Találati lista | Tudóstér

001-es BibID:	BIBFORM120944
035-os BibID:	(Scopus)85122546501
Első szerző:	Brouwer, Edward De
Cím:	Corrigendum to Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression : [Computer Methods and Programs in Biomedicine, Volume 208, (September 2021) 106180] / Edward De Brouwer, Thijs Becker, Yves Moreau, Eva Kubala Havrdova, Maria Trojano, Sara Eichau, Serkan Ozakbas, Marco Onofrj, Pierre Grammond, Jens Kuhle, Ludwig Kappos, Patrizia Sola, Elisabetta Cartechini, Jeannette Lechner-Scott, Raed Alroughani, Oliver Gerlach, Tomas Kalincik, Franco Granella, Francois Grand'Maison, Roberto Bergamaschi, Maria José Sá, Bart Van Wijmeersch, Aysun Soysal, Jose Luis Sanchez-Menoyo, Claudio Solaro, Cavit Boz, Gerardo Iuliano, Katherine Buzzard, Eduardo Aguera-Morales, Murat Terzi, Tamara Castillo Trivio, Daniele Spitaleri, Vincent Van Pesch, Vahid Shaygannejad, Fraser Moore, Celia Oreja-Guevara, Davide Maimone, Riadh Gouider, Tunde Csepany, Cristina Ramo-Tello, Liesbet Peeters
Dátum:	2022
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 hozzászólás folyóiratcikk Multiple sclerosis Machine learning Longitudinal data Recurrent neural networks Electronic health records Disability progression Real-world data
Megjelenés:	Computer Methods And Programs In Biomedicine. - 213 (2022), p. 1-3. -
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 Sá, Maria José 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
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon:

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
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon:

001-es BibID:	BIBFORM132945
035-os BibID:	(scopus)85217750289 (wos)001434985500001
Első szerző:	D'hondt, Robbe
Cím:	Explainable time-to-progression predictions in multiple sclerosis / D'hondt Robbe, Dedja Klest, Aerts Sofie, Van Wijmeersch Bart, Kalincik Tomas, Reddel Stephen, Havrdova Eva Kubala, Lugaresi Alessandra, Weinstock-Guttman Bianca, Mrabet Saloua, Lalive Patrice, Kermode Allan G., Ozakbas Serkan, Patti Francesco, Prat Alexandre, Tomassini Valentina, Roos Izanne, Alroughani Raed, Gerlach Oliver, Khoury Samia J., van Pesch Vincent, Sá Maria José, Prevost Julie, Spitaleri Daniele, McCombe Pamela, Solaro Claudio, van der Walt Anneke, Butzkueven Helmut, Laureys Guy, Sánchez-Menoyo José Luis, de Gans Koen, Al-Asmi Abdullah, Deri Norma, Csepany Tunde, Al-Harbi Talal, Carroll William M., Rozsa Csilla, Singhal Bhim, Hardy Todd A., Ramanathan Sudarshini, Peeters Liesbet, Vens Celine, MSBase Study Group
Dátum:	2025
ISSN:	0169-2607
Megjegyzések:	Background: Prognostic machine learning research in multiple sclerosis has been mainly focusing on black-box models predicting whether a patients' disability will progress in a fixed number of years. However, as this is a binary yes/no question, it cannot take individual disease severity into account. Therefore, in this work we propose to model the time to disease progression instead. Additionally, we use explainable machine learning techniques to make the model outputs more interpretable. Methods: A preprocessed subset of 29,201 patients of the international data registry MSBase was used. Disability was assessed in terms of the Expanded Disability Status Scale (EDSS). We predict the time to significant and confirmed disability progression using random survival forests, a machine learning model for survival analysis. Performance is evaluated on a time-dependent area under the receiver operating characteristic and the precision-recall curves. Importantly, predictions are then explained using SHAP and Bellatrex, two explainability toolboxes, and lead to both global (population-wide) as well as local (patient visit-specific) insights. Results: On the task of predicting progression in 2 years, the random survival forest achieves state-of-the-art performance, comparable to previous work employing a random forest. However, here the random survival forest has the added advantage of being able to predict progression over a longer time horizon, with AUROC >60% for the first 10 years after baseline. Explainability techniques further validated the model by extracting clinically valid insights from the predictions made by the model. For example, a clear decline in the per-visit probability of progression is observed in more recent years since 2012, likely reflecting globally increasing use of more effective MS therapies. Conclusion: The binary classification models found in the literature can be extended to a time-to-event setting without loss of performance, thus allowing a more comprehensive prediction of patient prognosis. Furthermore, explainability techniques proved to be key to reach a better understanding of the model and increase validation of its behaviour.
Tárgyszavak:	Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Disability progression Explainable artificial intelligence Longitudinal data Multiple sclerosis Survival analysis
Megjelenés:	Computer Methods And Programs In Biomedicine. - 263 (2025), p. 1-23. -
További szerzők:	Dedja, Klest Aerts, Sofie Wijmeersch, Bart Van Kalincik, Tomas Reddel, Stephen Havrdova, Eva Lugaresi, Alessandra Weinstock-Guttman, Bianca Mrabet, Saloua Lalive, Patrice H. Kermode, Allan G. Ozakbas, Serkan Patti, Francesco Prat, Alexandre Tomassini, Valentina Roos, Izanne Alroughani, Raed Gerlach, Oliver Khoury, Samia J. Pesch, Vincent van Sá, Maria José Prevost, Julie Spitaleri, Daniele McCombe, Pamela Solaro, Claudio Walt, Anneke van der Butzkueven, Helmut Laureys, Guy (Universitary Hospital Ghent) Sanchez-Menoyo, Jose de Gans, Koen Al-Asmi, Abdullah Deri, Norma Csépány Tünde (1956-) (neurológus, pszichiáter) Al-Harbi, Talal Carroll, William M. Rózsa Csilla Singhal, Bhim Hardy, Todd A. Ramanathan, Sudarshini Peeters, Liesbet Vens, Celine MSBase Study Group
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon: