var bibbase_data = {"data":"\"Loading..\"\n\n
\n\n \n\n \n\n \n \n\n \n\n \n \n\n \n\n \n
\n generated by\n \n \"bibbase.org\"\n\n \n
\n \n\n
\n\n \n\n\n
\n\n Excellent! Next you can\n create a new website with this list, or\n embed it in an existing web page by copying & pasting\n any of the following snippets.\n\n
\n JavaScript\n (easiest)\n
\n \n <script src=\"https://bibbase.org/show?bib=https%3A%2F%2Fsites.udel.edu%2Fhealthylaife%2Ffiles%2F2024%2F09%2Flabrefs.bib&jsonp=1&jsonp=1\"></script>\n \n
\n\n PHP\n
\n \n <?php\n $contents = file_get_contents(\"https://bibbase.org/show?bib=https%3A%2F%2Fsites.udel.edu%2Fhealthylaife%2Ffiles%2F2024%2F09%2Flabrefs.bib&jsonp=1\");\n print_r($contents);\n ?>\n \n
\n\n iFrame\n (not recommended)\n
\n \n <iframe src=\"https://bibbase.org/show?bib=https%3A%2F%2Fsites.udel.edu%2Fhealthylaife%2Ffiles%2F2024%2F09%2Flabrefs.bib&jsonp=1\"></iframe>\n \n
\n\n

\n For more details see the documention.\n

\n
\n
\n\n
\n\n This is a preview! To use this list on your own web site\n or create a new web site from it,\n create a free account. The file will be added\n and you will be able to edit it in the File Manager.\n We will show you instructions once you've created your account.\n
\n\n
\n\n

To the site owner:

\n\n

Action required! Mendeley is changing its\n API. In order to keep using Mendeley with BibBase past April\n 14th, you need to:\n

    \n
  1. renew the authorization for BibBase on Mendeley, and
    2. \n
  2. update the BibBase URL\n in your page the same way you did when you initially set up\n this page.\n
    3. \n
\n

