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Most existing predictive tools for childhood obesity primarily rely on traditional regression-type methods using only a few hand-picked features and without exploiting longitudinal patterns of children\u2019s data. Deep learning methods allow the use of high-dimensional longitudinal datasets. In this article, we present a deep learning model designed for predicting future obesity patterns from generally available items on children\u2019s medical history. To do this, we use a large unaugmented electronic health records dataset from a large pediatric health system in the United States. We adopt a general LSTM network architecture and train our proposed model using both static and dynamic EHR data. To add interpretability, we have additionally included an attention layer to calculate the attention scores for the timestamps and rank features of each timestamp. Our model is used to predict obesity for ages between 3 and 20 years using the data from 1 to 3 years in advance. We compare the performance of our LSTM model with a series of existing studies in the literature and show it outperforms their performance in most age ranges.<\/jats:p>","DOI":"10.1145\/3506719","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T12:44:47Z","timestamp":1649335487000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":76,"title":["Obesity Prediction with EHR Data: A Deep Learning Approach with Interpretable Elements"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8036-2025","authenticated-orcid":false,"given":"Mehak","family":"Gupta","sequence":"first","affiliation":[{"name":"University of Delaware, Newark, Delaware, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thao-Ly T.","family":"Phan","sequence":"additional","affiliation":[{"name":"Nemours Children\u2019s Health, Wilmington, Delaware, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H. 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Reference intervals of complete blood count constituents are highly correlated to waist circumference: Should obese patients have their own \u201cnormal values\u201d? American Journal of Hematology 89, 7 (2014), 671\u2013677.","journal-title":"American Journal of Hematology"},{"issue":"2","key":"e_1_3_2_73_2","first-page":"e414\u2013e421","article-title":"Estimating overweight risk in childhood from predictors during infancy","volume":"132","author":"Weng Stephen F.","year":"2013","unstructured":"Stephen F. Weng, Sarah A. Redsell, Dilip Nathan, Judy A. Swift, Min Yang, and Cris Glazebrook. 2013. Estimating overweight risk in childhood from predictors during infancy. Pediatrics 132, 2 (2013), e414\u2013e421.","journal-title":"Pediatrics"},{"key":"e_1_3_2_74_2","unstructured":"Nilmini Wickramasinghe. 2017. Deepr: A convolutional net for medical records. (2017)."},{"issue":"5","key":"e_1_3_2_75_2","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1681\/ASN.2007020245","article-title":"Vesicoureteral reflux","volume":"19","author":"Williams Gabrielle","year":"2008","unstructured":"Gabrielle Williams, Jeffery T. Fletcher, Stephen I. Alexander, and Jonathan C. Craig. 2008. Vesicoureteral reflux. Journal of the American Society of Nephrology 19, 5 (2008), 847\u2013862.","journal-title":"Journal of the American Society of Nephrology"},{"key":"e_1_3_2_76_2","first-page":"1","volume-title":"2019 IEEE International Conference on Healthcare Informatics (ICHI \u201919)","author":"Xu Enliang","year":"2019","unstructured":"Enliang Xu, Shiwan Zhao, Jing Mei, Eryu Xia, Yiqin Yu, and Songfang Huang. 2019. Multiple MACE risk prediction using multi-task recurrent neural network with attention. In 2019 IEEE International Conference on Healthcare Informatics (ICHI \u201919). 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