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\n  \n 2023\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Subtyping patients with chronic disease using longitudinal BMI patterns.\n \n \n \n\n\n \n Mottalib, M. M.; Jones-Smith, J. C; Sheridan, B.; and Beheshti, R.\n\n\n \n\n\n\n IEEE Journal of Biomedical and Health Informatics,1-12. 2023.\n \n\n\n\n
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@ARTICLE{dip2023,\n  author={Mottalib, Md Mozaharul and Jones-Smith, Jessica C and Sheridan, Bethany and Beheshti, Rahmatollah},\n  journal={IEEE Journal of Biomedical and Health Informatics}, \n  title={Subtyping patients with chronic disease using longitudinal BMI patterns}, \n  year={2023},\n              AUTHOR+an = {1=student;4=lead},\n  volume={},\n  number={},\n  pages={1-12},\n  doi={10.1109/JBHI.2023.3237753}}\n\n  \n
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\n  \n 2022\n \n \n (7)\n \n \n
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\n \n\n \n \n \n \n \n \n Obesity Prediction with EHR Data: A Deep Learning Approach with Interpretable Elements.\n \n \n \n \n\n\n \n Gupta, M.; Phan, T. T; Bunnell, H T.; and Beheshti, R.\n\n\n \n\n\n\n ACM Trans. Comput. Healthcare, 3(3): Article 32. 2022.\n \n\n\n\n
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@article{Gupta2022ObesityElements,\n    title = {{Obesity Prediction with EHR Data: A Deep Learning Approach with Interpretable Elements}},\n    year = {2022},\n    journal = {ACM Trans. Comput. Healthcare},\n    author = {Gupta, Mehak and Phan, Thao-Ly T and Bunnell, H Timothy and Beheshti, Rahmatollah},\n                    AUTHOR+an = {1=student;4=lead},\n    number = {3},\n    pages = {Article 32},\n    volume = {3},\n    url = {https://doi.org/10.1145/3506719},\n    doi = {10.1145/3506719},\n    issn = {2691-1957},\n    keywords = {electronic health records, long short-term memory, deep learning, Childhood obesity, temporal data, transfer learning}\n}\n\n
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\n \n\n \n \n \n \n \n \n Predicting Acute Events using the Movement Patterns of Older Adults: An Unsupervised Clustering Method.\n \n \n \n \n\n\n \n Ramazi, R.; Bowen, M. E.; and Beheshti, R.\n\n\n \n\n\n\n Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022, 1(22). 8 2022.\n \n\n\n\n
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@article{Ramazi2022PredictingMethod,\n    title = {{Predicting Acute Events using the Movement Patterns of Older Adults: An Unsupervised Clustering Method}},\n    year = {2022},\n    journal = {Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022},\n    author = {Ramazi, Ramin and Bowen, Mary Elizabeth and Beheshti, Rahmatollah},\n                    AUTHOR+an = {1=student;3=lead},\n    number = {22},\n    month = {8},\n    volume = {1},\n    publisher = {Association for Computing Machinery, Inc},\n    url = {https://doi.org/10.1145/3535508.3545561},\n    isbn = {9781450393867},\n    doi = {10.1145/3535508.3545561},\n    addendum = {Competitive}\n}\n\n\n
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\n \n\n \n \n \n \n \n \n Predicting attrition patterns from pediatric weight management programs.\n \n \n \n \n\n\n \n Fayyaz, H.; Phan, T. T.; Bunnell, H. T.; and Beheshti, R.\n\n\n \n\n\n\n In Parziale, A.; Agrawal, M.; Joshi, S.; Chen, I. Y.; Tang, S.; Oala, L.; and Subbaswamy, A., editor(s), Proceedings of the 2nd Machine Learning for Health symposium, volume 193, of Proceedings of Machine Learning Research, pages 326–342, 28 Nov 2022. PMLR\n \n\n\n\n
