Personalization Engine for Health
Personalization Engine for Health
Where CueZen minds contribute to scientific progress
| Publication | Author(s) |
|---|---|
| NudgeRank™: Digital Algorithmic Nudging for Personalized Health | Jodi Chiam, Aloysius Lim, Ankur Teredesai |
| Social Public Health Infrastructure for a Smart City Citizen Patient | Ankur Teredesai |
| Machine Learning for Deferral of Care Prediction | Ahmad, M.A., Ahmed, R., Overman, D., Campbell, P., Stroum, C. and Karunakaran, B., 2022. Machine Learning for Deferral of Care Prediction. arXiv preprint arXiv:2207.01485. |
| Software as a Medical Device: Regulating AI in Healthcare via Responsible AI | Ahmad, M.A., Overman, S., Allen, C., Kumar, V., Teredesai, A. and Eckert, C., 2021, August. Software as a Medical Device: Regulating AI in Healthcare via Responsible AI. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 4023-4024). |
| Fairness in Machine Learning for Healthcare | Ahmad, M.A., Patel, A., Eckert, C., Kumar, V. and Teredesai, A., 2020, August. Fairness in machine learning for healthcare. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3529-3530). |
| Machine Learning Approaches for Pressure Injury Prediction | Ahmad, M.A., Larson, B., Overman, S., Kumar, V., Xie, J., Rossington, A., Patel, A. and Teredesai, A., 2021, August. Machine learning approaches for pressure injury prediction. In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI) (pp. 427-431). IEEE. |
| Machine Learning Approaches for Patient State Prediction in Pediatric ICUs | Ahmad, M.A., Rivera, E.A.T., Murray, P.M., Carly, E.M., Anita, P.M. and Teredesai, A., 2021, August. Machine learning approaches for patient state prediction in pediatric ICUs. In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI) (pp. 422-426). IEEE. |
| Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management | Lim, A., Singh, A., Chiam, J., Eckert, C., Kumar, V., Ahmad, M.A. and Teredesai, A., 2021. Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management. arXiv preprint arXiv:2104.07820. |
| Assessing Fairness in Classification Parity of Machine Learning Models in Healthcare | Yuan, M., Kumar, V., Ahmad, M.A. and Teredesai, A., 2021. Assessing fairness in classification parity of machine learning models in healthcare. arXiv preprint arXiv:2102.03717. |
| Survey of Explainable Machine Learning with Visual and Granular Methods beyond Quasi-explanations | Kovalerchuk, B., Ahmad, M.A. and Teredesai, A., 2021. Survey of explainable machine learning with visual and granular methods beyond quasi-explanations. Interpretable artificial intelligence: A perspective of granular computing, pp.217-267. |
| The Challenge of Imputation in Explainable Artificial Intelligence Models | Ahmad, M.A., Eckert, C. and Teredesai, A., 2019. The challenge of imputation in explainable artificial intelligence models. arXiv preprint arXiv:1907.12669. |
| Development and Prospective Validation of a Machine Learning-Based Risk of Readmission Model in a Large Military Hospital | Eckert, C., Nieves-Robbins, N., Spieker, E., Louwers, T., Hazel, D., Marquardt, J., Solveson, K., Zahid, A., Ahmad, M., Barnhill, R. and McKelvey, T.G., 2019. Development and prospective validation of a machine learning-based risk of readmission model in a large military hospital. Applied clinical informatics, 10(02), pp.316-325. |
| S45 Predicting likelihood of emergency department admission prior to triage: utilising machine learning within a COPD cohort | Eckert, C., Ahmad, M., Zolfaghar, K., McKelvey, G., Carlin, C. and Lowe, D., 2018. S45 predicting likelihood of emergency department admission prior to triage: utilising machine learning within a COPD cohort. |
| Interpretable Machine Learning in Healthcare | Ahmad, M.A., Eckert, C. and Teredesai, A., 2018, August. Interpretable machine learning in healthcare. In Proceedings of the 2018 ACM international conference on bioinformatics, computational biology, and health informatics (pp. 559-560). |
| Death versus Data Science: Predicting End of Life | Ahmad, M., Eckert, C., McKelvey, G., Zolfagar, K., Zahid, A. and Teredesai, A., 2018, April. Death vs. data science: predicting end of life. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1). |