\n\n

\n \n \n Fix it now\n

\n
\n\n
\n\n\n
\n \n \n
\n
\n  \n 2024\n \n \n (6)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Associations of Longitudinal BMI-Percentile Classification Patterns in Early Childhood with Neighborhood-Level Social Determinants of Health.\n \n \n \n \n\n\n \n Gupta, M.; Phan, T. T.; Lê-Scherban, F.; Eckrich, D.; Bunnell, H. T.; and Beheshti, R.\n\n\n \n\n\n\n Childhood Obesity. 2024.\n PMID: 39187268\n\n\n\n
\n\n\n\n \n \n \"AssociationsPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{Gupta24Associate,\nauthor = {Gupta, Mehak and Phan, Thao-Ly T. and L\\^{e}-Scherban, F\\'{e}lice and Eckrich, Daniel and Bunnell, H. Timothy and Beheshti, Rahmatollah},\nAUTHOR+an = {1=student; 6=lead},\ntitle = {Associations of Longitudinal BMI-Percentile Classification Patterns in Early Childhood with Neighborhood-Level Social Determinants of Health},\njournal = {Childhood Obesity},\nyear = {2024},\ndoi = {10.1089/chi.2023.0157},\n    note ={PMID: 39187268},\nURL = {https://doi.org/10.1089/chi.2023.0157\n},\n addendum = {Impact Factor: 1.5},\neprint = { https://doi.org/10.1089/chi.2023.0157\n}\n}\n\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Impact of COVID-19 Diagnosis on Weight Trajectories of Children in the US National COVID Cohort Collaborative (N3C) .\n \n \n \n\n\n \n Mottalib, M. M.; Phan, T. T.; Bramante, C. T; Chute, C. G.; A., P. L.; and Beheshti, R.\n\n\n \n\n\n\n Childhood Obesity. 2024.\n Article in press\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article {Mottalib2024N3c,\n\tauthor = {Mottalib, Md Mozaharul and Phan, Thao-Ly T. and Bramante, Carolyn T and Chute, Christopher G.  and Pyles Lee A.   and  Beheshti, Rahmatollah},\nAUTHOR+an = {1=student; 6=lead},\n\ttitle = {Impact of {COVID}-19 Diagnosis on Weight Trajectories of Children in the US National {COVID} Cohort Collaborative ({N3C}) },\n\tyear = {2024},\n\tjournal = {Childhood Obesity},\nnote = {Article in press},\n addendum = {Impact Factor: 1.5}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Reliable prediction of childhood obesity using only routinely collected EHRs may be possible.\n \n \n \n \n\n\n \n Gupta, M.; Eckrich, D.; Bunnell, H. T.; Phan, T. T.; and Beheshti, R.\n\n\n \n\n\n\n Obesity Pillars, 12: 100128. 2024.\n \n\n\n\n
\n\n\n\n \n \n \"ReliablePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{Gupta2024Reliable,\ntitle = {Reliable prediction of childhood obesity using only routinely collected {EHRs} may be possible},\njournal = {Obesity Pillars},\nvolume = {12},\npages = {100128},\nyear = {2024},\nissn = {2667-3681},\ndoi = {https://doi.org/10.1016/j.obpill.2024.100128},\nurl = {https://www.sciencedirect.com/science/article/pii/S2667368124000305},\nAUTHOR+an = {1=student; 5=lead},\nauthor = {Mehak Gupta and Daniel Eckrich and H. Timothy Bunnell and Thao-Ly T. Phan and Rahmatollah Beheshti},\n addendum = {The flagship journal of the Obesity Medicine Association (OMA). Established in 2024.}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Multimodal Sleep Apnea Detection with Missing or Noisy Modalities.\n \n \n \n \n\n\n \n Fayyaz, H.; D'Souza, N. S.; and Beheshti, R.\n\n\n \n\n\n\n In Machine Learning for Healthcare 2024, 2024. \n In press\n\n\n\n
\n\n\n\n \n \n \"MultimodalPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{fayyaz2024multimodal,\n      title={Multimodal Sleep Apnea Detection with Missing or Noisy Modalities}, \n      author={Hamed Fayyaz and Niharika S. D'Souza and Rahmatollah Beheshti},\nAUTHOR+an = {1=student; 3=lead},\n      year={2024},\nbooktitle={Machine Learning for Healthcare 2024},\nnote = {In press},\n addendum = {A top-tier and very selective venue in my field. },\nurl={https://openreview.net/forum?id=DPpA0ggsz6}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n HealthGAT: Node Classifications in Electronic Health Records using Graph Attention Networks.\n \n \n \n \n\n\n \n Piya, F.; Gupta, M.; and Beheshti, R.\n\n\n \n\n\n\n In 2024 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pages 132-141, Los Alamitos, CA, USA, jun 2024. IEEE Computer Society\n \n\n\n\n
\n\n\n\n \n \n \"HealthGAT:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@INPROCEEDINGS {piya2024healthgat,\nauthor = {F. Piya and M. Gupta and R. Beheshti},\n     AUTHOR+an = {1=student; 2=student; 3=lead},\n    addendum = {Acceptance rate: $\\sim$25\\%},\nbooktitle = {2024 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)},\ntitle = {Health{GAT}: Node Classifications in Electronic Health Records using Graph Attention Networks},\nyear = {2024},\nvolume = {},\nissn = {},\npages = {132-141},\ndoi = {10.1109/CHASE60773.2024.00022},\nurl = {https://doi.ieeecomputersociety.org/10.1109/CHASE60773.2024.00022},\npublisher = {IEEE Computer Society},\naddress = {Los Alamitos, CA, USA},\nmonth = {jun}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Graph Transformers on EHRs: Better Representation Improves Downstream Performance.\n \n \n \n \n\n\n \n Poulain, R.; and Beheshti, R.\n\n\n \n\n\n\n In The Twelfth International Conference on Learning Representations (ICLR), 2024. \n \n\n\n\n
