Cargando…

Computational medication regimen for Parkinson’s disease using reinforcement learning

Our objective is to derive a sequential decision-making rule on the combination of medications to minimize motor symptoms using reinforcement learning (RL). Using an observational longitudinal cohort of Parkinson’s disease patients, the Parkinson’s Progression Markers Initiative database, we derived...

Descripción completa

Detalles Bibliográficos
Autores principales:	Kim, Yejin, Suescun, Jessika, Schiess, Mya C., Jiang, Xiaoqian
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Nature Publishing Group UK 2021
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8085228/ https://www.ncbi.nlm.nih.gov/pubmed/33927277 http://dx.doi.org/10.1038/s41598-021-88619-4

Ejemplares similares

Earlier Intervention with Deep Brain Stimulation for Parkinson's Disease
por: Suarez-Cedeno, Gerson, et al.
Publicado: (2017)

A Study of the Relationship Between Uric Acid and Substantia Nigra Brain Connectivity in Patients With REM Sleep Behavior Disorder and Parkinson's Disease
por: Ellmore, Timothy M., et al.
Publicado: (2020)

Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer’s Disease Conversion
por: Pena, Danilo, et al.
Publicado: (2022)

Quantifying Neurodegenerative Progression With DeepSymNet, an End-to-End Data-Driven Approach
por: Pena, Danilo, et al.
Publicado: (2019)

Longitudinal Connectomes as a Candidate Progression Marker for Prodromal Parkinson’s Disease
por: Peña-Nogales, Óscar, et al.
Publicado: (2019)

Updated Parkinson's disease motor subtypes classification and correlation to cerebrospinal homovanillic acid and 5-hydroxyindoleacetic acid levels
por: Adams, Christopher, et al.
Publicado: (2023)

Allogeneic Bone Marrow–Derived Mesenchymal Stem Cell Safety in Idiopathic Parkinson's Disease
por: Schiess, Mya, et al.
Publicado: (2021)

IgA-Biome Profiles Correlate with Clinical Parkinson’s Disease Subtypes
por: Brown, Eric L., et al.
Publicado: (2023)

Fecal microbiota transplantation in Parkinson's disease—A randomized repeat-dose, placebo-controlled clinical pilot study
por: DuPont, Herbert L., et al.
Publicado: (2023)

Using Artificial Intelligence to Learn Optimal Regimen Plan for Alzheimer’s Disease
por: Bhattarai, Kritib, et al.
Publicado: (2023)

Using artificial intelligence to learn optimal regimen plan for Alzheimer’s disease
por: Bhattarai, Kritib, et al.
Publicado: (2023)

Reinforcement learning in dendritic structures
por: Schiess, Mathieu, et al.
Publicado: (2011)

Slowed Prosaccades and Increased Antisaccade Errors As a Potential Behavioral Biomarker of Multiple System Atrophy
por: Brooks, Sarah H., et al.
Publicado: (2017)

Gradient estimation in dendritic reinforcement learning
por: Schiess, Mathieu, et al.
Publicado: (2012)

α-Synuclein and Anti-α-Synuclein Antibodies in Parkinson’s Disease, Atypical Parkinson Syndromes, REM Sleep Behavior Disorder, and Healthy Controls
por: Smith, Lynnae M., et al.
Publicado: (2012)

CSF from Parkinson disease Patients Differentially Affects Cultured Microglia and Astrocytes
por: Schiess, Mya C, et al.
Publicado: (2010)

Improving Medication Regimen Recommendation for Parkinson’s Disease Using Sensor Technology
por: Watts, Jeremy, et al.
Publicado: (2021)

Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data
por: Li, Ziyi, et al.
Publicado: (2021)

Effects of dopamine on reinforcement learning and consolidation in Parkinson’s disease
por: Grogan, John P, et al.
Publicado: (2017)

Effects of dopamine on reinforcement learning in Parkinson’s disease depend on motor phenotype
por: van Nuland, Annelies J, et al.
Publicado: (2020)

Referring Parkinson’s disease patients for deep brain stimulation: a RAND/UCLA appropriateness study
por: Moro, Elena, et al.
Publicado: (2015)

Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine
por: Upadhyaya, Pulakesh, et al.
Publicado: (2023)

Dopamine and performance in a reinforcement learning task: evidence from Parkinson’s disease
por: Shiner, Tamara, et al.
Publicado: (2012)

Synthesize heterogeneous biological knowledge via representation learning for Alzheimer’s disease drug repurposing
por: Hsieh, Kang-Lin, et al.
Publicado: (2022)

Dynamic Navigation and Area Assignment of Multiple USVs Based on Multi-Agent Deep Reinforcement Learning
por: Wen, Jiayi, et al.
Publicado: (2022)

Reinforcing outpatient medical student learning using brief computer tutorials: the Patient-Teacher-Tutorial sequence
por: Pusic, Martin V, et al.
Publicado: (2012)

Computational Studies Applied to Flavonoids against Alzheimer's and Parkinson's Diseases
por: Monteiro, Alex France M., et al.
Publicado: (2018)

Mechanisms of α-Synuclein Induced Synaptopathy in Parkinson's Disease
por: Bridi, Jessika C., et al.
Publicado: (2018)

The Computational Development of Reinforcement Learning during Adolescence
por: Palminteri, Stefano, et al.
Publicado: (2016)

Probiotics synergized with conventional regimen in managing Parkinson’s disease
por: Sun, Hairong, et al.
Publicado: (2022)

Computational reinforcement learning, reward (and punishment), and dopamine in psychiatric disorders
por: Liebenow, Brittany, et al.
Publicado: (2022)

Reinforcement Learning With Parsimonious Computation and a Forgetting Process
por: Toyama, Asako, et al.
Publicado: (2019)

Photonic reinforcement learning based on optoelectronic reservoir computing
por: Kanno, Kazutaka, et al.
Publicado: (2022)

Computational Optimization of Image-Based Reinforcement Learning for Robotics
por: Ferraro, Stefano, et al.
Publicado: (2022)

Reinforcement Learning-Based Adaptive Streaming Scheme with Edge Computing Assistance
por: Kim, Minsu, et al.
Publicado: (2022)

Learning Process of Gaze Following: Computational Modeling Based on Reinforcement Learning
por: Ishikawa, Mitsuhiko, et al.
Publicado: (2020)

Pragmatically Framed Cross-Situational Noun Learning Using Computational Reinforcement Models
por: Najnin, Shamima, et al.
Publicado: (2018)

Computational modelling of social cognition and behaviour—a reinforcement learning primer
por: Lockwood, Patricia L, et al.
Publicado: (2020)

Editorial: Computational accounts of reinforcement learning and decision making in psychiatric disorders
por: Chase, Henry W., et al.
Publicado: (2022)

Neural computations underlying inverse reinforcement learning in the human brain
por: Collette, Sven, et al.
Publicado: (2017)

Cannot write session to /tmp/vufind_sessions/sess_bi482hluej365mts6mpauiepeu