Machine Learning

Classification of Alzheimer’s Disease Stages using Radiology Imaging and Longitudinal Clinical Data – References


References for the project

C. Aditya and M. S. Pande. Devising an interpretable calibrated scale to quantitatively assess the dementia stage of subjects with alzheimer’s disease: A machine learning approach. Informatics in Medicine Unlocked, 6:28–35, jan 2017. ISSN 2352-9148. doi: 10.1016/J.IMU.2016.12.004.
V. Alves, R. Braga, E. Muratov, C. Andrade, V. M. Alves, R. C. Braga, E. Muratov, and C. H. Andrade. Development of Web and Mobile Applications for Chemical Toxicity Prediction. Journal of the Brazilian Chemical Society, 29(5):982–988, 2018. ISSN 01035053. doi: 10.21577/0103-5053.20180013.
Alzheimer’s Association. 2016 Alzheimer’s disease facts and figures. Alzheimer’s & Dementia, 12(4):459–509, apr 2016. doi: 10.1016/j.jalz.2016.03.001.
R. Azvan, V. Marinescu, N. P. Oxtoby, A. L. Young, E. E. Bron, A. W. Toga, M. W. Weiner, F. Barkhof, N. C. Fox, S. Klein, and D. C. Alexander. TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer’s Disease. Technical report, 2018.
R. J. Bateman, C. Xiong, T. L. Benzinger, A. M. Fagan, A. Goate, N. C. Fox, D. S. Marcus, N. J. Cairns, X. Xie, T. M. Blazey, D. M. Holtzman, A. Santacruz, V. Buckles, A. Oliver, K. Moulder, P. S. Aisen, B. Ghetti, W. E. Klunk, E. McDade, R. N. Martins, C. L. Masters, R. Mayeux, J. M. Ringman, M. N. Rossor, P. R. Schofield, R. A. Sperling, S. Salloway, and J. C. Morris. Clinical and Biomarker Changes in Dominantly Inherited Alzheimer’s Disease. New England Journal of Medicine, 367(9):795–804, aug 2012. ISSN 0028-4793. doi: 10.1056/NEJMoa1202753.
M. Bilgel and B. M. Jedynak. Predicting time to dementia using a quantitative template of disease progression. 2018. doi: 10.1101/458273.
CADDementia. CADDementia – Evaluation, 2014. URL https://caddementia.
E. Colantuoni, G. Surplus, A. Hackman, H. M. Arrighi, and R. Brookmeyer. Web-based application to project the burden of Alzheimer’s disease. Alzheimer’s & Dementia, 6 (5):425–428, sep 2010. ISSN 1552-5260. doi: 10.1016/J.JALZ.2010.01.014.
R. Cui and M. Liu. RNN-based longitudinal analysis for diagnosis of Alzheimer’s disease. Computerized Medical Imaging and Graphics, 73:1–10, apr 2019. ISSN 0895-6111. doi: 10.1016/J.COMPMEDIMAG.2019.01.005.
R. S. Doody, V. Pavlik, P. Massman, S. Rountree, E. Darby, and W. Chan. Predicting progression of Alzheimer’s disease. Alzheimer’s Research & Therapy, 2(1):2, 2010. ISSN 1758-9193. doi: 10.1186/alzrt25.
T. Fawcett. doi:10.1016/j.patrec.2005.10.010. 2005. doi: 10.1016/j.patrec.2005.10.010.
J.-B. Fiot, H. Raguet, L. Risser, L. D. Cohen, J. Fripp, and F.-X. Vialard. Longitudinal deformation models, spatial regularizations and learning strategies to quantify Alzheimer’s disease progression. NeuroImage: Clinical, 4:718–729, jan 2014. ISSN 2213-1582. doi: 10.1016/J.NICL.2014.02.002.
C. K. Fisher, A. M. Smith, and J. R. Walsh. Using deep learning for comprehensive, personalized forecasting of Alzheimer’s Disease progression. Technical report, 2018.
D. Goyal, D. Tjandra, R. Q. Migrino, B. Giordani, Z. Syed, and J. Wiens. Characterizing heterogeneity in the progression of Alzheimer’s disease using longitudinal clinical and neuroimaging biomarkers. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 10:629–637, jan 2018. ISSN 2352-8729. doi: 10.1016/J.DADM.2018.06.007.
C. Green, J. Shearer, C. W. Ritchie, and J. P. Zajicek. Model-Based Economic Evaluation in Alzheimer’s Disease: A Review of the Methods Available to Model Alzheimer’s Disease Progression. Value in Health, 14(5):621–630, jul 2011. ISSN 1098-3015. doi: 10.1016/J.JVAL.2010.12.008.
C. K. Fisher, A. M. Smith, J. R. Walsh, and the Coalition Against Major Diseases. Using deep learning for comprehensive, personalized forecasting of Alzheimer’s Disease progression. jul 2018.
