Artificial Neural Network and Falls in Community-Dwellers: A New Approach to Identify the Risk of Recurrent Falling?

Journal of the American Medical Directors Association

Volumn 16, Issue 4, 2015, Pages 277-281

  Kabeshova, Anastasiia ^a,b   Launay, Cyrille P ^a   Gromov, Vasilii A ^b   Annweiler, Cédric ^a,c   Fantino, Bruno ^a   Beauchet, Olivier ^a  

^a ANGERS UNIVERSITY HOSPITAL   (France)

^b OLES HONCHAR DNIPRO NATIONAL UNIVERSITY   (Ukraine)

^c ROBARTS RESEARCH INSTITUTE   (Canada)

Author keywords

Artificial neural network; Elderly; Recurrent fall

Indexed keywords

AGE; AGED; ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; CROSS-SECTIONAL STUDY; FALLING; FEMALE; GERIATRIC ASSESSMENT; HUMAN; INCIDENCE; INDEPENDENT LIVING; MALE; PREVENTION AND CONTROL; PROCEDURES; RECURRENT DISEASE; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY; SEX DIFFERENCE; STATISTICS AND NUMERICAL DATA; VERY ELDERLY;

ACCIDENTAL FALLS; AGE FACTORS; AGED; AGED, 80 AND OVER; CROSS-SECTIONAL STUDIES; FEMALE; GERIATRIC ASSESSMENT; HUMANS; INCIDENCE; INDEPENDENT LIVING; MALE; NEURAL NETWORKS (COMPUTER); RECURRENCE; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY; SEX FACTORS;

EID: 84929952347     PISSN: 15258610     EISSN: 15389375     Source Type: Journal    
DOI: 10.1016/j.jamda.2014.09.013     Document Type: Article

References (36)

