{"id":1277,"date":"2023-05-03T12:01:42","date_gmt":"2023-05-03T12:01:42","guid":{"rendered":"https:\/\/cuezen.com\/?page_id=1277"},"modified":"2024-11-19T08:05:27","modified_gmt":"2024-11-19T08:05:27","slug":"publications","status":"publish","type":"page","link":"https:\/\/qistaging.com\/cuezen\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\t\t
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Where CueZen minds contribute to scientific progress<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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Publication<\/th>\r\n\t\t\tAuthor(s)<\/th>\r\n\t\t<\/tr>\r\n\t<\/thead>\r\n\t
NudgeRank\u2122: Digital Algorithmic Nudging for Personalized Health<\/a><\/td>\r\n\t\t\tJodi Chiam, Aloysius Lim, Ankur Teredesai<\/b>\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Social Public Health Infrastructure for a Smart City Citizen Patient<\/a><\/td>\r\n\t\t\tAnkur Teredesai<\/b>\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Machine Learning for Deferral of Care Prediction<\/a><\/td>\r\n\t\t\tAhmad, M.A.<\/b>, Ahmed, R., Overman, D., Campbell, P., Stroum, C. and Karunakaran, B., 2022. Machine Learning for Deferral of Care Prediction. arXiv preprint arXiv:2207.01485.\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Software as a Medical Device: Regulating AI in Healthcare via Responsible AI<\/a><\/td>\r\n\t\t\tAhmad, M.A.<\/b>, 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).\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Fairness in Machine Learning for Healthcare<\/a><\/td>\r\n\t\t\tAhmad, M.A.<\/b>, Patel, A., Eckert, C., Kumar, V. and Teredesai, A.<\/b>, 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).\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Machine Learning Approaches for Pressure Injury Prediction<\/a><\/td>\r\n\t\t\tAhmad, M.A.<\/b>, Larson, B., Overman, S., Kumar, V., Xie, J., Rossington, A., Patel, A. and Teredesai, A.<\/b>, 2021, August. Machine learning approaches for pressure injury prediction. In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI) (pp. 427-431). IEEE.\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Machine Learning Approaches for Patient State Prediction in Pediatric ICUs<\/a><\/td>\r\n\t\t\tAhmad, M.A.<\/b>, Rivera, E.A.T., Murray, P.M., Carly, E.M., Anita, P.M. and Teredesai, A.<\/b>, 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.\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management<\/a><\/td>\r\n\t\t\tLim, A.<\/b>, Singh, A., Chiam, J., Eckert, C., Kumar, V., Ahmad, M.A. and Teredesai, A.<\/b>, 2021. Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management. arXiv preprint arXiv:2104.07820.\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Assessing Fairness in Classification Parity of Machine Learning Models in Healthcare<\/a><\/td>\r\n\t\t\tYuan, M., Kumar, V., Ahmad, M.A. and Teredesai, A.<\/b>, 2021. Assessing fairness in classification parity of machine learning models in healthcare. arXiv preprint arXiv:2102.03717.\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Survey of Explainable Machine Learning with Visual and Granular Methods beyond Quasi-explanations<\/a><\/td>\r\n\t\t\tKovalerchuk, B., Ahmad, M.A. and Teredesai, A.<\/b>, 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.\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
The Challenge of Imputation in Explainable Artificial Intelligence Models<\/a><\/td>\r\n\t\t\tAhmad, M.A.<\/b>, Eckert, C. and Teredesai, A.<\/b>, 2019. The challenge of imputation in explainable artificial intelligence models. arXiv preprint arXiv:1907.12669.\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Development and Prospective Validation of a Machine Learning-Based Risk of Readmission Model in a Large Military Hospital<\/a><\/td>\r\n\t\t\tEckert, C., Nieves-Robbins, N., Spieker, E., Louwers, T., Hazel, D., Marquardt, J., Solveson, K., Zahid, A., Ahmad, M.<\/b>, 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.\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
