1. Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271. 2. Rahman, M., Al Amin, M., Hasan, R., Hossain, S. T., Rahman, M. H., & Rashed, R. A. M. (2025). A Predictive AI Framework for Cardiovascular Disease Screening in the US: Integrating EHR Data with Machine and Deep Learning Models. British Journal of Nursing Studies, 5(2), 40-48. 3. ZakirHossain, M., Khan, M. M., Thapa, S., Uddin, R., Meem, E. J., Niloy, S. K., ... & Bhavani, G. D. (2025, February). Advanced Deep Learning Techniques for Precision Diagnosis of Tea Leaf Diseases. In 2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) (pp. 1-6). IEEE. 4. Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018). Recurrent neural networks for multivariate time series with missing values. Scientific Reports, *8*(1), 6085. 5. Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. In Proceedings of the 1st Machine Learning for Healthcare Conference (pp. 301-318). 6. Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, *24*(2), 361-370. 7. Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555. 8. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, *9*(8), 1735-1780. 9. Lipton, Z. C., Kale, D. C., Elkan, C., & Wetzel, R. (2016). Learning to diagnose with LSTM recurrent neural networks. arXiv preprint arXiv:1511.03677. 10. Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G. E., Irvine, J., Le, Q., Litsch, K., ... Dean, J. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, *1*(1), 18. 11. Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE Journal of Biomedical and Health Informatics, *22*(5), 1589-1604. 12. Song, H., Rajan, D., Thiagarajan, J. J., & Spanias, A. (2018). Attend and diagnose: Clinical time series analysis using attention models. In Proceedings of the AAAI Conference on Artificial Intelligence, *32*(1). 13. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems, *30*. 14. Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, *19*(6), 1236-1246. 15. Beaulieu-Jones, B. K., Yuan, W., Brat, G. A., Lai, A., Orzechowski, N., Finn, C., ... & Weber, G. M. (2019). Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?. NPJ Digital Medicine, *2*(1), 62. 16. Yoon, J., Zame, W. R., & van der Schaar, M. (2018). Deep sensing: Active sensing using deep learning. IEEE Transactions on Signal Processing, *66*(20), 5438-5452. 17. Futoma, J., Simons, M., Panch, T., Doshi-Velez, F., & Celi, L. A. (2020). The myth of generalisability in clinical research and machine learning in health care. The Lancet Digital Health, *2*(9), e489-e492.

BibTeX

@article{allodium:10.5281/zenodo.16966856,
  title = {1.	Bai, S., Kolter, J. Z., \& Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271. 2.	Rahman, M., Al Amin, M., Hasan, R., Hossain, S. T., Rahman, M. H., \& Rashed, R. A. M. (2025). A Predictive AI Framework for Cardiovascular Disease Screening in the US: Integrating EHR Data with Machine and Deep Learning Models. British Journal of Nursing Studies, 5(2), 40-48. 3.	ZakirHossain, M., Khan, M. M., Thapa, S., Uddin, R., Meem, E. J., Niloy, S. K., ... \& Bhavani, G. D. (2025, February). Advanced Deep Learning Techniques for Precision Diagnosis of Tea Leaf Diseases. In 2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) (pp. 1-6). IEEE.  4.	Che, Z., Purushotham, S., Cho, K., Sontag, D., \& Liu, Y. (2018). Recurrent neural networks for multivariate time series with missing values. Scientific Reports, *8*(1), 6085. 5.	Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., \& Sun, J. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. In Proceedings of the 1st Machine Learning for Healthcare Conference (pp. 301-318). 6.	Choi, E., Schuetz, A., Stewart, W. F., \& Sun, J. (2016). Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, *24*(2), 361-370. 7.	Chung, J., Gulcehre, C., Cho, K., \& Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555. 8.	Hochreiter, S., \& Schmidhuber, J. (1997). Long short-term memory. Neural Computation, *9*(8), 1735-1780. 9.	Lipton, Z. C., Kale, D. C., Elkan, C., \& Wetzel, R. (2016). Learning to diagnose with LSTM recurrent neural networks. arXiv preprint arXiv:1511.03677. 10.	Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G. E., Irvine, J., Le, Q., Litsch, K., ... Dean, J. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, *1*(1), 18. 11.	Shickel, B., Tighe, P. J., Bihorac, A., \& Rashidi, P. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE Journal of Biomedical and Health Informatics, *22*(5), 1589-1604. 12.	Song, H., Rajan, D., Thiagarajan, J. J., \& Spanias, A. (2018). Attend and diagnose: Clinical time series analysis using attention models. In Proceedings of the AAAI Conference on Artificial Intelligence, *32*(1). 13.	Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., \& Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems, *30*. 14.	Miotto, R., Wang, F., Wang, S., Jiang, X., \& Dudley, J. T. (2018). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, *19*(6), 1236-1246. 15.	Beaulieu-Jones, B. K., Yuan, W., Brat, G. A., Lai, A., Orzechowski, N., Finn, C., ... \& Weber, G. M. (2019). Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?. NPJ Digital Medicine, *2*(1), 62. 16.	Yoon, J., Zame, W. R., \& van der Schaar, M. (2018). Deep sensing: Active sensing using deep learning. IEEE Transactions on Signal Processing, *66*(20), 5438-5452. 17.	Futoma, J., Simons, M., Panch, T., Doshi-Velez, F., \& Celi, L. A. (2020). The myth of generalisability in clinical research and machine learning in health care. The Lancet Digital Health, *2*(9), e489-e492.},
  author = {Billy, Elly},
  year = {2025},
  doi = {10.5281/zenodo.16966856},
  url = {https://doi.org/10.5281/zenodo.16966856}
}

