Subscribe to RSS
Please copy the URL and add it into your RSS Feed Reader.
https://www.thieme-connect.de/rss/thieme/en/10.1055-s-00000160.xml
neuroreha 2021; 13(01): 15-20
DOI: 10.1055/a-1352-9449
DOI: 10.1055/a-1352-9449
Schwerpunkt
Algorithmen vs. Experten in der Neuroreha
Wer macht den besseren Job?Können sich Algorithmen mit Expertinnen und Experten in der Neuroreha messen? Wie steht es um ihren Reifegrad? Sind sie den Klinikern nur in spezialisierten Teilaufgaben oder bereits bei relevanten Aufgaben überlegen? Oder ist es vielmehr so, dass sie den Fachkräften nutzen, die sie gut einzusetzen wissen?
Publication History
Article published online:
17 March 2021
© 2021. Thieme. All rights reserved.
Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany
-
Literatur
- 1 Zweig KA. Ein Algorithmus hat kein Taktgefühl: Wo künstliche Intelligenz sich irrt, warum uns das betrifft und was wir dagegen tun können. München: Heyne; 2019
- 2 Steinbach P. Künstliche Intelligenz im Gesundheitswesen. neuroreha 2021; 13: 9-14
- 3 Stinear CM, Smith MC, Byblow WD. Prediction tools for stroke rehabilitation. Stroke 2019; 50: 3314-3322
- 4 Seghier ML, Patel E, Prejawa S. et al. The PLORAS Database: A data repository for Predicting Language Outcome and Recovery After Stroke. Neuroimage 2016; 124: 1208-1212
- 5 Hope TM, Seghier ML, Leff AP. et al. Predicting outcome and recovery after stroke with lesions extracted from MRI images. Neuroimage Clin 2013; 2: 424-433
- 6 de Man-van Ginkel JM, Hafsteinsdóttir TB, Lindeman E. et al. In-hospital risk prediction for post-stroke depression: Development and validation of the post-stroke depression prediction scale. Stroke 2013; 44: 2441-2445
- 7 Han J, Lee HI, Shin YI. et al. Factors influencing return to work after stroke: The Korean Stroke Cohort for Functioning and Rehabilitation (KOSCO) Study. BMJ Open 2019; 9: e028673
- 8 Jee S, Sohn MK, Lee J. et al. Prediction for return to driving after the first-ever stroke in Korea: The KOSCO study. J Rehabil Med 2018; 50: 800-805
- 9 van der Vliet R, Selles RW, Andrinopoulou ER. et al. Predicting upper limb motor impairment recovery after stroke: A mixture model. Annals of Neurology 2020; 87: 383-393
- 10 Elsner B, Mehrholz J. „Gehen Sie zurück auf Los?!?“. neuroreha 2019; 11: 59-64
- 11 Otte K, Ellermeyer T, Vater TS. et al. Instrumental assessment of stepping in place captures clinically relevant motor symptoms of Parkinson’s disease. Sensors 2020; 20: 5465
- 12 Kwakkel G. Predicting proportional recovery of the upper limb after stroke: The PROFITS-project. In: Tagung der DGNR und DGNKN. Düsseldorf: 2020
- 13 Kwakkel G, Lannin NA, Borschmann K. et al. Standardized measurement of sensorimotor recovery in stroke trials: Consensus-based core recommendations from the Stroke Recovery and Rehabilitation Roundtable. Neurorehabil Neural Repair 2017; 31: 784-792
- 14 Kindervater R. Personalisierte neurorehabilitative Präzisionsmedizin: Von Daten zu Therapien. Forum Gesundheitsstandort BW. 2020
- 15 Universität Duisburg-Essen. RehaBoard. 2020
- 16 Raab D. Das RehaBoard: Eine integrierte Umgebung für die objektivierte, evidenzbasierte personalisierte Behandlungsplanung von Schlaganfallpatienten mit Gangstörungen. In: Tagung der DGNR und DGNKN. Düsseldorf: 2020
- 17 Wang W, Kiik M, Peek N. et al. A systematic review of machine learning models for predicting outcomes of stroke with structured data. PLOS ONE 2020; 15: e0234722
- 18 Reardon S. Rise of robot radiologists. Nature 2019; 576: S54-S58
- 19 Scrutinio D, Lanzillo B, Guida P. et al. Development and validation of a predictive model for functional outcome after stroke rehabilitation: The Maugeri Model. Stroke 2017; 48: 3308-3315
- 20 de Ridder IR, Dijkland SA, Scheele M. et al. Development and validation of the Dutch Stroke Score for predicting disability and functional outcome after ischemic stroke: A tool to support efficient discharge planning. Eur Stroke J 2018; 3: 165-173
- 21 Douiri A, Grace J, Sarker SJ. et al. Patient-specific prediction of functional recovery after stroke. Int J Stroke 2017; 12: 539-548
- 22 Nijland RH, van Wegen EE, Harmeling-van der Wel BC. et al. Presence of finger extension and shoulder abduction within 72 hours after stroke predicts functional recovery: Early prediction of functional outcome after stroke: The EPOS cohort study. Stroke 2010; 41: 745-750
- 23 Stinear CM, Byblow WD, Ackerley SJ. et al. PREP2: A biomarker-based algorithm for predicting upper limb function after stroke. Ann Clin Transl Neurol 2017; 4: 811-820
- 24 Veerbeek JM, van Wegen EE, Harmeling-van der Wel BC. et al. Is accurate prediction of gait in nonambulatory stroke patients possible within 72 hours poststroke? The EPOS study. Neurorehabil Neural Repair 2011; 25: 268-274
- 25 Kwah LK, Harvey LA, Diong J. et al. Models containing age and NIHSS predict recovery of ambulation and upper limb function six months after stroke: An observational study. J Physiother 2013; 59: 189-197
- 26 Sánchez-Blanco I, Ochoa-Sangrador C, López-Munaín L. et al. Predictive model of functional independence in stroke patients admitted to a rehabilitation programme. Clin Rehabil 1999; 13: 464-475
- 27 Kinoshita S, Abo M, Okamoto T. et al. Utility of the revised version of the ability for basic movement scale in predicting ambulation during rehabilitation in poststroke patients. J Stroke Cerebrovasc Dis 2017; 26: 1663-1669
- 28 Smith MC, Barber PA, Stinear CM. The TWIST algorithm predicts Time to Walking Independently after Stroke. Neurorehabil Neural Repair 2017; 31: 955-964
- 29 Bland MD, Sturmoski A, Whitson M. et al. Prediction of discharge walking ability from initial assessment in a stroke inpatient rehabilitation facility population. Arch Phys Med Rehabil 2012; 93: 1441-1447
- 30 Faigle R, Marsh EB, Llinas RH. et al. Novel score predicting gastrostomy tube placement in intracerebral hemorrhage. Stroke 2015; 46: 31-36
- 31 Galovic M, Stauber AJ, Leisi N. et al. Development and validation of a prognostic model of swallowing recovery and enteral tube feeding after ischemic stroke. JAMA Neurol 2019; 76: 561-570