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@InProceedings{pmlr-v193-fayyaz22a,\n  title = \t {Predicting attrition patterns from pediatric weight management programs},\n  author =       {Fayyaz, Hamed and Phan, Thao-Ly T. and Bunnell, H. Timothy and Beheshti, Rahmatollah},\n  booktitle = \t {Proceedings of the 2nd Machine Learning for Health symposium},\n  pages = \t {326--342},\n  year = \t {2022},\n  editor = \t {Parziale, Antonio and Agrawal, Monica and Joshi, Shalmali and Chen, Irene Y. and Tang, Shengpu and Oala, Luis and Subbaswamy, Adarsh},\n  volume = \t {193},\n  series = \t {Proceedings of Machine Learning Research},\n  month = \t {28 Nov},\n  publisher =    {PMLR},\n  pdf = \t {https://proceedings.mlr.press/v193/fayyaz22a/fayyaz22a.pdf},\n  url = \t {https://proceedings.mlr.press/v193/fayyaz22a.html}\n}\n\n
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\n \n\n \n \n \n \n \n \n An Extensive Data Processing Pipeline for MIMIC-IV.\n \n \n \n \n\n\n \n Gupta, M.; Gallamoza, B.; Cutrona, N.; Dhakal, P.; Poulain, R.; and Beheshti, R.\n\n\n \n\n\n\n In Parziale, A.; Agrawal, M.; Joshi, S.; Chen, I. Y.; Tang, S.; Oala, L.; and Subbaswamy, A., editor(s), Proceedings of the 2nd Machine Learning for Health symposium, volume 193, of Proceedings of Machine Learning Research, pages 311–325, 28 Nov 2022. PMLR\n \n\n\n\n
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@InProceedings{pmlr-v193-gupta22a,\n  title = \t {An Extensive Data Processing Pipeline for MIMIC-IV},\n  author =       {Gupta, Mehak and Gallamoza, Brennan and Cutrona, Nicolas and Dhakal, Pranjal and Poulain, Raphael and Beheshti, Rahmatollah},\n  booktitle = \t {Proceedings of the 2nd Machine Learning for Health symposium},\n  pages = \t {311--325},\n  year = \t {2022},\n  editor = \t {Parziale, Antonio and Agrawal, Monica and Joshi, Shalmali and Chen, Irene Y. and Tang, Shengpu and Oala, Luis and Subbaswamy, Adarsh},\n  volume = \t {193},\n  series = \t {Proceedings of Machine Learning Research},\n  month = \t {28 Nov},\n  publisher =    {PMLR},\n  pdf = \t {https://proceedings.mlr.press/v193/gupta22a/gupta22a.pdf},\n  url = \t {https://proceedings.mlr.press/v193/gupta22a.html}\n}\n\n
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\n \n\n \n \n \n \n \n Developing Acute Event Risk Profiles for Older Adults With Dementia in Long-Term Care Using Motor Behavior Clusters Derived From Deep Learning.\n \n \n \n\n\n \n Ramazi, R.; Bowen, M. E.; Flynn, A. J; and Beheshti, R.\n\n\n \n\n\n\n Journal of the American Medical Directors Association. 2022.\n \n\n\n\n
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@article{Ramazi2022DevelopingLearning,\n    title = {{Developing Acute Event Risk Profiles for Older Adults With Dementia in Long-Term Care Using Motor Behavior Clusters Derived From Deep Learning}},\n    year = {2022},\n    journal = {Journal of the American Medical Directors Association},\n    author = {Ramazi, Ramin and Bowen, Mary Elizabeth and Flynn, Aidan J and Beheshti, Rahmatollah},\n            AUTHOR+an = {1=student;4=lead},\n    doi = {https://doi.org/10.1016/j.jamda.2022.04.009},\n    issn = {1525-8610},\n    keywords = {Falls, clustering, delirium, dementia, real-time locating system, urinary tract infections}\n}\n\n\n
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\n \n\n \n \n \n \n \n Few-Shot Learning with Semi-Supervised Transformers for Electronic Health Records.\n \n \n \n\n\n \n Poulain, R.; Gupta, M.; and Beheshti, R.\n\n\n \n\n\n\n In Proceedings of the Machine Learning for Healthcare (MLHC-2022), 2022. \n \n\n\n\n
