Treatment outcome prediction using multi-task learning: application to botulinum toxin in gait rehabilitation

Adil Khan (1) , Antoine Hazart (1) , Omar Galarraga , Sonia Garcia-Salicetti (2, 3, 4) , Vincent Vigneron (1)

1 IBISC - Informatique, BioInformatique, Systèmes Complexes
2 IP Paris - Institut Polytechnique de Paris
3 TSP - EPH - Département Electronique et Physique
4 ARMEDIA-SAMOVAR - ARMEDIA

We propose a framework for optimizing personalized treatment outcomes for patients with neurological diseases. A typical consequence of such diseases is gait disorders, partially explained by command and muscle tone problems associated with spasticity. Intramuscular injection of botulinum toxin type A is a common treatment for spasticity. According to the patient’s profile, offering the optimal treatment combined with the highest possible benefit-risk ratio is important. For the prediction of knee and ankle kinematics after botulinum toxin type A (BTX-A) treatment, we propose: (1) a regression strategy based on a multi-task architecture composed of LSTM models; (2) to introduce medical treatment data (MTD) for context modeling; and (3) a gating mechanism to model treatment interaction more efficiently. The proposed models were compared with and without metadata describing treatments and with serial models. Multi-task learning (MTL) achieved the lowest root-mean-squared error (RMSE) (5.60°) for traumatic brain injury (TBI) patients on knee trajectories and the lowest RMSE (3.77°) for cerebral palsy (CP) patients on ankle trajectories, with only a difference of 5.60° between actual and predicted. Overall, the best RMSE ranged from 5.24° to 6.24° for the MTL models. To the best of our knowledge, this is the first time that MTL has been used for post-treatment gait trajectory prediction. The MTL models outperformed the serial models, particularly when introducing treatment metadata. The gating mechanism is efficient in modeling treatment interaction and improving trajectory prediction.

Keywords: Botulinum toxin; Clinical gait analysis; Deep learning; Gait rehabilitation; Long short-term memory; Multi-task learning

Domains: Signal and Image processing

Adil Khan, Antoine Hazart, Omar Galarraga, Sonia Garcia-Salicetti, Vincent Vigneron. Treatment outcome prediction using multi-task learning: application to botulinum toxin in gait rehabilitation. Sensors, 2022, Machine Learning Methods for Biomedical Data Analysis, 22 (21), pp.1-19. ⟨10.3390/s22218452⟩. ⟨hal-03864673⟩