Disability prediction in multiple sclerosis using performance outcome measures and demographic data

Subhrajit Roy, Diana Mincu, Lev Proleev, Negar Rostamzadeh, Chintan Ghate, Natalie Harris, Christina Chen, Jessica Schrouff, Nenad Tomašev, Fletcher Lee Hartsell, Katherine Heller
Proceedings of the Conference on Health, Inference, and Learning, PMLR 174:375-396, 2022.

Abstract

Literature on machine learning for multiple sclerosis has primarily focused on the use of neuroimaging data such as magnetic resonance imaging and clinical laboratory tests for disease identification. However, studies have shown that these modalities are not consistent with disease activity such as symptoms or disease progression. Furthermore, the cost of collecting data from these modalities is high, leading to scarce evaluations. In this work, we used multi-dimensional, affordable, physical and smartphone-based performance outcome measures (POM) in conjunction with demographic data to predict multiple sclerosis disease progression. We performed a rigorous benchmarking exercise on two datasets and present results across 13 clinically actionable prediction endpoints and 6 machine learning models. To the best of our knowledge, our results are the first to show that it is possible to predict disease progression using POMs and demographic data in the context of both clinical trials and smartphone-based studies by using two datasets. Moreover, we investigate our models to understand the impact of different POMs and demographics on model performance through feature ablation studies. We also show that model performance is similar across different demographic subgroups (based on age and sex). To enable this work, we developed an end-to-end reusable pre-processing and machine learning framework which allows quicker experimentation over disparate MS datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v174-roy22a, title = {Disability prediction in multiple sclerosis using performance outcome measures and demographic data}, author = {Roy, Subhrajit and Mincu, Diana and Proleev, Lev and Rostamzadeh, Negar and Ghate, Chintan and Harris, Natalie and Chen, Christina and Schrouff, Jessica and Toma\v{s}ev, Nenad and Hartsell, Fletcher Lee and Heller, Katherine}, booktitle = {Proceedings of the Conference on Health, Inference, and Learning}, pages = {375--396}, year = {2022}, editor = {Flores, Gerardo and Chen, George H and Pollard, Tom and Ho, Joyce C and Naumann, Tristan}, volume = {174}, series = {Proceedings of Machine Learning Research}, month = {07--08 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v174/roy22a/roy22a.pdf}, url = {https://proceedings.mlr.press/v174/roy22a.html}, abstract = {Literature on machine learning for multiple sclerosis has primarily focused on the use of neuroimaging data such as magnetic resonance imaging and clinical laboratory tests for disease identification. However, studies have shown that these modalities are not consistent with disease activity such as symptoms or disease progression. Furthermore, the cost of collecting data from these modalities is high, leading to scarce evaluations. In this work, we used multi-dimensional, affordable, physical and smartphone-based performance outcome measures (POM) in conjunction with demographic data to predict multiple sclerosis disease progression. We performed a rigorous benchmarking exercise on two datasets and present results across 13 clinically actionable prediction endpoints and 6 machine learning models. To the best of our knowledge, our results are the first to show that it is possible to predict disease progression using POMs and demographic data in the context of both clinical trials and smartphone-based studies by using two datasets. Moreover, we investigate our models to understand the impact of different POMs and demographics on model performance through feature ablation studies. We also show that model performance is similar across different demographic subgroups (based on age and sex). To enable this work, we developed an end-to-end reusable pre-processing and machine learning framework which allows quicker experimentation over disparate MS datasets.} }
Endnote
%0 Conference Paper %T Disability prediction in multiple sclerosis using performance outcome measures and demographic data %A Subhrajit Roy %A Diana Mincu %A Lev Proleev %A Negar Rostamzadeh %A Chintan Ghate %A Natalie Harris %A Christina Chen %A Jessica Schrouff %A Nenad Tomašev %A Fletcher Lee Hartsell %A Katherine Heller %B Proceedings of the Conference on Health, Inference, and Learning %C Proceedings of Machine Learning Research %D 2022 %E Gerardo Flores %E George H Chen %E Tom Pollard %E Joyce C Ho %E Tristan Naumann %F pmlr-v174-roy22a %I PMLR %P 375--396 %U https://proceedings.mlr.press/v174/roy22a.html %V 174 %X Literature on machine learning for multiple sclerosis has primarily focused on the use of neuroimaging data such as magnetic resonance imaging and clinical laboratory tests for disease identification. However, studies have shown that these modalities are not consistent with disease activity such as symptoms or disease progression. Furthermore, the cost of collecting data from these modalities is high, leading to scarce evaluations. In this work, we used multi-dimensional, affordable, physical and smartphone-based performance outcome measures (POM) in conjunction with demographic data to predict multiple sclerosis disease progression. We performed a rigorous benchmarking exercise on two datasets and present results across 13 clinically actionable prediction endpoints and 6 machine learning models. To the best of our knowledge, our results are the first to show that it is possible to predict disease progression using POMs and demographic data in the context of both clinical trials and smartphone-based studies by using two datasets. Moreover, we investigate our models to understand the impact of different POMs and demographics on model performance through feature ablation studies. We also show that model performance is similar across different demographic subgroups (based on age and sex). To enable this work, we developed an end-to-end reusable pre-processing and machine learning framework which allows quicker experimentation over disparate MS datasets.
APA
Roy, S., Mincu, D., Proleev, L., Rostamzadeh, N., Ghate, C., Harris, N., Chen, C., Schrouff, J., Tomašev, N., Hartsell, F.L. & Heller, K.. (2022). Disability prediction in multiple sclerosis using performance outcome measures and demographic data. Proceedings of the Conference on Health, Inference, and Learning, in Proceedings of Machine Learning Research 174:375-396 Available from https://proceedings.mlr.press/v174/roy22a.html.

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