RIS

TY  - JOUR
TI  - 1.	Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271. 2.	Rahman, M., Al Amin, M., Hasan, R., Hossain, S. T., Rahman, M. H., & Rashed, R. A. M. (2025). A Predictive AI Framework for Cardiovascular Disease Screening in the US: Integrating EHR Data with Machine and Deep Learning Models. British Journal of Nursing Studies, 5(2), 40-48. 3.	ZakirHossain, M., Khan, M. M., Thapa, S., Uddin, R., Meem, E. J., Niloy, S. K., ... & Bhavani, G. D. (2025, February). Advanced Deep Learning Techniques for Precision Diagnosis of Tea Leaf Diseases. In 2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) (pp. 1-6). IEEE.  4.	Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018). Recurrent neural networks for multivariate time series with missing values. Scientific Reports, *8*(1), 6085. 5.	Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. In Proceedings of the 1st Machine Learning for Healthcare Conference (pp. 301-318). 6.	Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, *24*(2), 361-370. 7.	Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555. 8.	Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, *9*(8), 1735-1780. 9.	Lipton, Z. C., Kale, D. C., Elkan, C., & Wetzel, R. (2016). Learning to diagnose with LSTM recurrent neural networks. arXiv preprint arXiv:1511.03677. 10.	Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G. E., Irvine, J., Le, Q., Litsch, K., ... Dean, J. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, *1*(1), 18. 11.	Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE Journal of Biomedical and Health Informatics, *22*(5), 1589-1604. 12.	Song, H., Rajan, D., Thiagarajan, J. J., & Spanias, A. (2018). Attend and diagnose: Clinical time series analysis using attention models. In Proceedings of the AAAI Conference on Artificial Intelligence, *32*(1). 13.	Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems, *30*. 14.	Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, *19*(6), 1236-1246. 15.	Beaulieu-Jones, B. K., Yuan, W., Brat, G. A., Lai, A., Orzechowski, N., Finn, C., ... & Weber, G. M. (2019). Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?. NPJ Digital Medicine, *2*(1), 62. 16.	Yoon, J., Zame, W. R., & van der Schaar, M. (2018). Deep sensing: Active sensing using deep learning. IEEE Transactions on Signal Processing, *66*(20), 5438-5452. 17.	Futoma, J., Simons, M., Panch, T., Doshi-Velez, F., & Celi, L. A. (2020). The myth of generalisability in clinical research and machine learning in health care. The Lancet Digital Health, *2*(9), e489-e492.
AU  - Billy, Elly
PY  - 2025
DO  - 10.5281/zenodo.16966856
UR  - https://doi.org/10.5281/zenodo.16966856
ER  - 

APA

Elly, B. (2025). 1. Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271. 2. Rahman, M., Al Amin, M., Hasan, R., Hossain, S. T., Rahman, M. H., & Rashed, R. A. M. (2025). A Predictive AI Framework for Cardiovascular Disease Screening in the US: Integrating EHR Data with Machine and Deep Learning Models. British Journal of Nursing Studies, 5(2), 40-48. 3. ZakirHossain, M., Khan, M. M., Thapa, S., Uddin, R., Meem, E. J., Niloy, S. K., ... & Bhavani, G. D. (2025, February). Advanced Deep Learning Techniques for Precision Diagnosis of Tea Leaf Diseases. In 2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) (pp. 1-6). IEEE. 4. Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018). Recurrent neural networks for multivariate time series with missing values. Scientific Reports, *8*(1), 6085. 5. Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. In Proceedings of the 1st Machine Learning for Healthcare Conference (pp. 301-318). 6. Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, *24*(2), 361-370. 7. Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555. 8. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, *9*(8), 1735-1780. 9. Lipton, Z. C., Kale, D. C., Elkan, C., & Wetzel, R. (2016). Learning to diagnose with LSTM recurrent neural networks. arXiv preprint arXiv:1511.03677. 10. Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G. E., Irvine, J., Le, Q., Litsch, K., ... Dean, J. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, *1*(1), 18. 11. Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE Journal of Biomedical and Health Informatics, *22*(5), 1589-1604. 12. Song, H., Rajan, D., Thiagarajan, J. J., & Spanias, A. (2018). Attend and diagnose: Clinical time series analysis using attention models. In Proceedings of the AAAI Conference on Artificial Intelligence, *32*(1). 13. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems, *30*. 14. Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, *19*(6), 1236-1246. 15. Beaulieu-Jones, B. K., Yuan, W., Brat, G. A., Lai, A., Orzechowski, N., Finn, C., ... & Weber, G. M. (2019). Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?. NPJ Digital Medicine, *2*(1), 62. 16. Yoon, J., Zame, W. R., & van der Schaar, M. (2018). Deep sensing: Active sensing using deep learning. IEEE Transactions on Signal Processing, *66*(20), 5438-5452. 17. Futoma, J., Simons, M., Panch, T., Doshi-Velez, F., & Celi, L. A. (2020). The myth of generalisability in clinical research and machine learning in health care. The Lancet Digital Health, *2*(9), e489-e492.. https://doi.org/10.5281/zenodo.16966856