Uncertainty-Aware Prediction of Parkinson’s Disease Medication Needs: A Two-Stage Conformal Prediction Approach

Ricardo Diaz-Rincon, Muxuan Liang, Adolfo Ramirez-Zamora, Benjamin Shickel
Proceedings of the 10th Machine Learning for Healthcare Conference, PMLR 298, 2025.

Abstract

Parkinson’s Disease (PD) medication management presents unique challenges due to heterogeneous disease progression, symptoms, and treatment response. Neurologists must balance symptom control with optimal dopaminergic medication dosing based on functional disability while minimizing risks of side effects. This balance is crucial as inadequate or abrupt changes can cause levodopa-induced dyskinesia (LID), wearing off, and neuropsychiatric side effects, significantly reducing quality of life. Current approaches rely on trial-and-error decision-making without systematic predictive methods. Despite machine learning advances in medication forecasting, clinical adoption remains limited due to reliance on point predictions that do not account for prediction uncertainty, undermining clinical trust and utility. To facilitate trust, clinicians require not only predictions of future medication needs but also reliable confidence measures. Without quantified uncertainty, medication adjustments risk premature escalation to maximum doses or prolonged periods of inadequate symptom control. To address this challenge, we developed a conformal prediction framework anticipating medication needs up to two years in advance with reliable prediction intervals and statistical guarantees. Our approach addresses zero-inflation in PD inpatient data, where patients maintain stable medication regimens between visits. Using electronic health records data from 631 inpatient admissions at XYZ (2011-2021), our novel two-stage approach identifies patients likely to need medication changes, then predicts required levodopa equivalent daily dose adjustments. Our framework achieved marginal coverage while significantly reducing prediction interval lengths compared to traditional approaches, providing precise predictions for short-term planning and appropriately wider ranges for long-term forecasting, matching the increasing uncertainty in extended projections. By quantifying uncertainty in medication needs, our approach enables evidence-based decisions about levodopa dosing and medication adjustments, potentially optimizing symptom control while minimizing side effects and improving patients’ quality of life.

Cite this Paper

BibTeX
@InProceedings{pmlr-v298-diaz-rincon25a, title = {Uncertainty-Aware Prediction of Parkinson’s Disease Medication Needs: A Two-Stage Conformal Prediction Approach}, author = {Diaz-Rincon, Ricardo and Liang, Muxuan and Ramirez-Zamora, Adolfo and Shickel, Benjamin}, booktitle = {Proceedings of the 10th Machine Learning for Healthcare Conference}, year = {2025}, editor = {Agrawal, Monica and Deshpande, Kaivalya and Engelhard, Matthew and Joshi, Shalmali and Tang, Shengpu and Urteaga, Iñigo}, volume = {298}, series = {Proceedings of Machine Learning Research}, month = {15--16 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v298/main/assets/diaz-rincon25a/diaz-rincon25a.pdf}, url = {https://proceedings.mlr.press/v298/diaz-rincon25a.html}, abstract = {Parkinson’s Disease (PD) medication management presents unique challenges due to heterogeneous disease progression, symptoms, and treatment response. Neurologists must balance symptom control with optimal dopaminergic medication dosing based on functional disability while minimizing risks of side effects. This balance is crucial as inadequate or abrupt changes can cause levodopa-induced dyskinesia (LID), wearing off, and neuropsychiatric side effects, significantly reducing quality of life. Current approaches rely on trial-and-error decision-making without systematic predictive methods. Despite machine learning advances in medication forecasting, clinical adoption remains limited due to reliance on point predictions that do not account for prediction uncertainty, undermining clinical trust and utility. To facilitate trust, clinicians require not only predictions of future medication needs but also reliable confidence measures. Without quantified uncertainty, medication adjustments risk premature escalation to maximum doses or prolonged periods of inadequate symptom control. To address this challenge, we developed a conformal prediction framework anticipating medication needs up to two years in advance with reliable prediction intervals and statistical guarantees. Our approach addresses zero-inflation in PD inpatient data, where patients maintain stable medication regimens between visits. Using electronic health records data from 631 inpatient admissions at XYZ (2011-2021), our novel two-stage approach identifies patients likely to need medication changes, then predicts required levodopa equivalent daily dose adjustments. Our framework achieved marginal coverage while significantly reducing prediction interval lengths compared to traditional approaches, providing precise predictions for short-term planning and appropriately wider ranges for long-term forecasting, matching the increasing uncertainty in extended projections. By quantifying uncertainty in medication needs, our approach enables evidence-based decisions about levodopa dosing and medication adjustments, potentially optimizing symptom control while minimizing side effects and improving patients’ quality of life.} }
Endnote
%0 Conference Paper %T Uncertainty-Aware Prediction of Parkinson’s Disease Medication Needs: A Two-Stage Conformal Prediction Approach %A Ricardo Diaz-Rincon %A Muxuan Liang %A Adolfo Ramirez-Zamora %A Benjamin Shickel %B Proceedings of the 10th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2025 %E Monica Agrawal %E Kaivalya Deshpande %E Matthew Engelhard %E Shalmali Joshi %E Shengpu Tang %E Iñigo Urteaga %F pmlr-v298-diaz-rincon25a %I PMLR %U https://proceedings.mlr.press/v298/diaz-rincon25a.html %V 298 %X Parkinson’s Disease (PD) medication management presents unique challenges due to heterogeneous disease progression, symptoms, and treatment response. Neurologists must balance symptom control with optimal dopaminergic medication dosing based on functional disability while minimizing risks of side effects. This balance is crucial as inadequate or abrupt changes can cause levodopa-induced dyskinesia (LID), wearing off, and neuropsychiatric side effects, significantly reducing quality of life. Current approaches rely on trial-and-error decision-making without systematic predictive methods. Despite machine learning advances in medication forecasting, clinical adoption remains limited due to reliance on point predictions that do not account for prediction uncertainty, undermining clinical trust and utility. To facilitate trust, clinicians require not only predictions of future medication needs but also reliable confidence measures. Without quantified uncertainty, medication adjustments risk premature escalation to maximum doses or prolonged periods of inadequate symptom control. To address this challenge, we developed a conformal prediction framework anticipating medication needs up to two years in advance with reliable prediction intervals and statistical guarantees. Our approach addresses zero-inflation in PD inpatient data, where patients maintain stable medication regimens between visits. Using electronic health records data from 631 inpatient admissions at XYZ (2011-2021), our novel two-stage approach identifies patients likely to need medication changes, then predicts required levodopa equivalent daily dose adjustments. Our framework achieved marginal coverage while significantly reducing prediction interval lengths compared to traditional approaches, providing precise predictions for short-term planning and appropriately wider ranges for long-term forecasting, matching the increasing uncertainty in extended projections. By quantifying uncertainty in medication needs, our approach enables evidence-based decisions about levodopa dosing and medication adjustments, potentially optimizing symptom control while minimizing side effects and improving patients’ quality of life.
APA
Diaz-Rincon, R., Liang, M., Ramirez-Zamora, A. & Shickel, B.. (2025). Uncertainty-Aware Prediction of Parkinson’s Disease Medication Needs: A Two-Stage Conformal Prediction Approach. Proceedings of the 10th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 298 Available from https://proceedings.mlr.press/v298/diaz-rincon25a.html.

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