G. Patti, S. Raposeiras-Roubin, E. Abu-Assi, G. M. De Ferrari, F. Piroli, A. Saglietto, F. Conrotto, P. Omedé, A. Montefusco, M. Pennone, F. Bruno, P. P. Bocchino, G. Boccuzzi, E. Cerrato, F. Varbella, M. Sperti, S. B. Wilton, L. Velicki, I. Xanthopoulou, A. Cequier, A. Iniguez-Romo, I. Munoz Pousa, M. Cespon Fernandez, B. Caneiro Queija, R. Cobas-Paz, A. Lopez-Cuenca, A. Garay, P. F. Blanco, A. Rognoni, G. Biondi Zoccai, S. Biscaglia, I. Nunez-Gil, T. Fujii, A. Durante, X. Song, T. Kawaji, D. Alexopoulos, Z. Huczek, J. R. Gonzalez Juanatey, S. Nie, M. Kawashiri, I. Colonnelli, B. Cantalupo, R. Esposito, S. Leonardi, W. Grosso Marra, A. Chieffo, U. Michelucci, D. Piga, M. Malavolta, S. Gili, M. Mennuni, C. Montalto, L. Oltrona Visconti, and Y. Arfat, "Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets," The Lancet, vol. 397, iss. 10270, pp. 199-207, 2021. doi:10.1016/S0140-6736(20)32519-8
    [BibTeX] [Abstract] [Download PDF]

    Background The accuracy of current prediction tools for ischaemic and bleeding events after an acute coronary syndrome (ACS) remains insufficient for individualised patient management strategies. We developed a machine learning-based risk stratification model to predict all-cause death, recurrent acute myocardial infarction, and major bleeding after ACS. Methods Different machine learning models for the prediction of 1-year post-discharge all-cause death, myocardial infarction, and major bleeding (defined as Bleeding Academic Research Consortium type 3 or 5) were trained on a cohort of 19826 adult patients with ACS (split into a training cohort [80%] and internal validation cohort [20%]) from the BleeMACS and RENAMI registries, which included patients across several continents. 25 clinical features routinely assessed at discharge were used to inform the models. The best-performing model for each study outcome (the PRAISE score) was tested in an external validation cohort of 3444 patients with ACS pooled from a randomised controlled trial and three prospective registries. Model performance was assessed according to a range of learning metrics including area under the receiver operating characteristic curve (AUC). Findings The PRAISE score showed an AUC of 0.82 (95% CI 0.78-0.85) in the internal validation cohort and 0.92 (0.90-0.93) in the external validation cohort for 1-year all-cause death; an AUC of 0.74 (0.70-0.78) in the internal validation cohort and 0.81 (0.76-0.85) in the external validation cohort for 1-year myocardial infarction; and an AUC of 0.70 (0.66-0.75) in the internal validation cohort and 0.86 (0.82-0.89) in the external validation cohort for 1-year major bleeding. Interpretation A machine learning-based approach for the identification of predictors of events after an ACS is feasible and effective. The PRAISE score showed accurate discriminative capabilities for the prediction of all-cause death, myocardial infarction, and major bleeding, and might be useful to guide clinical decision making.

    @article{21:lancet,
abstract = {Background The accuracy of current prediction tools for ischaemic and bleeding events after an acute coronary syndrome (ACS) remains insufficient for individualised patient management strategies. We developed a machine learning-based risk stratification model to predict all-cause death, recurrent acute myocardial infarction, and major bleeding after ACS.
Methods Different machine learning models for the prediction of 1-year post-discharge all-cause death, myocardial infarction, and major bleeding (defined as Bleeding Academic Research Consortium type 3 or 5) were trained on a cohort of 19826 adult patients with ACS (split into a training cohort [80%] and internal validation cohort [20%]) from the BleeMACS and RENAMI registries, which included patients across several continents. 25 clinical features routinely assessed at discharge were used to inform the models. The best-performing model for each study outcome (the PRAISE score) was tested in an external validation cohort of 3444 patients with ACS pooled from a randomised controlled trial and three prospective registries. Model performance was assessed according to a range of learning metrics including area under the receiver operating characteristic curve (AUC).