\n\n\n\n \n \n \"GraphPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{poulain23ICLR,\ntitle={Graph Transformers on {EHR}s: Better Representation Improves Downstream Performance},\nauthor = {Poulain, Raphael and Beheshti, Rahmatollah},\n    AUTHOR+an = {1=student; 2=lead},\n    addendum = {Acceptance rate: 30\\%. Considerd as \\textbf{top \\#1 or \\#2 conference in AI/ML.}},\nbooktitle={The Twelfth International Conference on Learning Representations (ICLR)},\nyear={2024},\nurl={https://openreview.net/forum?id=pe0Vdv7rsL}\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2023\n \n \n (3)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Bringing At-home Pediatric Sleep Apnea Testing Closer to Reality: A Multi-modal Transformer Approach.\n \n \n \n \n\n\n \n Fayyaz, H.; Strang, A.; and Beheshti, R.\n\n\n \n\n\n\n In Proceedings of the 8th Machine Learning for Healthcare Conference, volume 219, of Proceedings of Machine Learning Research, pages 167–185, 11–12 Aug 2023. PMLR\n \n\n\n\n
\n\n\n\n \n \n \"BringingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{fayyaz23apneaMLHC,\n  title = \t {Bringing At-home Pediatric Sleep Apnea Testing Closer to Reality: A Multi-modal Transformer Approach},\n  author =       {Fayyaz, Hamed and Strang, Abigail and Beheshti, Rahmatollah},\n  booktitle = \t {Proceedings of the 8th Machine Learning for Healthcare Conference},\n  pages = \t {167--185},\n  year = \t {2023},\n  volume = \t {219},\n  series = \t {Proceedings of Machine Learning Research},\n  month = \t {11--12 Aug},\n  publisher =    {PMLR},\n  pdf = \t {https://proceedings.mlr.press/v219/fayyaz23a/fayyaz23a.pdf},\n  url = \t {https://proceedings.mlr.press/v219/fayyaz23a.html},\n AUTHOR+an = {1=student; 3=lead},\n    addendum = {A top-tier and very selective venue in my field. \\textbf{Featured in Sleep Review Magazine}: \\url{sleepreviewmag.com/sleep-diagnostics/connected-care/ai-machine-learning/multimodal-ai-sleep-context}},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods.\n \n \n \n \n\n\n \n Poulain, R.; Bin Tarek, M. F.; and Beheshti, R.\n\n\n \n\n\n\n In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, of FAccT '23, pages 1599–1608, New York, NY, USA, 2023. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"ImprovingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{Poulain2023Improve,\nauthor = {Poulain, Raphael and Bin Tarek, Mirza Farhan and Beheshti, Rahmatollah},\n    AUTHOR+an = {1=student; 2=student; 3=lead},\n    addendum = {Acceptance rate: 25\\%},\ntitle = {Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods},\nyear = {2023},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nurl = {https://doi.org/10.1145/3593013.3594102},\ndoi = {10.1145/3593013.3594102},\nbooktitle = {Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency},\npages = {1599–1608},\nnumpages = {10},\nkeywords = {Adversarial Fairness, Algorithmic Fairness, Federated Learning},\nlocation = {Chicago, IL, USA},\nseries = {FAccT '23}\n}\n\n\n\n
\n
\n\n\n\n
\n\n\n
\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
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@ARTICLE{Mottalib2023subtyping,\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},\naddendum = {Impact factor: 7.7},\n  volume={},\n  number={},\n  pages={1-12},\n  doi={10.1109/JBHI.2023.3237753}}\n\n  \n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2022\n \n \n (7)\n \n \n
\n
\n \n \n
\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 Transactions on Computing for Healthcare (HEALTH), 3(3): Article 32. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"ObesityPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{Gupta2022ObesityElements,\n    title = {{Obesity Prediction with EHR Data: A Deep Learning Approach with Interpretable Elements}},\n    year = {2022},\n    journal = {ACM Transactions on Computing for Healthcare (HEALTH)},\n    author = {Gupta, Mehak and Phan, Thao-Ly T and Bunnell, H Timothy and Beheshti, Rahmatollah},\n                    AUTHOR+an = {1=student;4=lead},\n    addendum = {\\textbf{Cited by 60+}. A recently established journal. },\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
\n
\n\n\n\n
\n\n\n
\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
