Machine learning for comprehensive forecasting of Alzheimer’s Disease progression

Abstract

Most approaches to machine learning from electronic health data can only predict a single endpoint. The ability to simultaneously simulate dozens of patient characteristics is a crucial step towards personalized medicine for Alzheimer’s Disease. Here, we use an unsupervised machine learning model called a Conditional Restricted Boltzmann Machine (CRBM) to simulate detailed patient trajectories. We use data comprising 18-month trajectories of 44 clinical variables from 1909 patients with Mild Cognitive Impairment or Alzheimer’s Disease to train a model for personalized forecasting of disease progression. We simulate synthetic patient data including the evolution of each sub-component of cognitive exams, laboratory tests, and their associations with baseline clinical characteristics. Synthetic patient data generated by the CRBM accurately reflect the means, standard deviations, and correlations of each variable over time to the extent that synthetic data cannot be distinguished from actual data by a logistic regression. Moreover, our unsupervised model predicts changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models, additionally capturing the correlation structure in the components of ADAS-Cog, and identifies sub-components associated with word recall as predictive of progression.

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References 50

1. 1.

   Collins FS, Varmus H. A new initiative on precision medicine. New Engl. J. Medicine. 2015;372:793-795.

   Not in our library10.1056/nejmp1500523

2. 2.
   Scalable and accurate deep learning with electronic health records · 2018

   No notice
3. 3.
   Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records · 2016

   No notice
4. 4.

   Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F. & Sun, J. Doctor AI: Predicting clinical events via recurrent neural networks. In Machine Learning for Healthcare Conference, 301–318 (2016).

   Not in our library

5. 5.
   Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data · 2013

   Corrected
6. 6.

   Lipton, Z. C., Kale, D. C., Elkan, C. & Wetzel, R. Learning to diagnose with LSTM recurrent neural networks. arXiv preprint arXiv:1511.03677 (2015).

   Not in our library

7. 7.

   Myers PD, Scirica BM, Stultz CM. Machine learning improves risk stratification after acute coronary syndrome. Sci. reports. 2017;7:12692.

   Not in our library10.1038/s41598-017-12951-x

8. 8.

   Choi, E. et al. Generating multi-label discrete electronic health records using generative adversarial networks. arXiv preprint arXiv:1703.06490 (2017).

   Not in our library

9. 9.

   Esteban, C., Hyland, S. L. & Rätsch, G. Real-valued (medical) time series generation with recurrent conditional gans. arXiv preprint arXiv:1706.02633 (2017).

   Not in our library

10. 10.
    Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing · 2019

    No notice
11. 11.

    Goldstein BA, Navar AM, Pencina MJ, Ioannidis J. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J. Am. Med. Informatics Assoc. 2017;24:198-208.

    Not in our library10.1093/jamia/ocw042

12. 12.

    Kumar A, et al. A review on alzheimer’s disease pathophysiology and its management: an update. Pharmacol. Reports. 2015;67:195-203.

    Not in our library10.1016/j.pharep.2014.09.004

13. 13.

    Rosen, W. G., Mohs, R. C. & Davis, K. L. A new rating scale for Alzheimer’s disease. The Am. journal psychiatry (1984).

    Not in our library10.1176/ajp.141.11.1356

14. 14.

    Folstein MF, Folstein SE, McHugh PR. “Mini-Mental State”: a practical method for grading the cognitive state of patients for the clinician. J. psychiatric research. 1975;12:189-198.

    Not in our library10.1016/0022-3956(75)90026-6

15. 15.
    Drug development in Alzheimer’s disease: the path to 2025 · 2016

    No notice
16. 16.

    Raamana PR, et al. Three-Class Differential Diagnosis among Alzheimer Disease, Frontotemporal Dementia, and Controls. Front. Neurol. 2014;5:71.

    Not in our library10.3389/fneur.2014.00071

17. 17.

    Rogers JA, et al. Combining patient-level and summary-level data for Alzheimer’s disease modeling and simulation: a beta regression meta-analysis. J. pharmacokinetics pharmacodynamics. 2012;39:479-498.

    Not in our library10.1007/s10928-012-9263-3

18. 18.

    Ito K, et al. Understanding placebo responses in Alzheimer’s disease clinical trials from the literature meta-data and CAMD database. J. Alzheimer’s Dis. 2013;37:173-183.

    Not in our library10.3233/jad-130575

19. 19.

    Kennedy RE, Cutter GR, Wang G, Schneider LS. Post hoc analyses of apoe genotype-defined subgroups in clinical trials. J. Alzheimer’s Dis. 2016;50:1205-1215.

    Not in our library10.3233/jad-150847

20. 20.

