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001-es BibID:	BIBFORM125679
035-os BibID:	(Scopus)85201493485
Első szerző:	De Brouwer, Edward
Cím:	Machine-learning-based prediction of disability progression in multiple sclerosis : an observational, international, multi-center study / Edward De Brouwer, Thijs Becker, Lorin Werthen-Brabants, Tunde Csepany, Norbert Vella, Yves Moreau, Liesbet Peeters
Dátum:	2024
ISSN:	2767-3170
Megjegyzések:	Background Disability progression is a key milestone in the disease evolution of people with multiple sclerosis (PwMS). Prediction models of the probability of disability progression have not yet reached the level of trust needed to be adopted in the clinic. A common benchmark to assess model development in multiple sclerosis is also currently lacking. Methods Data of adult PwMS with a follow-up of at least three years from 146 MS centers, spread over 40 countries and collected by the MSBase consortium was used. With basic inclusion criteria for quality requirements, it represents a total of 15, 240 PwMS. External validation was performed and repeated five times to assess the significance of the results. Transparent Reporting for Individual Prognosis Or Diagnosis (TRIPOD) guidelines were followed. Confirmed disability progression after two years was predicted, with a confirmation window of six months. Only routinely collected variables were used such as the expanded disability status scale, treatment, relapse information, and MS course. To learn the probability of disability progression, state-of-the-art machine learning models were investigated. The discrimination performance of the models is evaluated with the area under the receiver operator curve (ROC-AUC) and under the precision recall curve (AUC-PR), and their calibration via the Brier score and the expected calibration error. All our preprocessing and model code are available at https://gitlab.com/edebrouwer/ms_benchmark, making this task an ideal benchmark for predicting disability progression in MS. Findings Machine learning models achieved a ROC-AUC of 0.71 ? 0.01, an AUC-PR of 0.26 ? 0.02, a Brier score of 0.1 ? 0.01 and an expected calibration error of 0.07 ? 0.04. The history of disability progression was identified as being more predictive for future disability progression than the treatment or relapses history. Conclusions Good discrimination and calibration performance on an external validation set is achieved, using only routinely collected variables. This suggests machine-learning models can reliably inform clinicians about the future occurrence of progression and are mature for a clinical impact study. Copyright: ? 2024 De Brouwer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Tárgyszavak:	Orvostudományok Elméleti orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk machine learning
Megjelenés:	PLOS Digital Health. - 3 : 7 (2024), p. 1-25. -
További szerzők:	Becker, Thijs Werthen-Brabants, Lorin Csépány Tünde (1956-) (neurológus, pszichiáter) Vella Norbert Moreau, Yves Peeters, Liesbet
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM126277
035-os BibID:	(scopus)85201493485
Első szerző:	De Brouwer, Edward
Cím:	Machine-learning-based prediction of disability progression in multiple sclerosis : an observational, international, multi-center study / De Brouwer E., Becker T., Werthen-Brabants L., Dewulf P., Iliadis D., Dekeyser C., Laureys G., Van Wijmeersch B., Popescu V., Dhaene T., Deschrijver D., Waegeman W., De Baets B., Stock M., Horakova D., Patti F., Izquierdo G., Eichau S., Girard M., Prat A., Lugaresi A., Grammond P., Kalincik T., Alroughani R., Grand'Maison F., Skibina O., Terzi M., Lechner-Scott J., Gerlach O., Khoury S. J., Cartechini E., Van Pesch V., Sa M. J., Weinstock-Guttman B., Blanco Y., Ampapa R., Spitaleri D., Solaro C., Maimone D., Soysal A., Iuliano G., Gouider R., Castillo-Trivino T., Sánchez-Menoyo J. L., Laureys G., van der Walt A., Oh J., Aguera-Morales E., Altintas A., Al-Asmi A., de Gans K., Fragoso Y., Csepany T., Hodgkinson S., Deri N., Al-Harbi T., Taylor B., Gray O., Lalive P., Rozsa C., McGuigan C., Kermode A., Sempere A. P., Mihaela S., Simo M., Hardy T., Decoo D., Hughes S., Grigoriadis N., Sas A., Vella N., Moreau Y., Peeters L.