S45\u2005Predicting likelihood of emergency department admission prior to triage: utilising machine learning within a COPD cohort<\/a><\/td>\r\n\t\t\tEckert, C., Ahmad, M.<\/b>, 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.\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Interpretable Machine Learning in Healthcare<\/a><\/td>\r\n\t\t\tAhmad, M.A.<\/b>, Eckert, C. and Teredesai, A.<\/b>, 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).\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Death versus Data Science: Predicting End of Life<\/a><\/td>\r\n\t\t\tAhmad, M.<\/b>, Eckert, C., McKelvey, G., Zolfagar, K., Zahid, A. and Teredesai, A.<\/b>, 2018, April. Death vs. data science: predicting end of life. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1).\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Social Public Health Infrastructure for a Smart City Citizen Patient: Advances and Opportunities for AI Driven Disruptive Innovation<\/a><\/td>\r\n\t\t\tTeredesai, A.<\/b>, 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).\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Sub-Sequence Graph Representation Learning on High Variability Data for Dynamic Risk Prediction in Critical Care<\/a><\/td>\r\n\t\t\tTeredesai, A.<\/b>, Huang, S., Stewart, T., Hu, J., Thakker, A., Stern, K. and O\u2019Keefe, 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.\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Smart Personalized Routing For Smart Cities<\/a><\/td>\r\n\t\t\tHendawi, A.M., Rustum, A., Ahmadain, A.A., Hazel, D., Teredesai, A.<\/b>, 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.\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Risk Stratification for Hospital Readmission of Heart Failure Patients: A Machine Learning Approach<\/a><\/td>\r\n\t\t\tHon, C.P., Pereira, M., Sushmita, S., Teredesai, A.<\/b> 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).\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Dynamic and Personalized Routing in PreGo<\/a><\/td>\r\n\t\t\tHendawi, A.M., Rustum, A., Ahmadain, A.A., Oliver, D., Hazel, D., Teredesai, A.<\/b> 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.\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Predicting 30-Day Risk and Cost of \u201cAll-Cause\u201d Hospital Readmissions<\/a><\/td>\r\n\t\t\tSushmita, S., Khulbe, G., Hasan, A., Newman, S., Ravindra, P., Roy, S.B., De Cock, M. and Teredesai, A.<\/b>, 2016, March. Predicting 30-day risk and cost of\" all-cause\" hospital readmissions. In Workshops at the thirtieth AAAI conference on artificial intelligence.\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Dynamic Hierarchical Classification for Patient Risk-of-Readmission<\/a><\/td>\r\n\t\t\tBasu Roy, S., Teredesai, A.<\/b>, 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).\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Population Cost Prediction on Public Healthcare Datasets<\/a><\/td>\r\n\t\t\tSushmita, S., Newman, S., Marquardt, J., Ram, P., Prasad, V., Cock, M.D. and Teredesai, A.<\/b>, 2015, May. Population cost prediction on public healthcare datasets. In Proceedings of the 5th international conference on digital health 2015 (pp. 87-94).\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
HealthSCOPE: An Interactive Distributed Data Mining Framework for Scalable Prediction of Healthcare Costs<\/a><\/td>\r\n\t\t\tMarquardt, A., Newman, S., Hattarki, D., Srinivasan, R., Sushmita, S., Ram, P., Prasad, V., Hazel, D., Ramesh, A., De Cock, M. and Teredesai, A.<\/b>, 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.\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Pathway-Finder: An Interactive Recommender System for Supporting Personalized Care Pathways<\/a><\/td>\r\n\t\t\tLiu, 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.\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
A Framework to Recommend Interventions for 30-Day Heart Failure Readmission Risk<\/a><\/td>\r\n\t\t\tLiu, R., Zolfaghar, K., Chin, S.C., Roy, S.B. and Teredesai, A.<\/b>, 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.\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Dietary Intake Assessment using Integrated Sensors and Software<\/a><\/td>\r\n\t\t\tShang, J., Pepin, E., Johnson, E., Hazel, D., Teredesai, A.<\/b>, 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.\r\n<\/td>\r\n\t\t<\/tr>\r\n\t\t
Discovering Meaningful Cut-points to Predict High HbA1c Variation<\/a><\/td>\r\n\t\t\tChin, S.C., Street, W.N. and Teredesai, A.<\/b>, 2012. Discovering meaningful cut-points to predict high HbA1c variation. Delta, 7, pp.8-0.\r\n<\/td>\r\n\t\t<\/tr>\r\n\t<\/tbody>\r\n<\/table>\r\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Where CueZen minds contribute to scientific progress Publication Author(s) NudgeRank\u2122: 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. 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