{"type":"ScholarlyArticle","title":"A Novel Spatio-Temporal Multi-Task Approach for the Prediction of Diabetes-Related Complication: a Cardiopathy Case of Study","authors":[{"name":"Luca Romeo","slug":"luca-romeo","given":"Luca","family":"Romeo","affiliation":"Department of Information Engineering , Università Politecnica delle Marche , Ancona , Italy"},{"name":"Giuseppe Armentano","slug":"giuseppe-armentano","given":"Giuseppe","family":"Armentano","affiliation":"Diabetological Center DEA"},{"name":"Antonio Nicolucci","slug":"antonio-nicolucci","given":"Antonio","family":"Nicolucci","affiliation":"Center for Outcomes Research and Clinical Epidemiology"},{"name":"Marco Vespasiani","slug":"marco-vespasiani","given":"Marco","family":"Vespasiani","affiliation":"METEDA"},{"name":"Giacomo Vespasiani","slug":"giacomo-vespasiani","given":"Giacomo","family":"Vespasiani","affiliation":"METEDA"},{"name":"Emanuele Frontoni","slug":"emanuele-frontoni","given":"Emanuele","family":"Frontoni","affiliation":"Department of Information Engineering , Università Politecnica delle Marche , Ancona , Italy"}],"abstract":"The prediction of the risk profile related to the cardiopathy complication is a core research task that could support clinical decision making. However, the design and implementation of a clinical decision support system based on Electronic Health Record (EHR) temporal data comprise of several challenges. Several single task learning approaches consider the prediction of the risk profile related to a specific diabetes complication (i.e., cardiopathy) independent from other complications. Accordingly, the state-of-the-art multi-task learning (MTL) model encapsulates only the temporal relatedness among the EHR data. However, this assumption might be restricted in the clinical scenario where both spatio-temporal constraints should be taken into account. The aim of this study is the proposal of two different MTL procedures, called spatio-temporal lasso (STL-MTL) and spatio-temporal group lasso (STGL-MTL), which encode the spatio-temporal relatedness using a regularization term and a graph-based approach (i.e., encoding the task relatedness using the structure matrix). Experimental results on a real-world EHR dataset demonstrate the robust performance and the interpretability of the proposed approach.","doi":"10.24963/ijcai.2020/593","url":"https://ijcai.org/proceedings/2020/593","pdf":"https://ijcai.org/proceedings/2020/0593.pdf","markdown":"https://ijcai.org/proceedings/2020/593.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/593","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Special track on AI for CompSust and Human well-being","track":"Special track on AI for CompSust and Human well-being","number":593,"pages":{"first":4299,"last":4305},"keywords":["Machine Learning: Classification","Machine Learning: Transfer, Adaptation, Multi-task Learning","Machine Learning Applications: Bio/Medicine","Machine Learning Applications: Applications of Supervised Learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24093/file_24093.m3u8","length_sec":257,"provider":"hls"}],"references":[{"position":1,"raw":"[Anderson et al., 2016] Ariana E. Anderson, Wesley T. Kerr, April Thames, Tong Li, Jiayang Xiao, and Mark S. Cohen. Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general united states population: A cross-sectional, unselected, retrospective study. J. Biomed. Inform., 60:162 -168, 2016.","title":"Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general united states population: A cross-sectional, unselected, retrospective study","authors":"Ariana E Anderson, Wesley T Kerr, April Thames, Tong Li, Jiayang Xiao, Mark S Cohen","year":"2016","venue":"J. Biomed. Inform","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Argyriou et al., 2007] Andreas Argyriou, Theodoros Evge- niou, and Massimiliano Pontil. Multi-task feature learn- ing. In Advances in neural information processing sys- tems, pages 41-48, 2007.","title":"Multi-task feature learning","authors":"Andreas Argyriou, Theodoros Evgeniou, Massimiliano Pontil","year":"2007","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bernardini et al., 2019] M. Bernardini, L. Romeo, P. Mis- ericordia, and E. Frontoni. Discovering the type 2 dia- betes in electronic health records using the sparse balanced support vector machine. IEEE Journal of Biomedical and Health Informatics, pages 1-1, 2019.","title":"Discovering the type 2 diabetes in electronic health records using the sparse balanced support vector machine","authors":"M Bernardini, L Romeo, P Misericordia, E Frontoni","year":"2019","venue":"IEEE Journal of Biomedical and Health Informatics","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bickel et al., 2008] Steffen Bickel, Jasmina Bogojeska, Thomas Lengauer, and Tobias