1. Beauchet O., Dubost V., Revel Delhom C., et al. How to manage recurrent falls in clinical practice: Guidelines of the French Society of Geriatrics and Gerontology. JNutr Health Aging 2011, 15:79-84. French Society of Geriatrics and Gerontology.
2. de Decker L., Annweiler C., Yvain F., et al. How to manage gait and balance disorders among older adults aged 65 years and older with mild to moderate dementia in clinical practice?. Geriatr Psychol Neuropsychiatr Vieil 2011, 9:269-276.
3. Rossat A., Fantino B., Nitenberg C., et al. Risk factors for falling in community-dwelling older adults: Which of them are associated with the recurrence of falls?. JNutr Health Aging 2010, 14:787-791.
4. Kelsey J.L., Procter-Gray E., Berry S.D., et al. Reevaluating the implications of recurrent falls in older adults: Location changes the inference. JAm Geriatr Soc 2012, 60:517-524.
5. Oliver D., Britton M., Seed P., et al. Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: Case-control and cohort studies. BMJ 1997, 315:1049-1053.
6. Oliver D., Daly F., Martin F.C., McMurdo M.E. Risk factors and risk assessment tools for falls in hospital in-patients: A systematic review. Age Ageing 2004, 33:122-130.
7. Bongue B., Dupré C., Beauchet O., et al. Ascreening tool with five risk factors was developed for fall-risk prediction in community-dwelling elderly. JClin Epidemiol 2011, 64:1152-1160.
8. Rossat A., Fantino B., Bongue B., et al. Association between benzodiazepines and recurrent falls: A cross-sectional elderly population-based study. JNutr Health Aging 2011, 15:72-77.
9. Akl A., Ghoneim M. Forecasting the clinical outcome: Artificial neural networks or multivariate statistical models?. Artificial Neural Networks-Methodological Advances and Biomedical Applications 2011, InTech, 45-68; 303-342; 355-366. K. Suzuki (Ed.).
10. Baxt W.G. Application of artificial neural networks to clinical medicine. Lancet 1995, 346:1135-1138.
11. Baxt W.G., Skora J. Prospective validation of artificial neural network trained to identify acute myocardial infarction. Lancet 1996, 347:12-15.
12. Kuhn M., Johnson K. Applied Predictive Modeling 2013, Springer, London.
13. Patel J.L., Goyal R.K. Applications of artificial neural networks in medical science. Curr Clin Pharmacol 2007, 10:217-226.
14. Lei S. Dalian, China. Artificial neural networks: Current applications in modern medicine 2010, 2:383-387. Inf. Sci. & Technol. Coll., Dalian Maritime Univ.
15. Lisboa P.J. Areview of evidence of health benefit from artificial neural networks in medical intervention. Neural Netw 2002, 15:11-39.
16. Bath P., Pendleton N., Morgan K. New approach to risk determination: Development of risk profile for new falls among community-dwelling older people by use of a Genetic Algorithm Neural Network (GANN). JGerontol A Biol Sci Med Sci 2000, 55:M17-M21.
17. Giansanti D., Maccioni G., Cesinaro S., et al. Assessment of fall-risk by means of a neural network based on parameters assessed by a wearable device during posturography. Med Eng Phys 2008, 30:367-372.
18. Giansanti D., Macellari V., Maccioni G. New neural network classifier of fall-risk based on the Mahalanobis distance and kinematic parameters assessed by a wearable device. Physiol Meas 2008, 29:N11-N19.
19. Özesmi SL, Tan CO, Özesmi U. Methodological issues in building, training, and testing artificial neural networks in ecological applications. Ecol. Model., Selected Papers from the Third Conference of the International Society for Ecological Informatics (ISEI), Rome, Italy 2006;195:83-93.
20. Fritsch S, Guenther F. Neuralnet: Training of Neural Networks (2012). Available at: . Accessed October 23, 2014. http://cran.r-project.org/web/packages/neuralnet/neuralnet.pdf.
21. Yáñez A., Barro S., Bugarin A. Backpropagation Multilayer Perceptron: A Modular Implementation. Artificial Neural Networks, Lecture Notes in Computer Science 1991, 285-295. Springer, Berlin.
22. Stanley K.O., Miikkulainen R. Evolving neural networks through augmenting topologies. Evol Comput 2002, 10:99-127.
23. Dedecker A.P., Goethals P.L., Gabriels W., De Pauw N. Optimization of Artificial Neural Network (ANN) model design for prediction of macroinvertebrates in the Zwalm river basin (Flanders, Belgium). Ecol Model 2004, 174:161-173.
24. Rossat A., Beauchet O., Nitenberg C., et al. Risk factors for fear of falling: Across-sectional population-based study. JAm Geriatr Soc 2009, 57:1304-1306.
25. Lord S.R., Ward J.A., Williams P. Physiological factors associated with falls in older community-dwelling women. JAm Geriatr Soc 1994, 42:1110-1117.
26. Podsiadlo D., Richardson S. "The Timed Up & Go": A test of basic functional mobility for frail elderly persons. JAm Geriatr Soc 1991, 39:142-149.
27. Shah A., Herbert R., Lewis S., et al. Screening for depression among acutely ill geriatric inpatients with a short Geriatric Depression Scale. Age Ageing 1997, 26:217-221.
28. Cahn D.A., Salmon D.P., Monsch A.U., et al. Screening for dementia of the Alzheimer type in the community: The utility of the Clock Drawing Test. Arch Clin Neuropsychol 1996, 11:529-539.
29. Howcroft J., Kofman J., Lemaire E.D. Review of fall risk assessment in geriatric populations using inertial sensors. JNeuroeng Rehabil 2013, 10:91.
30. Lord S.R., Sherrington C., Cameron I.D., Close J.C. Implementing falls prevention research into policy and practice in Australia: Past, present and future. JSafety Res 2011, 42:517-520.
31. Jones T.S., Ghosh T.S., Horn K., et al. Primary care physician perceptions and practices regarding fall prevention in adults 65 years and over. Accid Anal Prev 2011, 43:1605-1609.
32. Summary of the Updated American Geriatrics Society/British Geriatrics Society clinical practice guideline for prevention of falls in older persons. JAm Geriatr Soc 2011, 59:148-157. Panel on Prevention of Falls in Older Persons, American Geriatrics Society and British Geriatrics Society.
33. Beauchet O., Rossat A., Bongue B., et al. Change in arm position during one-leg balance test: A predictor of recurrent falls in community-dwelling older adults. JAm Geriatr Soc 2010, 58:1598-1600.
34. Scheffer A.C., Schuurmans M.J., van Dijk N., et al. Fear of falling: Measurement strategy, prevalence, risk factors and consequences among older persons. Age Ageing 2008, 37:19-24.
35. Cummings S.R., Nevitt M.C., Kidd S. Forgetting falls. The limited accuracy of recall of falls in the elderly. JAm Geriatr Soc 1988, 36:613-616.
36. Lawrence S., Giles C.L., Tsoi A.C. Lessons in neural network training: Overfitting may be harder than expected. AAAI/IAAI 1997, 540-545. Citeseer, Menlo Park, CA.