Revealing clinical heterogeneity in a large brain bank cohort
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Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s41591-024-02871-5.pdf
Reference5 articles.
1. Young, A. L. et al. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with subtype and atage inference. Nat. Commun. 9, 4273 (2018). This paper uses machine learning to identify disease phenotypes, based on patient studies.
2. Selvackadunco, S. et al. Comparison of clinical and neuropathological diagnoses of neurodegenerative diseases in two centres from the Brains for Dementia Research (BDR) cohort. J. Neural Transm. 126, 327–337 (2019). This paper determined the agreement between in-life clinical diagnosis and postmortem pathological results for different dementias.
3. Dementia research needs a global approach Nat. Med. 29, 279 (2023). This editorial advocates for dementia research that is most likely to produce the greatest global impact.
4. Yu, G. et al. Domain-specific language model pretraining for biomedical natural language processing. ACM Trans. Comput. Healthc. 3, 1–23 (2021). This paper presents the natural language model PubMedBERT.
5. Che, Z. et al. Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8, 6085 (2018). This paper presents the neural network GRU-D used in this study.
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