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@inproceedings{Poulain2022Few-ShotRecords,\n    title = {{Few-Shot Learning with Semi-Supervised Transformers for Electronic Health Records}},\n    year = {2022},\n    booktitle = {Proceedings of the Machine Learning for Healthcare (MLHC-2022)},\n    author = {Poulain, Raphael and Gupta, Mehak and Beheshti, Rahmatollah},\n            AUTHOR+an = {1=student;2=student;4=lead},\n    addendum = {Highly competitive}\n}\n\n
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\n \n\n \n \n \n \n \n Flexible-window Predictions on Electronic Health Records.\n \n \n \n\n\n \n Gupta, M.; Phan, T. T; Bunnell, H T.; and Beheshti, R.\n\n\n \n\n\n\n In Proceedings of the Thirty-Fourth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-22), 2022. AAAI\n \n\n\n\n
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@inproceedings{Gupta2022Flexible-windowRecords,\n    title = {{Flexible-window Predictions on Electronic Health Records}},\n    year = {2022},\n    booktitle = {Proceedings of the Thirty-Fourth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-22)},\n    author = {Gupta, Mehak and Phan, Thao-Ly T and Bunnell, H Timothy and Beheshti, Rahmatollah},\n                AUTHOR+an = {1=student;4=lead},\n    publisher = {AAAI},\n    addendum = {Highly competitive}\n}\n\n\n
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\n  \n 2021\n \n \n (5)\n \n \n
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\n \n\n \n \n \n \n \n Predicting cardiovascular health trajectories in time-series electronic health records with LSTM models.\n \n \n \n\n\n \n Guo, A.; Beheshti, R.; Khan, Y. M; Langabeer, J. R; and Foraker, R. E\n\n\n \n\n\n\n BMC Medical Informatics and Decision Making, 21(1): 1–10. 2021.\n \n\n\n\n
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@article{Guo2021PredictingModels,\n    title = {{Predicting cardiovascular health trajectories in time-series electronic health records with LSTM models}},\n    year = {2021},\n    journal = {BMC Medical Informatics and Decision Making},\n    author = {Guo, Aixia and Beheshti, Rahmatollah and Khan, Yosef M and Langabeer, James R and Foraker, Randi E},\n                        AUTHOR+an = {2=lead},\n    number = {1},\n    pages = {1--10},\n    volume = {21},\n    publisher = {BioMed Central}\n}\n\n\n\n
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\n \n\n \n \n \n \n \n Predicting progression patterns of type 2 diabetes using multi-sensor measurements.\n \n \n \n\n\n \n Ramazi, R.; Perndorfer, C.; Soriano, E. C; Laurenceau, J.; and Beheshti, R.\n\n\n \n\n\n\n Smart Health, 21: 100206. 2021.\n \n\n\n\n
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@article{Ramazi2021PredictingMeasurements,\n    title = {{Predicting progression patterns of type 2 diabetes using multi-sensor measurements}},\n    year = {2021},\n    journal = {Smart Health},\n    author = {Ramazi, Ramin and Perndorfer, Christine and Soriano, Emily C and Laurenceau, Jean-Philippe and Beheshti, Rahmatollah},\n                    AUTHOR+an = {1=student;5=lead},\n    pages = {100206},\n    volume = {21},\n    doi = {https://doi.org/10.1016/j.smhl.2021.100206},\n    issn = {2352-6483},\n    keywords = {Continuous glucose monitoring, Deep learning, Multi-modal data, Predictive modeling, Type 2 diabetes}\n}\n\n\n
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\n \n\n \n \n \n \n \n Transformer-based Multi-target Regression on Electronic Health Records for Primordial Prevention of Cardiovascular Disease.\n \n \n \n\n\n \n Poulain, R.; Gupta, M.; Foraker, R.; and Beheshti, R.\n\n\n \n\n\n\n In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 726–731, 2021. \n \n\n\n\n