K. Kruthika, Rajeswari, and H. Maheshappa. CBIR system using Capsule Networks and 3D CNN for Alzheimer’s disease diagnosis. Informatics in Medicine Unlocked, 14:59–68, jan 2019a. ISSN 2352-9148. doi: 10.1016/J.IMU.2018.12.001.
K. Kruthika, Rajeswari, and H. Maheshappa. Multistage classifier-based approach for Alzheimer’s disease prediction and retrieval. Informatics in Medicine Unlocked, 14: 34–42, jan 2019b. ISSN 2352-9148. doi: 10.1016/J.IMU.2018.12.003.
S. Lahmiri and A. Shmuel. Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer’s disease. Biomedical Signal Processing and Control, nov 2018. ISSN 1746-8094. doi: 10.1016/J.BSPC.2018.08.009.
S. H. Lee, A. H. Bachman, D. Yu, J. Lim, and B. A. Ardekani. Predicting progression from mild cognitive impairment to Alzheimer’s disease using longitudinal callosal atrophy. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 2:68–74, jan 2016. ISSN 2352-8729. doi: 10.1016/J.DADM.2016.01.003.
Lemaitre G., Nogueira F., Oliveira D., and Aridas C. imblearn.over sampling.SMOTE imbalanced-learn 0.5.0 documentation, 2017.
K. Li, W. Chan, R. S. Doody, J. Quinn, S. Luo, and t. A. D. N. Alzheimer’s Disease Neuroimaging Initiative. Prediction of Conversion to Alzheimer’s Disease with Longitudinal Measures and Time-To-Event Data. Journal of Alzheimer’s disease : JAD, 58(2):361–371, 2017. ISSN 1875-8908. doi: 10.3233/JAD-161201.
S. M. Lundberg and S.-I. Lee. A Unified Approach to Interpreting Model Predictions, 2017.
J. C. Masdeu, J. L. Zubieta, and J. Arbizu. Neuroimaging as a marker of the onset and progression of Alzheimer’s disease. Journal of the Neurological Sciences, 236(1-2):55–64, sep 2005. ISSN 0022-510X. doi: 10.1016/J.JNS.2005.05.001.
M. Mehdipour Ghazi, M. Nielsen, A. Pai, M. J. Cardoso, M. Modat, S. Ourselin, and L. Sørensen. Training recurrent neural networks robust to incomplete data: Application to Alzheimer’s disease progression modeling. Medical Image Analysis, 53:39–46, apr 2019. ISSN 1361-8415. doi: 10.1016/J.MEDIA.2019.01.004.
M. Memedi, J. Westin, D. Nyholm, M. Dougherty, and T. Groth. A web application for follow-up of results from a mobile device test battery for Parkinson’s disease patients. Computer Methods and Programs in Biomedicine, 104(2):219–226, nov 2011. ISSN 01692607. doi: 10.1016/j.cmpb.2011.07.017.
A. J. Mishizen-Eberz, R. A. Rissman, T. L. Carter, M. D. Ikonomovic, B. B. Wolfe, and D. M. Armstrong. Biochemical and molecular studies of NMDA receptor subunits NR1/2A/2B in hippocampal subregions throughout progression of Alzheimer’s disease pathology. Neurobiology of Disease, 15(1):80–92, feb 2004. ISSN 0969-9961. doi: 10.1016/J.NBD.2003.09.016.
A. Moscoso, J. Silva-Rodr´ıguez, J. M. Aldrey, J. Cort´es, A. Fern´andez-Ferreiro, N. G´omez-Lado, ´A. Ruibal, and P. Aguiar. Prediction of Alzheimer’s disease dementia with MRI beyond the short-term: Implications for the design of predictive models. NeuroImage: Clinical, page 101837, apr 2019. ISSN 2213-1582. doi: 10.1016/J.NICL.2019.101837.
L. Nanni, C. Salvatore, A. Cerasa, and I. Castiglioni. Combining multiple approaches for the early diagnosis of Alzheimer’s Disease. Pattern Recognition Letters, 84:259–266, dec 2016. ISSN 0167-8655. doi: 10.1016/J.PATREC.2016.10.010.
New York State Coordinating Council. 2017 Report of the New York State Coordinating Council for Services Related to Alzheimer’s Disease and Other Dementias to Governor Andrew M. Cuomo and the New York State Legislature. Technical report, 2017.
N. O’Kelly. Use of Machine Learning Technology in the Diagnosis of Alzheimer’s Disease Declaration of Authorship. Technical report, 2016.
S. Patel, B.-R. Bor-rong Chen, T. Buckley, R. Rednic, D. McClure, D. Tarsy, L. Shih, J. Dy, M. Welsh, and P. Bonato. Home monitoring of patients with Parkinson’s disease via wearable technology and a web-based application. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, volume 2010, pages 4411–4414. IEEE, aug 2010. ISBN 978-1-4244-4123-5. doi: 10.1109/IEMBS.2010. 5627124.