Dátum:	2024
ISSN:	2767-3170
Megjegyzések:	Background Disability progression is a key milestone in the disease evolution of people with multiple sclerosis (PwMS). Prediction models of the probability of disability progression have not yet reached the level of trust needed to be adopted in the clinic. A common benchmark to assess model development in multiple sclerosis is also currently lacking. Methods Data of adult PwMS with a follow-up of at least three years from 146 MS centers, spread over 40 countries and collected by the MSBase consortium was used. With basic inclusion criteria for quality requirements, it represents a total of 15, 240 PwMS. External validation was performed and repeated five times to assess the significance of the results. Transparent Reporting for Individual Prognosis Or Diagnosis (TRIPOD) guidelines were followed. Confirmed disability progression after two years was predicted, with a confirmation window of six months. Only routinely collected variables were used such as the expanded disability status scale, treatment, relapse information, and MS course. To learn the probability of disability progression, state-of-the-art machine learning models were investigated. The discrimination performance of the models is evaluated with the area under the receiver operator curve (ROC-AUC) and under the precision recall curve (AUC-PR), and their calibration via the Brier score and the expected calibration error. All our preprocessing and model code are available at https://gitlab.com/edebrouwer/ms_benchmark, making this task an ideal benchmark for predicting disability progression in MS. Findings Machine learning models achieved a ROC-AUC of 0?71 ? 0?01, an AUC-PR of 0?26 ? 0?02, a Brier score of 0?1 ? 0?01 and an expected calibration error of 0?07 ? 0?04. The history of disability progression was identified as being more predictive for future disability progression than the treatment or relapses history. Conclusions Good discrimination and calibration performance on an external validation set is achieved, using only routinely collected variables. This suggests machine-learning models can reliably inform clinicians about the future occurrence of progression and are mature for a clinical impact study.
Tárgyszavak:	Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Machine learning disability progression multiple sclerosis
Megjelenés:	PLOS Digital Health. - 3 : 7 (2024), p. 1-25. -
További szerzők:	Becker, Thijs Werthen-Brabants, Lorin Dewulf, Pieter Iliadis, Dimitrios Dekeyser, Cathérine Laureys, Guy Wijmeersch, Bart Van Popescu, Veronica Dhaene, Tom Deschrijver, Dirk Waegeman, Willem De Baets, Bernard Stock, Michael J. Horakova, Dana Patti, Francesco Izquierdo, Guillermo Eichau, Sara Girard, Marc Prat, Alexandre Lugaresi, Alessandra Grammond, Pierre Kalincik, Tomas Alroughani, Raed Grand'Maison, Francois Skibina, Olga Terzi, Murat Lechner-Scott, Jeannette Gerlach, Oliver Khoury, Samia J. Cartechini, Elisabetta Pesch, Vincent van Sá, Maria José Weinstock-Guttman, Bianca Blanco, Yolanda Ampapa, Radek Spitaleri, Daniele Solaro, Claudio Maimone, Davide Soysal, Aysun Iuliano, Gerardo Gouider, Riadh Castillo Triviño, Tamara Sanchez-Menoyo, Jose Laureys, Guy (Universitary Hospital Ghent) Walt, Anneke van der Oh, Jiwon Aguera-Morales, Eduardo Altintas, Ayse Al-Asmi, Abdullah de Gans, Koen Fragoso, Yara Csépány Tünde (1956-) (neurológus, pszichiáter) Hodgkinson, Suzanne Deri, Norma Al-Harbi, Talal Taylor, Bruce V. Gray, Orla Lalive, Patrice H. Rózsa Csilla McGuigan, Christopher Kermode, Allan G. Sempere, Perez A. Mihaela, Simu Simó Magdolna Hardy, Todd A. Decoo, Danny Hughes, Stella Grigoriadis, Nikolaos Sas Attila Vella Norbert Moreau, Yves Peeters, Liesbet
