457
Views
13
CrossRef citations to date
0
Altmetric
Research Article

Longitudinal modeling to predict vital capacity in amyotrophic lateral sclerosis

, , , , , , , & show all
Pages 294-302 | Received 09 Aug 2017, Accepted 03 Dec 2017, Published online: 20 Dec 2017

References

  • Küffner R, Zach N, Norel R, Hawe J, Schoenfeld D, Wang L, et al. Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression. Nat Biotechnol. 2015;33:51–7.
  • Vucic S, Rothstein JD, Kiernan MC. Advances in treating amyotrophic lateral sclerosis: insights from pathophysiological studies. Trends Neurosci. 2014;37:433–42.
  • Louwerse ES, Visser CE, Bossuyt PM, Weverling GJ. Amyotrophic lateral sclerosis: mortality risk during the course of the disease and prognostic factors. The Netherlands ALS consortium. J Neurol Sci 1997;152(Suppl 1):S10–S7.
  • Schmidt EP, Drachman DB, Wiener CM, Clawson L, Kimball R, Lechtzin N. Pulmonary predictors of survival in amyotrophic lateral sclerosis: use in clinical trial design. Muscle Nerve. 2006;33:127–32.
  • Meininger V. Clinical trials in ALS: what did we learn from recent trials in humans? Neurodegener Dis. 2005;2:208–14.
  • O'Brien C, Guest PJ, Hill SL, Stockley RA. Physiological and radiological characterisation of patients diagnosed with chronic obstructive pulmonary disease in primary care. Thorax 2000;55:635–42.
  • Chhabra SK. Forced vital capacity, slow vital capacity, or inspiratory vital capacity: which is the best measure of vital capacity?. J Asthma. 1998;35:361–5.
  • Brusasco V, Pellegrino R, Rodarte JR. Vital capacities in acute and chronic airway obstruction: dependence on flow and volume histories. Eur Respir J. 1997;10:1316–20.
  • Pinto S, de Carvalho M. Correlation between forced vital capacity and slow vital capacity for the assessment of respiratory involvement in amyotrophic lateral sclerosis: a prospective study. Amyotroph Lateral Scler Frontotemporal Degener. 2017;18:86–91.
  • Hothorn T, Jung HH. RandomForest4Life: a random forest for predicting ALS disease progression. Amyotroph Lateral Scler Frontotemporal Degener. 2014;15:444–52.
  • Gomeni R, Fava M. The Pooled Resource Open-Access ALS Clinical Trials Consortium. Amyotrophic lateral sclerosis disease progression model. Amyotroph Lateral Scler Frontotemporal Degener. 2014;15:119–29.
  • Du W, Cheung H, Goldberg I, Thambisetty M, Becker K, Johnson CA. A longitudinal support vector regression for prediction of ALS score. IEEE Int Conf Bioinform Biomed Workshops. 2015;2015:1586–90.
  • Breiman L. Random forests. Machine Learning. 2001;45:261–77.
  • Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Statist. 2001;29:1189–232.
  • Chen T, Guestrin C. XGBoost: a scalable tree boosting system. Presented at the Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; August 13–17, 2016; San Francisco, CA, USA.
  • Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci. 2003;43:1947–58.
  • Statnikov A, Wang L, Aliferis CF. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics. 2008;9:319.
  • Díaz-Uriarte R, Alvarez de Andrés S. Gene selection and classification of microarray data using random forest. BMC Bioinformatics. 2006;7:3.
  • Ramírez J, Górriz JM, Segovia F, Chaves R, Salas-Gonzalez D, López M, et al. Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification. Neurosci Lett. 2010;472:99–103.
  • Gray KR, Aljabar P, Heckemann RA, Hammers A, Rueckert D. for the Alzheimer's Disease Neuroimaging Initiative. Random forest-based similarity measures for multi-modal classification of Alzheimer's disease. Neuroimage 2013;65:167–75.
  • Liaw A, Wiener M. Classification and regression by randomForest. R News 2002;2:18–22.
  • Lu J, Lu D, Zhang X, Bi Y, Cheng K, Zheng M, et al. Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine. Biochim Biophys Acta. 2016;1860:2664–71.
  • Atkinson EJ, Therneau TM, Melton LJ, 3rd, Camp JJ, Achenbach SJ, Amin S, et al. Assessing fracture risk using gradient boosting machine (GBM) models. J Bone Miner Res. 2012;27:1397–404.
  • Hastie T, Tibshirani R, Friedman JD. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York, NY: Springer; 2009.
  • Wickham H. ggplot2: elegant graphics for data analysis. New York, NY: Springer; 2009.
  • Revelle W. psych: procedures for personality and psychological research. Version = 1.6.9. 2017. Available at: https://CRAN.R-project.org/package=psych
  • Wickham H. The split-apply-combine strategy for data analysis. J Stat Soft. 2011;40:1–29.
  • Mangera Z, Panesar G, Makker H. Practical approach to management of respiratory complications in neurological disorders. Int J Gen Med. 2012;5:255–63.
  • American Academy of Neurology. The care of the patient with amyotrophic lateral sclerosis: drug, nutritional, and respiratory therapies. 2017 [last accessed November 21, 2017]. Available at: https://www.aan.com/Guidelines/Home/GetGuidelineContent/373

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.