T. Pereira, L. Lemos, S. Cardoso, D. Silva, A. Rodrigues, I. Santana, A. de Mendon¸ca, M. Guerreiro, and S. C. Madeira. Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows. BMC Medical Informatics and Decision Making, 17(1):110, dec 2017. ISSN 1472-6947. doi: 10.1186/s12911-017-0497-2.
K. Przednowek, K. Wiktorowicz, T. Krzeszowski, and J. Iskra. A web-oriented expert system for planning hurdles race training programmes. Neural Computing and Applications, pages 1–17, may 2018. ISSN 0941-0643. doi: 10.1007/s00521-018-3559-1.
A. Schmidt-Richberg, C. Ledig, R. Guerrero, H. Molina-Abril, A. Frangi, D. Rueckert, and o. b. o. t. A. D. N. Alzheimer’s Disease Neuroimaging Initiative. Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease. PloS one, 11(4):e0153040, 2016. ISSN 1932-6203. doi: 10.1371/journal.pone.0153040.
J. Skinner, J. O. Carvalho, G. G. Potter, A. Thames, E. Zelinski, P. K. Crane, L. E. Gibbons, and Alzheimer’s Disease Neuroimaging Initiative. The Alzheimer’s Disease Assessment Scale-Cognitive-Plus (ADAS-Cog-Plus): an expansion of the ADAS-Cog to improve responsiveness in MCI. Brain imaging and behavior, 6(4):489–501, dec 2012. ISSN 1931-7565. doi: 10.1007/s11682-012-9166-3.
V. Venkatraghavan, E. E. Bron, W. J. Niessen, and S. Klein. Disease progression timeline estimation for Alzheimer’s disease using discriminative event based modeling. NeuroImage, 186:518–532, feb 2019. ISSN 1053-8119. doi: 10.1016/J.NEUROIMAGE. 2018.11.024.
S. Vieira, W. H. Pinaya, and A. Mechelli. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neuroscience & Biobehavioral Reviews, 74:58–75, mar 2017. ISSN 0149-7634. doi: 10.1016/J.NEUBIOREV.2017.01.002.
M. Wang, D. Zhang, D. Shen, and M. Liu. Multi-task exclusive relationship learning for alzheimer’s disease progression prediction with longitudinal data. Medical Image Analysis, 53:111–122, apr 2019. ISSN 1361-8415. doi: 10.1016/J.MEDIA.2019.01.007.
T. Wang, R. G. Qiu, and M. Yu. Predictive Modeling of the Progression of Alzheimer’s Disease with Recurrent Neural Networks. Scientific Reports, 8(1):9161, dec 2018. doi: 10.1038/s41598-018-27337-w.
E. Yang, M. Farnum, V. Lobanov, T. Schultz, N. Raghavan, M. N. Samtani, G. Novak, V. Narayan, and A. DiBernardo. Quantifying the Pathophysiological Timeline of Alzheimer’s Disease. Journal of Alzheimer’s Disease, 26(4):745–753, oct 2011. ISSN 18758908. doi: 10.3233/JAD-2011-110551.
D. Yao, V. D. Calhoun, Z. Fu, Y. Du, and J. Sui. An ensemble learning system for a 4-way classification of Alzheimer’s disease and mild cognitive impairment. Journal of Neuroscience Methods, 302:75–81, may 2018. ISSN 0165-0270. doi: 10.1016/J. JNEUMETH.2018.03.008.
A. L. Young, N. P. Oxtoby, P. Daga, D. M. Cash, N. C. Fox, S. Ourselin, J. M. Schott, and D. C. Alexander. A data-driven model of biomarker changes in sporadic Alzheimer’s disease. Brain, 137(9):2564–2577, sep 2014a. ISSN 1460-2156. doi: 10.1093/brain/ awu176.
A. L. Young, N. P. Oxtoby, P. Daga, D. M. Cash, N. C. Fox, S. Ourselin, J. M. Schott, and D. C. Alexander. A data-driven model of biomarker changes in sporadic Alzheimer’s disease. Brain, 137(9):2564–2577, sep 2014b. ISSN 1460-2156. doi: 10.1093/brain/ awu176.
R. Zhang, G. Simon, and F. Yu. Advancing Alzheimer’s research: A review of big data promises. International Journal of Medical Informatics, 106:48–56, oct 2017. ISSN 1386-5056. doi: 10.1016/J.IJMEDINF.2017.07.002.
Z. Zhang and E. Sejdi´c. Radiological images and machine learning: Trends, perspectives, and prospects. Computers in Biology and Medicine, 108:354–370, may 2019. ISSN 0010-4825. doi: 10.1016/J.COMPBIOMED.2019.02.017.
J. Zhou, E. Gennatas, J. Kramer, B. Miller, and W. Seeley. Predicting Regional Neurodegeneration from the Healthy Brain Functional Connectome. Neuron, 73(6): 1216–1227, mar 2012. ISSN 08966273. doi: 10.1016/j.neuron.2012.03.004.

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