| Social Public Health Infrastructure for a Smart City Citizen Patient: Advances and Opportunities for AI Driven Disruptive Innovation | Teredesai, A., 2023, February. Social Public Health Infrastructure for a Smart City Citizen Patient: Advances and Opportunities for AI Driven Disruptive Innovation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (pp. 1297-1298). |
| Sub-Sequence Graph Representation Learning on High Variability Data for Dynamic Risk Prediction in Critical Care | Teredesai, A., Huang, S., Stewart, T., Hu, J., Thakker, A., Stern, K. and O’Keefe, G.E., 2022, December. Sub-Sequence Graph Representation Learning on High Variability Data for Dynamic Risk Prediction in Critical Care. In 2022 IEEE International Conference on Big Data (Big Data) (pp. 2082-2092). IEEE. |
| Smart Personalized Routing For Smart Cities | Hendawi, A.M., Rustum, A., Ahmadain, A.A., Hazel, D., Teredesai, A., Oliver, D., Ali, M. and Stankovic, J.A., 2017, April. Smart personalized routing for smart cities. In 2017 IEEE 33rd International Conference on Data Engineering (ICDE) (pp. 1295-1306). IEEE. |
| Risk Stratification for Hospital Readmission of Heart Failure Patients: A Machine Learning Approach | Hon, C.P., Pereira, M., Sushmita, S., Teredesai, A. and De Cock, M., 2016, October. Risk stratification for hospital readmission of heart failure patients: A machine learning approach. In Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 491-492). |
| Dynamic and Personalized Routing in PreGo | Hendawi, A.M., Rustum, A., Ahmadain, A.A., Oliver, D., Hazel, D., Teredesai, A. and Ali, M., 2016, June. Dynamic and personalized routing in prego. In 2016 17th IEEE International Conference on Mobile Data Management (MDM) (Vol. 1, pp. 357-360). IEEE. |
| Predicting 30-Day Risk and Cost of “All-Cause” Hospital Readmissions | Sushmita, S., Khulbe, G., Hasan, A., Newman, S., Ravindra, P., Roy, S.B., De Cock, M. and Teredesai, A., 2016, March. Predicting 30-day risk and cost of" all-cause" hospital readmissions. In Workshops at the thirtieth AAAI conference on artificial intelligence. |
| Dynamic Hierarchical Classification for Patient Risk-of-Readmission | Basu Roy, S., Teredesai, A., Zolfaghar, K., Liu, R., Hazel, D., Newman, S. and Marinez, A., 2015, August. Dynamic hierarchical classification for patient risk-of-readmission. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1691-1700). |
| Population Cost Prediction on Public Healthcare Datasets | Sushmita, S., Newman, S., Marquardt, J., Ram, P., Prasad, V., Cock, M.D. and Teredesai, A., 2015, May. Population cost prediction on public healthcare datasets. In Proceedings of the 5th international conference on digital health 2015 (pp. 87-94). |
| HealthSCOPE: An Interactive Distributed Data Mining Framework for Scalable Prediction of Healthcare Costs | Marquardt, A., Newman, S., Hattarki, D., Srinivasan, R., Sushmita, S., Ram, P., Prasad, V., Hazel, D., Ramesh, A., De Cock, M. and Teredesai, A., 2014, December. Healthscope: An interactive distributed data mining framework for scalable prediction of healthcare costs. In 2014 IEEE International Conference on Data Mining Workshop (pp. 1227-1230). IEEE. |
| Pathway-Finder: An Interactive Recommender System for Supporting Personalized Care Pathways | Liu, R., Srinivasan, R.V., Zolfaghar, K., Chin, S.C., Roy, S.B., Hasan, A. and Hazel, D., 2014, December. Pathway-finder: An interactive recommender system for supporting personalized care pathways. In 2014 IEEE International Conference on Data Mining Workshop (pp. 1219-1222). IEEE. |
| A Framework to Recommend Interventions for 30-Day Heart Failure Readmission Risk | Liu, R., Zolfaghar, K., Chin, S.C., Roy, S.B. and Teredesai, A., 2014, December. A framework to recommend interventions for 30-day heart failure readmission risk. In 2014 IEEE International Conference on Data Mining (pp. 911-916). IEEE. |
| Dietary Intake Assessment using Integrated Sensors and Software | Shang, J., Pepin, E., Johnson, E., Hazel, D., Teredesai, A., Kristal, A. and Mamishev, A., 2012, February. Dietary intake assessment using integrated sensors and software. In Multimedia on Mobile Devices 2012; and Multimedia Content Access: Algorithms and Systems VI (Vol. 8304, pp. 14-24). SPIE. |
| Discovering Meaningful Cut-points to Predict High HbA1c Variation | Chin, S.C., Street, W.N. and Teredesai, A., 2012. Discovering meaningful cut-points to predict high HbA1c variation. Delta, 7, pp.8-0. |