Scheffer. Multi-task learn- ing for hiv therapy screening. In Proceedings of the 25th international conference on Machine learning, pages 56- 63. ACM, 2008.","title":"Multi-task learning for hiv therapy screening","authors":"Steffen Bickel, Jasmina Bogojeska, Thomas Lengauer, Tobias Scheffer","year":"2008","venue":"Proceedings of the 25th international conference on Machine learning","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cawley and Talbot, 2010] Gavin C Cawley and Nicola LC Talbot. On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Ma- chine Learning Research, 11(Jul):2079-2107, 2010.","title":"On over-fitting in model selection and subsequent selection bias in performance evaluation","authors":"C Gavin, Nicola Lc Cawley, Talbot","year":"2010","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chawla et al., 2002] Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321-357, 2002.","title":"SMOTE: synthetic minority over-sampling technique","authors":"Kevin W Nitesh V Chawla, Lawrence O Bowyer, Philip Hall, Kegelmeyer","year":"2002","venue":"Journal of Artificial Intelligence Research","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Evgeniou and Pontil, 2004] Theodoros Evgeniou and Mas- similiano Pontil. Regularized multi-task learning. In Pro- ceedings of the tenth ACM SIGKDD international con- ference on Knowledge discovery and data mining, pages 109-117. ACM, 2004.","title":"Theodoros Evgeniou and Massimiliano Pontil. Regularized multi-task learning","authors":"Pontil Evgeniou","year":"2004","venue":"Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[García-Laencina et al., 2010] Pedro J García-Laencina, José-Luis Sancho-Gómez, and Aníbal R Figueiras-Vidal. Pattern classification with missing data: a review. Neural Computing and Applications, 19(2):263-282, 2010.","title":"Pattern classification with missing data: a review","authors":"Pedro J García-Laencina, José-Luis Sancho-Gómez, Aníbal R Figueiras- Vidal","year":"2010","venue":"Neural Computing and Applications","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hall et al., 2016] Adam James Hall, Amir Hussain, and M. Guftar Shaikh. Predicting insulin resistance in chil- dren using a machine-learning-based clinical decision sup- port system. In Cheng-Lin Liu, Amir Hussain, Bin Luo, Kay Chen Tan, Yi Zeng, and Zhaoxiang Zhang, editors, Advances in Brain Inspired Cognitive Systems, pages 274- 283, Cham, 2016. Springer International Publishing.","title":"Predicting insulin resistance in children using a machine-learning-based clinical decision support system","authors":"Adam James, Hall, Amir Hussain, M Guftar Shaikh","year":"2016","venue":"Advances in Brain Inspired Cognitive Systems","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Lipton, 2018] Zachary C. Lipton. The mythos of model in- terpretability. Queue, 16(3):30:31-30:57, June 2018.","title":"The mythos of model interpretability","authors":"Zachary C Lipton","year":"2018","venue":"Queue","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Madley-Dowd et al., 2019] Paul Madley-Dowd, Rachael Hughes, Kate Tilling, and Jon Heron. The proportion of missing data should not be used to guide decisions on mul- tiple imputation. Journal of Clinical Epidemiology, 110:63 -73, 2019.","title":"The proportion of missing data should not be used to guide decisions on multiple imputation","authors":"Madley-Dowd","year":"2019","venue":"Journal of Clinical Epidemiology","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Mani et al., 2012] Subramani Mani, Yukun Chen, Tom Elasy, Warren Clayton, and Joshua Denny. Type 2 diabetes risk forecasting from emr data using machine learning. In AMIA annual symposium proceedings, volume 2012, page 606. American Medical Informatics Association, 2012.","title":"Type 2 diabetes risk forecasting from emr data using machine learning","authors":"Subramani Mani, Yukun Chen, Tom Elasy, Warren Clayton, Joshua Denny","year":"2012","venue":"AMIA annual symposium proceedings","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Miotto et al., 2016] Riccardo Miotto, Li Li, Brian A Kidd, and Joel T Dudley. Deep patient: an unsupervised rep- resentation to predict the future of patients from the elec- tronic health records. Scientific Reports, 6:26094, 2016.","title":"Deep patient: an unsupervised representation to predict the future of patients from the electronic health records","authors":"Riccardo Miotto, Li Li, Brian A Kidd, Joel T Dudley","year":"2016","venue":"Scientific Reports","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Perveen et al., 2016] Sajida Perveen, Muhammad Shahbaz, Aziz Guergachi, and Karim Keshavjee. Performance anal- ysis of data mining classification techniques to predict di- abetes. Procedia Computer Science, 82:115-121, 2016.","title":"Performance analysis of