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Læknablaðið - jún. 2019, Blaðsíða 26

Læknablaðið - jún. 2019, Blaðsíða 26
282 LÆKNAblaðið 2019/105 Y F I R L I T S G R E I N 1.­­ tr.is/tryggingastofnun/tryggingastofnun_i_tolum/rafraen­ ar-stadtolur/-­apríl­2018 2. Tryggingastofnun. Helsta orsök örorku eftir sjúkdóma­ flokkum 2015. Tryggingastofnun, Reykjavík 2015. 3. Virk starfsendurhæfingarsjóður. Ársrit um starfsendur­ hæfingu 2018, Virk starfsendurhæfingarsjóður, Reykjavík 2018. 4.­­ virk.is/is/virk/frettir/aldrei-fleiri-nyir-hja-virk­ -­ febrúar­ 2018. 5. Haraldsson SO, Brynjolfsdottir RD, Woodward JR, Siggeirsdottir K, Gudnason V. The Use of Predictive Models in Dynamic Treatment Planning. Proc ­ IEEE Symp Comput Commun 2017. 6. Siggeirsdottir K, Brynjolfsdottir RD, Haraldsson SO, Vidar S, Gudmundsson EG, Brynjolfsson JH, et al. Determinants of outcome of vocational rehabilitation. Work 2016; 55: 577­83. 7. Haraldsson SO, Brynjolfsdottir RD, Gudnason V, Tomasson K, Siggeirsdottir K. Predicting Changes in Quality of Life for Patients in Vocational Rehabilitation. Proc – IEEE Conf Evol Adapt Intell Syst (EAIS 2018) 2018. 8. Shefer G, Henderson C, Howard LM, Murray J, Thornicroft G. Diagnostic overshadowing and other challenges involved in the diagnostic process of patients with mental illness who present in emergency depart­ ments with physical symptoms ­ a qualitative study. PLoS One 2014; 9: e111682. 9. Uragaki K, Hosaka T, Arahori Y, Kushima M, Yamazaki T, Araki K, et al. Sequential Pattern Mining on Electronic Medical Records with Handling Time Intervals and the Efficacy of Medicines. Í: Proceedings ­ IEEE Symposium on Computers and Communications. IEEE 2016: 1­6. 10. Soares E, Oliveira C, Maia J, Almeida R, Coimbra M, Brandao P, et al. Modular Health Kiosk for health self­assessment. Í: Proceedings ­ IEEE Symposium on Computers and Communications. IEEE 2016: 278­80. 11. Wang Y, Kung LA, Byrd TA. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technol Forecast Soc Change 2018; 126: 3­13. 12. Rusbridge SL, Walmsley NC, Griffiths SB, Wilford PA, Rees JH. Predicting outcomes of vocational rehabilitation in patients with brain tumours. Psychooncology 2013; 22: 1907­11. 13. Rahimian F, Salimi­Khorshidi G, Payberah AH, Tran J, Ayala Solares R, Raimondi F, et al. Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records. PLOS Medicine 2018; 15: e1002695. 14. Kakadiaris IA, Vrigkas M, Yen AA, Kuznetsova T, Budoff M,­ Naghavi­ M.­ Machine­ Learning­ Outperforms­ ACC/ AHA CVD Risk Calculator in MESA. J Am Heart Ass 2018; 7: e009476. 15. Zhu M, Zhang Z, Hirdes JP, Stolee P. Using machine learning algorithms to guide rehabilitation planning for home care clients. BMC Med Inform Decis Mak 2007; 7: 41. 16. Chekroud AM, Zotti RJ, Shehzad Z, Gueorguieva R, Johnson MK, Trivedi MH, et al. Cross­trial prediction of treatment outcome in depression: A machine learning approach. Lancet Psychiatr 2016; 3: 243­50. 17. Siggeirsdottir K, Alfredsdottir U, Einarsdóttir G, Jonsson BY. A new approach in vocational rehabilitation in Iceland: preliminary report. Work 2004; 22: 3­8. 18. Geirsdottir OG, Arnarson A, Briem K, Ramel A, Tomasson K, Jonsson P V., et al. Physical function predicts improvement in quality of life in elderly icelanders after 12 weeks of resistance exercise. J Nutr Health Aging 2012; 16: 62­6. 19. Helgason T, Björnsson JK, Tómasson K, Ingimarsson S. Heilsutengd lífsgæði. Læknablaðið 1997; 83: 492­502. 20. Helgason T, Björnsson J, Tómasson K, Grétarsdóttir E, Jónsson HJ, Zoëga T, et al. Heilsutengd lífsgæði sjúklinga fyrir og eftir meðferð. Læknablaðið 2000; 86: 682­8. 21. Gudmundsson OO, Tomasson K. Quality of life and mental health of parents of children with mental health problems. Nord J Psychiatry 2002; 56: 413­7. 22. Brynjólfsson JH. Heilsutengd lífsgæði: Starfsendurhæfing og geðræn vandamál [master‘s thesis]. Háskóli Íslands, Reykjavík 2016. Heimildir Barst til blaðsins 21. janúar 2019, samþykkt til birtingar 26. apríl 2019. Kristín Siggeirsdóttir1,2 Ragnheiður D. Brynjólfsdóttir1 Sæmundur Ó. Haraldsson1,3, Ómar Hjaltason1,4, Vilmundur Guðnason1,2,5 Demand for Vocational Rehabilitation in Iceland has been stea- dily rising in recent years where the presence of young patients has increased proportionally the most. It is essential that public spending is efficient without compromising the treatment quality. It is worth exploring if a solution for increasing the efficiency in this healthcare section is to use Artificial Intelligence (AI). An innovative project on developing, testing, and implementing specialised AI software in its services is being performed in Janus Rehabilitation. The software, named Völvan in Icelandic, can identify latent areas of possible interest in patient’s circumstances which might affect the outcome of their treatment, and assist specialists in providing timely and appropriate interventions. The accuracy, precision, and recall of its predictions have been ver- ified in two recent publications. Völvan seems to be a promising tool for individualised rehabilitation, where patients are dealing with difficult and complex problems. Janus Rehabilitation is in the process of launching Völvan as an unbiased member of the interdisciplinary teams of specialists. The aim of this report is to introduce Völvan and the associated research. Novel Innovation: Can Artificial Intelligence make Rehabilitation more Efficient? ENGLISH SUMMARY 1Janus Rehabilitation, 2Icelandic Heart Association, 3Lancaster University, Bailrigg, Lancaster, LA1 4YW, UK, 4Lækning, Lágmúla 7, 105 Reykjavík, 5University of Iceland. Key words: Quality of Life, Vocational Rehabilitation, Artificial Intelligence, Prediction models. Correspondence: Kristín Siggeirsdóttir, kristin@janus.is

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