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001-es BibID:	BIBFORM103017
035-os BibID:	(Wos)000685503300008 (Scopus)85107912293
Első szerző:	De Brouwer, Edward
Cím:	Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression / De Brouwer Edward, Becker Thijs, Moreau Yves, Havrdova Eva Kubala, Trojano Maria, Eichau Sara, Ozakbas Serkan, Onofrj Marco, Grammond Pierre, Kuhle Jens, Kappos Ludwig, Sola Patrizia, Cartechini Elisabetta, Lechner-Scott Jeannette, Alroughani Raed, Gerlach Oliver, Kalincik Tomas, Granella Franco, Grand'Maison Francois, Bergamaschi Roberto, José Sá Maria, Van Wijmeersch Bart, Soysal Aysun, Sanchez-Menoyo Jose Luis, Solaro Claudio, Boz Cavit, Iuliano Gerardo, Buzzard Katherine, Aguera-Morales Eduardo, Terzi Murat, Trivio Tamara Castillo, Spitaleri Daniele, Van Pesch Vincent, Shaygannejad Vahid, Moore Fraser, Oreja-Guevara Celia, Maimone Davide, Gouider Riadh, Csepany Tunde, Ramo-Tello Cristina, Peeters Liesbet
Dátum:	2021
ISSN:	0169-2607
Megjegyzések:	Background and Objectives: Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work we aim to predict disability progression by optimally extracting information from longitudinal patient data in the real-world setting, with a special focus on the sporadic sampling problem. Methods: We use machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization. A subset of 6682 patients from the MSBase registry is used. Results: We can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.85, which represents a 32% decrease in the ranking pair error (1-AUC) compared to reference methods using static clinical features. Conclusions: Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction and represents a step forward towards AI-assisted precision medicine in MS.
Tárgyszavak:	Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Computer Methods And Programs In Biomedicine. - 208 (2021), p. 1-14. -
További szerzők:	Becker, Thijs Moreau, Yves Havrdova, Eva Trojano, Maria Eichau, Sara Ozakbas, Serkan Onofrj, Marco Grammond, Pierre Kuhle, Jens Kappos, Ludwig Sola, Patrizia Cartechini, Elisabetta Lechner-Scott, Jeannette Alroughani, Raed Gerlach, Oliver Kalincik, Tomas Granella, Franco Grand'Maison, Francois Bergamaschi, Roberto José Sá, Maria Wijmeersch, Bart Van Soysal, Aysun Sanchez-Menoyo, Jose Solaro, Claudio Boz, Cavit Iuliano, Gerardo Buzzard, Katherine Aguera-Morales, Eduardo Terzi, Murat Trivio, Tamara Castillo Spitaleri, Daniele Pesch, Vincent van Shaygannejad, Vahid Moore, Fraser Oreja-Guevara, Celia Maimone, Davide Gouider, Riadh Csépány Tünde (1956-) (neurológus, pszichiáter) Ramo-Tello, Cristina Peeters, Liesbet
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001-es BibID:	BIBFORM132947
035-os BibID:	(scopus)105011413182 (wos)001536298500003
Első szerző:	Pirmani, Ashkan
Cím:	Personalized federated learning for predicting disability progression in multiple sclerosis using real-world routine clinical data / Pirmani Ashkan, De Brouwer Edward, Arany Ádám, Oldenhof Martijn, Passemiers Antoine, Faes Axel, Kalincik Tomas, Ozakbas Serkan, Gouider Riadh, Willekens Barbara, Horakova Dana, Havrdova Eva Kubala, Patti Francesco, Prat Alexandre, Lugaresi Alessandra, Tomassini Valentina, Grammond Pierre, Cartechini Elisabetta, Roos Izanne, Boz Cavit, Alroughani Raed, Amato Maria Pia, Buzzard Katherine, Lechner-Scott Jeannette, Guimaraes Joana, Solaro Claudio, Gerlach Oliver, Soysal Aysun, Kuhle