data mining classification techniques to predict diabetes","authors":"Muhammad Sajida Perveen, Aziz Shahbaz, Karim Guergachi, Keshavjee","year":"2016","venue":"Procedia Computer Science","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Pimentel et al., 2018] Angela Pimentel, André V Carreiro, Rogério T Ribeiro, and Hugo Gamboa. Screening diabetes mellitus 2 based on electronic health records using tempo- ral features. Health Informatics Journal, 24(2):194-205, 2018.","title":"Screening diabetes mellitus 2 based on electronic health records using temporal features","authors":"Angela Pimentel, V André, Rogério T Carreiro, Hugo Ribeiro, Gamboa","year":"2018","venue":"Health Informatics Journal","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Qiao et al., 2018] Zhi Qiao, Shiwan Zhao, Cao Xiao, Xi- ang Li, Yong Qin, and Fei Wang. Pairwise-ranking based collaborative recurrent neural networks for clinical event prediction. In Proceedings of the Twenty-Seventh Interna- tional Joint Conference on Artificial Intelligence, 2018.","title":"Pairwise-ranking based collaborative recurrent neural networks for clinical event prediction","authors":"Zhi Qiao, Shiwan Zhao, Cao Xiao, Xiang Li, Yong Qin, Fei Wang","year":"2018","venue":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2018/489"},{"position":17,"raw":"[Singh et al., 2015] Anima Singh, Girish Nadkarni, Omri Gottesman, Stephen B. Ellis, Erwin P. Bottinger, and John V. Guttag. Incorporating temporal EHR data in pre- dictive models for risk stratification of renal function dete- rioration. J. Biomed. Inform., 53:220 -228, 2015.","title":"Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration","authors":"Anima Singh, Girish Nadkarni, Omri Gottesman, Stephen B Ellis, Erwin P Bottinger, John V Guttag","year":"2015","venue":"J. Biomed. Inform","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Steyerberg, 2019] Ewout W Steyerberg. Missing values. In Clinical Prediction Models, pages 127-155. Springer, 2019.","title":"Missing values","authors":"W Ewout, Steyerberg","year":"2019","venue":"Clinical Prediction Models","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Talaei-Khoei and Wilson, 2018] Amir Talaei-Khoei and James M. Wilson. Identifying people at risk of developing type 2 diabetes: A comparison of predictive analytics techniques and predictor variables. International Journal of Medical Informatics, 119:22 -38, 2018.","title":"Identifying people at risk of developing type 2 diabetes: A comparison of predictive analytics techniques and predictor variables","authors":"Talaei-Khoei, Wilson ; Amir Talaei-Khoei, James M Wilson","year":"2018","venue":"International Journal of Medical Informatics","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Tibshirani, 1996] Robert Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1):267-288, 1996.","title":"Regression shrinkage and selection via the lasso","authors":"Robert Tibshirani","year":"1996","venue":"Journal of the Royal Statistical Society: Series B (Methodological)","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Zhao et al., 2019] Jing Zhao, Shaopeng Gu, and Adam Mc- Dermaid. Predicting outcomes of chronic kidney disease from emr data based on random forest regression. Mathe- matical Biosciences, 310:24-30, 2019.","title":"Predicting outcomes of chronic kidney disease from emr data based on random forest regression","authors":"Jing Zhao, Shaopeng Gu, Adam Mc-Dermaid","year":"2019","venue":"Mathematical Biosciences","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Zhou et al., 2011] Jiayu Zhou, Lei Yuan, Jun Liu, and Jieping Ye. A multi-task learning formulation for predict- ing disease progression. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discov- ery and data mining, pages 814-822. ACM, 2011.","title":"A multi-task learning formulation for predicting disease progression","authors":"Jiayu Zhou, Lei Yuan, Jun Liu, Jieping Ye","year":"2011","venue":"Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zhou et al., 2012] Jiayu Zhou, Jun Liu, Vaibhav A Narayan, and Jieping Ye. Modeling disease progression via fused sparse group lasso. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discov- ery and data mining, pages 1095-1103. ACM, 2012.","title":"Modeling disease progression via fused sparse group lasso","authors":"Jiayu Zhou, Jun Liu, Jieping Vaibhav A Narayan, Ye","year":"2012","venue":"Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Zhu et al., 2004] Ji Zhu, Saharon Rosset, Robert Tibshirani, and Trevor J Hastie. 1-norm support vector machines. In Advances in neural information processing systems, pages 49-56, 2004.","title":"1-norm support vector machines","authors":"Ji Zhu, Saharon Rosset, Robert Tibshirani, Trevor J Hastie","year":"2004","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}