SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Raul Teles(raulteles@usp.br)
2 Mariana de Melo(marianamelo@estudante.ufscar.br)
3 Karina Gramani-Say(gramanisay@ufscar.br)
4 Juliana Ansai(jhansai@ufscar.br)
5 Paulo Pelicioni(paulo.silvapelicioni@unsw.edu.au)
6 Jean Ponciano(jeanponciano@icmc.usp.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Alberto, S. N., Ansai, J. H., Janducci, A. L., Florido, J. V. B., Novaes, A. D. C., Caetano, M. J. D., Rossi, P. G., Tavares, L. R. C., Lord, S. R., and Gramani-Say, K. (2022). A Case Management Program at Home to Reduce Fall Risk in Older Adults (the MAGIC Study): Protocol for a Single-Blind Randomized Controlled Trial. JMIR Res Protoc, 11(6):e34796.
2 Capodici, A., Fanconi, C., Curtin, C., Shapiro, A., Noci, F., Giannoni, A., and Hernandez-Boussard, T. (2025). A scoping review of machine learning models to predict risk of falls in elders, without using sensor data. Diagnostic and Prognostic Research, 9(1):11.
3 Ebrahimi, A., Wiil, U. K., Naemi, A., Mansourvar, M., Andersen, K., and Nielsen, A. S. (2022). Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods. BMC Medical Informatics and Decision Making, 22(1):304.
4 Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine learning, 46(1):389–422.
5 Hopewell, S., Adedire, O., Copsey, B. J., Boniface, G. J., Sherrington, C., Clemson, L., Close, J. C., and Lamb, S. E. (2018). Multifactorial and multiple component interventions for preventing falls in older people living in the community. Cochrane database of systematic reviews, (7).
6 Li, Y., Hou, L., Zhao, H., Xie, R., Yi, Y., and Ding, X. (2023). Risk factors for falls among community-dwelling older adults: A systematic review and meta-analysis. Frontiers in medicine, 9:1019094.
7 Liang, H.-W., Ameri, R., Band, S., Chen, H.-S., Ho, S.-Y., Zaidan, B., Chang, K.- C., and Chang, A. (2024). Fall risk classification with posturographic parameters in community-dwelling older adults: a machine learning and explainable artificial intelligence approach. Journal of neuroengineering and rehabilitation, 21(1):15.
8 Maruf, M., Haque, M. M., Hasan, M. M., Farhan, M., and Islam, A. (2025). State-of-the-art review on fall prediction among older adults: Exploring edge devices as a promising approach for the future. Measurement: Sensors, 39:101878.
9 Remeseiro, B. and Bolon-Canedo, V. (2019). A review of feature selection methods in medical applications. Computers in biology and medicine, 112:103375.
10 Saunders, S., D’Amore, C., Hao, Q., El-Moneim, N. A., Richardson, J., Kuspinar, A., and Beauchamp, M. (2025). Risk factors for falls in community-dwelling older adults: An umbrella review. J AM MED DIR ASSOC, 26(9):105765.
11 Zhang, H., An, Y., Song, M., and Meng, Y. (2025). Dynamic fall risk prediction in hospitalized cancer patients: development and validation of a machine learning model using multidimensional clinical data to overcome over-sensitivity in traditional scales. BMC Medical Informatics and Decision Making, 25(1):377.