Jens, Sanchez-Menoyo Jose Luis, Spitaleri Daniele, Csepany Tunde, Van Wijmeersch Bart, Ampapa Radek, Prevost Julie, Khoury Samia J., Van Pesch Vincent, John Nevin, Maimone Davide, Weinstock-Guttman Bianca, Laureys Guy, McCombe Pamela, Blanco Yolanda, Altintas Ayse, Al-Asmi Abdullah, Garber Justin, Van der Walt Anneke, Butzkueven Helmut, de Gans Koen, Rozsa Csilla, Taylor Bruce, Al-Harbi Talal, Sas Attila, Rajda Cecilia, Gray Orla, Decoo Danny, Carroll William M., Kermode Allan G., Fabis-Pedrini Marzena, Mason Deborah, Perez-Sempere Angel, Simu Mihaela, Shuey Neil, Singhal Bhim, Cauchi Marija, Hardy Todd A., Ramanathan Sudarshini, Lalive Patrice, Sirbu Carmen-Adella, Hughes Stella, Castillo Trivino Tamara, Peeters Liesbet M., Moreau Yves
Dátum:	2025
ISSN:	2398-6352
Megjegyzések:	Early prediction of disability progression in multiple sclerosis (MS) remains challenging despite its critical importance for therapeutic decision-making. We present the first systematic evaluation of personalized federated learning (PFL) for 2-year MS disability progression prediction, leveraging multi-center real-world data from over 26,000 patients. While conventional federated learning (FL) enables privacy-aware collaborative modeling, it remains vulnerable to institutional data heterogeneity. PFL overcomes this challenge by adapting shared models to local data distributions without compromising privacy. We evaluated two personalization strategies: a novel AdaptiveDualBranchNet architecture with selective parameter sharing, and personalized fine-tuning of global models, benchmarked against centralized and client-specific approaches. Baseline FL underperformed relative to personalized methods, whereas personalization significantly improved performance, with personalized FedProx and FedAVG achieving ROC-AUC scores of 0.8398 ? 0.0019 and 0.8384 ? 0.0014, respectively. These findings establish personalization as critical for scalable, privacy-aware clinical prediction models and highlight its potential to inform earlier intervention strategies in MS and beyond.
Tárgyszavak:	Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	npj Digital Medicine. - 8 : 1 (2025), p. 1-15. -
További szerzők:	De Brouwer, Edward Arany Ádám Oldenhof, Martijn Passemiers, Antoine Faes, Axel Kalincik, Tomas Ozakbas, Serkan Gouider, Riadh Willekens, Barbara Horakova, Dana Havrdova, Eva Patti, Francesco Prat, Alexandre Lugaresi, Alessandra Tomassini, Valentina Grammond, Pierre Cartechini, Elisabetta Roos, Izanne Boz, Cavit Alroughani, Raed Amato, Maria Pia Buzzard, Katherine Lechner-Scott, Jeannette Guimaraes, Joana Solaro, Claudio Gerlach, Oliver Soysal, Aysun Kuhle, Jens Sanchez-Menoyo, Jose Spitaleri, Daniele Csépány Tünde (1956-) (neurológus, pszichiáter) Wijmeersch, Bart Van Ampapa, Radek Prevost, Julie Khoury, Samia J. Pesch, Vincent van John, Nevin Maimone, Davide Weinstock-Guttman, Bianca Laureys, Guy (Universitary Hospital Ghent) McCombe, Pamela Blanco, Yolanda Altintas, Ayse Al-Asmi, Abdullah Garber, Justin Walt, Anneke van der Butzkueven, Helmut de Gans, Koen Rózsa Csilla Taylor, Bruce V. Al-Harbi, Talal Sas Attila Rajda Cecília Gray, Orla Decoo, Danny Carroll, William M. Kermode, Allan G. Fabis-Pedrini, Marzena Mason, Deborah Perez-Sempere, Angel Simu, Mihaela Shuey, Neil Singhal, Bhim Cauchi, Marija Hardy, Todd A. Ramanathan, Sudarshini Lalive, Patrice H. Sirbu, Carmen-Adella Hughes, Stella Castillo Triviño, Tamara Peeters, Liesbet Moreau, Yves
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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