\n\n\n\n \n \n \"PredictingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@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 = {Acceptance rate: 29\\%}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\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 Proceedings of the 2nd Machine Learning for Health symposium, volume 193, of Proceedings of Machine Learning Research, pages 326–342, 11 2022. PMLR\n \n\n\n\n
\n\n\n\n \n \n \"PredictingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{fayyaz22attrition,\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   AUTHOR+an = {1=student;4=lead},\naddendum = {Acceptance rate: 32\\%},\n  booktitle = \t {Proceedings of the 2nd Machine Learning for Health symposium},\n  pages = \t {326--342},\n  year = \t {2022},\n  volume = \t {193},\n  series = \t {Proceedings of Machine Learning Research},\n  month = \t {11},\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
\n
\n\n\n\n
\n\n\n
\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 Proceedings of the 2nd Machine Learning for Health symposium, volume 193, of Proceedings of Machine Learning Research, pages 311–325, 11 2022. PMLR\n \n\n\n\n
\n\n\n\n \n \n \"AnPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{gupta22mimic,\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                AUTHOR+an = {1=student;2=student;3=student;4=student;5=student;6=lead},\naddendum={Acceptance rate: 32\\%. \\textbf{Cited by 30+}},\n  booktitle = \t {Proceedings of the 2nd Machine Learning for Health symposium},\n  pages = \t {311--325},\n  year = \t {2022},\n  volume = \t {193},\n  series = \t {Proceedings of Machine Learning Research},\n  month = \t {11},\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
\n
\n\n\n\n
\n\n\n
\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
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@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},\naddendum = {\\textbf{Impact factor: 7.8}. A top journal in  long-term care and geriatrics.},\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
\n
\n\n\n\n
\n\n\n
\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
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@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;3=lead},\n    addendum = {A top-tier and very selective venue in my field}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Flexible-window Predictions on Electronic Health Records.\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 AAAI Conference on Artificial Intelligence, volume 36, pages 12510-12516, 2022. AAAI\n \n\n\n\n
\n\n\n\n \n \n \"Flexible-windowPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{Gupta2022Flexible-windowRecords,\n    title = {{Flexible-window Predictions on Electronic Health Records}},\n    year = {2022},\n    booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},\n volume={36}, \nurl={https://ojs.aaai.org/index.php/AAAI/article/view/21520}, \nDOI={10.1609/aaai.v36i11.21520},  number={11}, \n    author = {Gupta, Mehak and Phan, Thao-Ly T and Bunnell, H Timothy and Beheshti, Rahmatollah},\n                AUTHOR+an ={1=student;4=lead},\naddendum = {Acceptance rate: $\\sim$25\\%},\n    publisher = {AAAI},\n    pages={12510-12516}\n}\n\n\n\n\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2021\n \n \n (4)\n \n \n
\n
\n \n \n
\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
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@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=me},\naddendum = {Impact factor: 2.8},\n    number = {1},\n    pages = {1--10},\n    volume = {21},\n    publisher = {BioMed Central}\n}\n\n\n\n
\n
\n\n\n\n
\n\n\n
\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
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@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},\naddendum = {Elsevier's CiteScore: 4.9},\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
\n
\n\n\n\n
\n\n\n
\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
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@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 = {Acceptance rate: 20\\%}\n}\n\n\n\n
\n
\n\n\n\n
\n\n\n
\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
\n\n\n\n \n \n \"ConcurrentPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@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 = {Acceptance rate: 29\\%}\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2019\n \n \n (1)\n \n \n
\n
\n \n \n
\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
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@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 = {Acceptance rate: 27\\%}\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n\n\n\n
\n\n\n \n\n \n \n \n \n\n
\n"}; document.write(bibbase_data.data);