Personalized Neurorehabilitative Precision Medicine - From Data to Therapies
NCT04688970 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 500
Last updated 2020-12-30
Summary
Stroke is the most common neurological disease leaving one third dead and one third with permanent impairment despite best medical treatment. The aim of the present study is to investigate why patients differ in how they benefit from neurorehabilitation by collecting clinical, electrophysiological, imaging and laboratory data in the acute phase of stroke as well as later on during rehabilitation and after 90 days. Following a closed-loop approach the data is analyzed by a machine learning algorithm to create a personalized neurorehabilitation strategy.
Conditions
- Stroke, Acute
- Outcome, Improvement
- Rehabilitation
- Upper Extremity Paresis
Interventions
- OTHER
-
no intervention
no intervention
Sponsors & Collaborators
-
Interfaculty Institute for Biomedical Informatics (IBMI)
collaborator UNKNOWN -
Cluster of Excellence - Machine Learning for Science
collaborator UNKNOWN -
Department for diagnostic and interventional neuroradiology, University hospital of Tuebingen
collaborator UNKNOWN -
Deparmet of biomedical magnetic resonance, University hospital of Tuebingen
collaborator UNKNOWN -
Kliniken Schmieder
collaborator UNKNOWN -
SRH-Kliniken
collaborator UNKNOWN -
University Hospital Tuebingen
lead OTHER
Principal Investigators
-
Ulf Ziemann, PhD, M. d., Prof. · Head of the department of neurology of the university hospital Tuebingen and Hertie-Institut for clinical brain research
Eligibility
- Min Age
- 18 Years
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2020-12-01
- Primary Completion
- 2022-03-31
- Completion
- 2022-06-30
Countries
- Germany
Study Locations
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