Læknablaðið - jun. 2019, Síða 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