    Tishchenko, I., Riveros, C., Moscato, P. & Diseases, C. A. M. Alzheimer’s disease patient groups derived from a multivariate analysis of cognitive test outcomes in the Coalition Against Major Diseases dataset. Futur. science OA2, FSO140 (2016).

    Not in our library10.4155/fsoa-2016-0041

21. 21.

    Szalkai B, et al. Identifying combinatorial biomarkers by association rule mining in the CAMD Alzheimer’s database. Arch. gerontology geriatrics. 2017;73:300-307.

    Not in our library10.1016/j.archger.2017.08.006

22. 22.

    Mueller SG, et al. Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimer’s & Dementia: journal Alzheimer’s Assoc. 2005;1:55-66.

    Not in our library10.1016/j.jalz.2005.06.003

23. 23.
    Baseline MRI Predictors of Conversion from MCI to Probable AD in the ADNI Cohort · 2009

    No notice
24. 24.

    Hinrichs C, et al. Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. Neuroimage. 2011;55:574-589.

    Not in our library10.1016/j.neuroimage.2010.10.081

25. 25.

    Ito K, et al. Disease progression model for cognitive deterioration from Alzheimer’s Disease Neuroimaging Initiative database. Alzheimer’s & Dementia: journal Alzheimer’s Assoc. 2011;7:151-160.

    Not in our library10.1016/j.jalz.2010.03.018

26. 26.

    Weiner MW, et al. The Alzheimer’s Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimer’s &. Dementia: The J. Alzheimer’s Assoc. 2012;8:S1-68.

    Not in our library10.1016/j.jalz.2011.09.172

27. 27.

    Suk, H.-I. & Shen, D. Deep learning-based feature representation for AD/MCI classification. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 583–590 (Springer, 2013).

    Not in our library10.1007/978-3-642-40763-5_72

28. 28.

    Suk H-I, et al. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage. 2014;101:569-582.

    Not in our library10.1016/j.neuroimage.2014.06.077

29. 29.

    Liu, S. et al. Early diagnosis of Alzheimer’s disease with deep learning. In Biomedical Imaging (ISBI), 2014 IEEE 11thInternational Symposium on, 1015–1018 (IEEE, 2014).

    Not in our library

30. 30.

    Ortiz A, Munilla J, Gorriz JM, Ramirez J. Ensembles of deep learning architectures for the early diagnosis of the Alzheimer’s disease. Int. journal neural systems. 2016;26:1650025.

    Not in our library10.1142/s0129065716500258

31. 31.

    Samper-Gonzalez, J. et al. Yet another ADNI machine learning paper? Paving the way towards fully-reproducible research on classification of Alzheimer’s disease. In International Workshop on Machine Learning in Medical Imaging, 53–60 (Springer, 2017).

    Not in our library

32. 32.

    Corrigan, B. et al. Clinical trial simulation in Alzheimer’s disease. In Applied Pharmacometrics, 451–476 (Springer, 2014).

    Not in our library

33. 33.

    Romero K, et al. The future is now: Model-based clinical trial design for Alzheimer’s disease. Clin. Pharmacol. & Ther. 2015;97:210-214.

    Not in our library10.1002/cpt.16

34. 34.

    Romero K, et al. The Coalition Against Major Diseases: developing tools for an integrated drug development process for Alzheimer’s and Parkinson’s diseases. Clin. Pharmacol & Ther. 2009;86:365-367.

    Not in our library10.1038/clpt.2009.165

35. 35.

    Neville J, et al. Development of a unified clinical trial database for Alzheimer’s disease. Alzheimer’s & Dementia: journal Alzheimer’s Assoc. 2015;11:1212-1221.

    Not in our library10.1016/j.jalz.2014.11.005

36. 36.

    Ackley DH, Hinton GE, Sejnowski TJ. A learning algorithm for Boltzmann machines. Cogn. science. 1985;9:147-169.

    Not in our library10.1207/s15516709cog0901_7

37. 37.

    Hinton G. A practical guide to training restricted Boltzmann machines. Momentum. 2010;9:926.

    Not in our library

38. 38.

    Taylor, G. W., Hinton, G. E. & Roweis, S. T. Modeling human motion using binary latent variables. In Advances in Neural Information Processing Systems, 1345–1352 (2007).

    Not in our library

39. 39.

    Mnih, V., Larochelle, H. & Hinton, G. E. Conditional restricted Boltzmann machines for structured output prediction. In Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, 514–522 (AUAI Press, 2011).

    Not in our library

40. 40.

    Tubiana J, Monasson R. Emergence of compositional representations in restricted Boltzmann machines. Phys. review letters. 2017;118:138301.

    Not in our library10.1103/physrevlett.118.138301

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