Findings The PRAISE score showed an AUC of 0.82 (95% CI 0.78-0.85) in the internal validation cohort and 0.92 (0.90-0.93) in the external validation cohort for 1-year all-cause death; an AUC of 0.74 (0.70-0.78) in the internal validation cohort and 0.81 (0.76-0.85) in the external validation cohort for 1-year myocardial infarction; and an AUC of 0.70 (0.66-0.75) in the internal validation cohort and 0.86 (0.82-0.89) in the external validation cohort for 1-year major bleeding.
Interpretation A machine learning-based approach for the identification of predictors of events after an ACS is feasible and effective. The PRAISE score showed accurate discriminative capabilities for the prediction of all-cause death, myocardial infarction, and major bleeding, and might be useful to guide clinical decision making.},
author = {Fabrizio D'Ascenzo and Ovidio {De Filippo} and Guglielmo Gallone and Gianluca Mittone and Marco Agostino Deriu and Mario Iannaccone and Albert Ariza-Sol\'e and Christoph Liebetrau and Sergio Manzano-Fern\'andez and Giorgio Quadri and Tim Kinnaird and Gianluca Campo and Jose Paulo {Simao Henriques} and James M Hughes and Alberto Dominguez-Rodriguez and Marco Aldinucci and Umberto Morbiducci and Giuseppe Patti and Sergio Raposeiras-Roubin and Emad Abu-Assi and Gaetano Maria {De Ferrari} and Francesco Piroli and Andrea Saglietto and Federico Conrotto and Pierluigi Omed\'e and Antonio Montefusco and Mauro Pennone and Francesco Bruno and Pier Paolo Bocchino and Giacomo Boccuzzi and Enrico Cerrato and Ferdinando Varbella and Michela Sperti and Stephen B. Wilton and Lazar Velicki and Ioanna Xanthopoulou and Angel Cequier and Andres Iniguez-Romo and Isabel {Munoz Pousa} and Maria {Cespon Fernandez} and Berenice {Caneiro Queija} and Rafael Cobas-Paz and Angel Lopez-Cuenca and Alberto Garay and Pedro Flores Blanco and Andrea Rognoni and Giuseppe {Biondi Zoccai} and Simone Biscaglia and Ivan Nunez-Gil and Toshiharu Fujii and Alessandro Durante and Xiantao Song and Tetsuma Kawaji and Dimitrios Alexopoulos and Zenon Huczek and Jose Ramon {Gonzalez Juanatey} and Shao-Ping Nie and Masa-aki Kawashiri and Iacopo Colonnelli and Barbara Cantalupo and Roberto Esposito and Sergio Leonardi and Walter {Grosso Marra} and Alaide Chieffo and Umberto Michelucci and Dario Piga and Marta Malavolta and Sebastiano Gili and Marco Mennuni and Claudio Montalto and Luigi {Oltrona Visconti} and Yasir Arfat},
date-modified = {2021-03-26 23:53:19 +0100},
doi = {10.1016/S0140-6736(20)32519-8},
issn = {0140-6736},
journal = {The Lancet},
keywords = {deephealth, hpc4ai},
number = {10270},
pages = {199-207},
title = {Machine learning-based prediction of adverse events following an acute coronary syndrome {(PRAISE)}: a modelling study of pooled datasets},
url = {https://www.researchgate.net/profile/James_Hughes3/publication/348501148_Machine_learning-based_prediction_of_adverse_events_following_an_acute_coronary_syndrome_PRAISE_a_modelling_study_of_pooled_datasets/links/6002a81ba6fdccdcb858b6c2/Machine-learning-based-prediction-of-adverse-events-following-an-acute-coronary-syndrome-PRAISE-a-modelling-study-of-pooled-datasets.pdf},
volume = {397},
year = {2021},
bdsk-url-1 = {https://www.researchgate.net/profile/James_Hughes3/publication/348501148_Machine_learning-based_prediction_of_adverse_events_following_an_acute_coronary_syndrome_PRAISE_a_modelling_study_of_pooled_datasets/links/6002a81ba6fdccdcb858b6c2/Machine-learning-based-prediction-of-adverse-events-following-an-acute-coronary-syndrome-PRAISE-a-modelling-study-of-pooled-datasets.pdf},
bdsk-url-2 = {https://doi.org/10.1016/S0140-6736(20)32519-8}
}