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@inproceedings{Poulain2021Transformer-basedDisease,\n    title = {{Transformer-based Multi-target Regression on Electronic Health Records for Primordial Prevention of Cardiovascular Disease}},\n    year = {2021},\n    booktitle = {2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},\n    author = {Poulain, Raphael and Gupta, Mehak and Foraker, Randi and Beheshti, Rahmatollah},\n                    AUTHOR+an = {1=student;2=student;4=lead},\n    pages = {726--731},\n    doi = {10.1109/BIBM52615.2021.9669441},\n    addendum = {Competitive}\n}\n\n\n\n
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\n \n\n \n \n \n \n \n \n Concurrent imputation and prediction on EHR data using bi-directional GANs.\n \n \n \n \n\n\n \n Gupta, M.; Phan, T. T; Bunnell, H T.; and Beheshti, R.\n\n\n \n\n\n\n In Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, pages Article 7, Gainesville, Florida, 2021. Association for Computing Machinery\n \n\n\n\n
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@inproceedings{Gupta2021ConcurrentGANs,\n    title = {{Concurrent imputation and prediction on EHR data using bi-directional GANs}},\n    year = {2021},\n    booktitle = {Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics},\n    author = {Gupta, Mehak and Phan, Thao-Ly T and Bunnell, H Timothy and Beheshti, Rahmatollah},\n        AUTHOR+an = {1=student;4=lead},\n    pages = {Article 7},\n    publisher = {Association for Computing Machinery},\n    url = {https://doi.org/10.1145/3459930.3469512},\n    address = {Gainesville, Florida},\n    doi = {10.1145/3459930.3469512},\n    keywords = {electronic health record, adversarial training, recurrent neural network},\n    addendum = {Competitive}\n}\n\n
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\n \n\n \n \n \n \n \n Identifying the Leading Factors of Significant Weight Gains Using a New Rule Discovery Method.\n \n \n \n\n\n \n Samizadeh, M.; Jones-Smith, J. C; Sheridan, B.; and Beheshti, R.\n\n\n \n\n\n\n arXiv preprint arXiv:2111.04475. 2021.\n \n\n\n\n
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@article{Samizadeh2021IdentifyingMethod,\n    title = {{Identifying the Leading Factors of Significant Weight Gains Using a New Rule Discovery Method}},\n    year = {2021},\n    journal = {arXiv preprint arXiv:2111.04475},\n                        AUTHOR+an = {1=lead},\n    author = {Samizadeh, Mina and Jones-Smith, Jessica C and Sheridan, Bethany and Beheshti, Rahmatollah}\n}\n\n\n
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\n \n\n \n \n \n \n \n Multi-Modal Predictive Models of Diabetes Progression.\n \n \n \n\n\n \n Ramazi, R.; Perndorfer, C.; Soriano, E.; Laurenceau, J.; and Beheshti, R.\n\n\n \n\n\n\n In Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, pages 253–258, New York, NY, USA, 2019. Association for Computing Machinery\n \n\n\n\n
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@inproceedings{Ramazi2019Multi-ModalProgression,\n    title = {{Multi-Modal Predictive Models of Diabetes Progression}},\n    year = {2019},\n    booktitle = {Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics},\n    author = {Ramazi, Ramin and Perndorfer, Christine and Soriano, Emily and Laurenceau, Jean-Philippe and Beheshti, Rahmatollah},\n                    AUTHOR+an = {1=student;5=lead},\n    pages = {253--258},\n    publisher = {Association for Computing Machinery},\n    address = {New York, NY, USA},\n    isbn = {9781450366663},\n    doi = {10.1145/3307339.3342177},\n    addendum = {Competitive}\n}
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