research-article

Obesity Prediction with EHR Data: A Deep Learning Approach with Interpretable Elements

Authors:

Thao-Ly T. Phan,

H. Timothy Bunnell,

Rahmatollah BeheshtiAuthors Info & Claims

ACM Transactions on Computing for Healthcare (HEALTH), Volume 3, Issue 3

Article No.: 32, Pages 1 - 19

https://doi.org/10.1145/3506719

Published: 07 April 2022 Publication History

Abstract

Childhood obesity is a major public health challenge. Early prediction and identification of the children at an elevated risk of developing childhood obesity may help in engaging earlier and more effective interventions to prevent and manage obesity. 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’s 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’s 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.

Supplementary Material

3506719.supp (3506719.supp.pdf)

Supplementary material

Download
1.75 MB

References

[1]

Hesham Saleh Al-Sallami, Ailsa Goulding, Andrea Grant, Rachael Taylor, Nicholas Holford, and Stephen Brent Duffull. 2015. Prediction of fat-free mass in children. Clinical Pharmacokinetics 54, 11 (2015), 1169–1178.

[2]

A. Amaddeo, L. De Sanctis, J. P. Giordanella, P. J. Monteyrol, B. Fauroux, et al. 2016. Obesity and obstructive sleep apnea in children. Archives de Pediatrie: Organe Officiel de la Societe Francaise de Pediatrie 24 (2016), S34–S38.

[3]

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).

[4]

Giorgio Bedogni, A.B. Tsybakov, and S. Berlin. 2009. Clinical prediction models-a practical approach to development, validation and updating. Development 18, 500 (2009), 53–99.

[5]

Yoshua Bengio, Ian Goodfellow, and Aaron Courville. 2017. Deep Learning, vol. 1. MIT press Massachusetts, USA.

[6]

Sara N. Bleich, Kelsey A. Vercammen, Laura Y. Zatz, Johannah M. Frelier, Cara B. Ebbeling, and Anna Peeters. 2018. Interventions to prevent global childhood overweight and obesity: A systematic review. Lancet Diabetes & Endocrinology 6, 4 (2018), 332–346.

[7]

Edward Choi, Mohammad Taha Bahadori, Joshua A. Kulas, Andy Schuetz, Walter F. Stewart, and Jimeng Sun. 2016. RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism. In Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS’16). Curran Associates Inc., Red Hook, NY, 3512–3520.

[8]

Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F. Stewart, and Jimeng Sun. 2016. Doctor AI: Predicting clinical events via recurrent neural networks. In Machine Learning for Healthcare Conference. PMLR, 301–318.

[9]

Edward Choi, Mohammad Taha Bahadori, Elizabeth Searles, Catherine Coffey, Michael Thompson, James Bost, Javier Tejedor-Sojo, and Jimeng Sun. 2016. Multi-layer representation learning for medical concepts. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1495–1504.

Digital Library

[10]

Edward Choi, Andy Schuetz, Walter F. Stewart, and Jimeng Sun. 2016. Medical concept representation learning from electronic health records and its application on heart failure prediction. arXiv preprint arXiv:1602.03686 (2016).

[11]

Edward Choi, Andy Schuetz, Walter F. Stewart, and Jimeng Sun. 2017. Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association 24, 2 (2017), 361–370.

[12]

Gonzalo Colmenarejo. 2020. Machine learning models to predict childhood and adolescent obesity: A review. Nutrients 12, 8 (2020), 2466.

[13]

William H. Dietz. 1998. Health consequences of obesity in youth: Childhood predictors of adult disease. Pediatrics 101, Supplement 2 (1998), 518–525.

[14]

Céline Druet, Nicolas Stettler, Stephen Sharp, Rebecca K. Simmons, Cyrus Cooper, George Davey Smith, Ulf Ekelund, Claire Lévy-Marchal, Marjo-Ritta Jarvelin, Diana Kuh, et al. 2012. Prediction of childhood obesity by infancy weight gain: An individual-level meta-analysis. Paediatric and Perinatal Epidemiology 26, 1 (2012), 19–26.

[15]

Kathryn C. Eckstein, Laura M. Mikhail, Adolfo J. Ariza, J. Scott Thomson, Scott C. Millard, Helen J. Binns, et al. 2006. Parents’ perceptions of their child’s weight and health. Pediatrics 117, 3 (2006), 681–690.

[16]

Damodar Reddy Edla, Diwakar Tripathi, Ramalingaswamy Cheruku, and Venkatanareshbabu Kuppili. 2018. An efficient multi-layer ensemble framework with BPSOGSA-based feature selection for credit scoring data analysis. Arabian Journal for Science and Engineering 43, 12 (2018), 6909–6928.

[17]

Tycho Foerste, Matthew Sabin, S. Reid, and D. Reddihough. 2016. Understanding the causes of obesity in children with trisomy 21: Hyperphagia vs physical inactivity. Journal of Intellectual Disability Research 60, 9 (2016), 856–864.

[18]

Centers for Disease Control and Prevention. 2021. Childhood obesity facts. https://www.cdc.gov/obesity/data/childhood.html.

[19]

Lise Graversen, Thorkild I.A. Sørensen, Thomas A. Gerds, Liselotte Petersen, Ulla Sovio, Marika Kaakinen, Annelli Sandbaek, Jaana Laitinen, Anja Taanila, Anneli Pouta, et al. 2015. Prediction of adolescent and adult adiposity outcomes from early life anthropometrics. Obesity 23, 1 (2015), 162–169.

[20]

Mehak Gupta and Rahmatollah Beheshti. 2020. Time-series Imputation and Prediction with Bi-Directional Generative Adversarial Networks. arxiv:2009.08900 [cs.LG]

[21]

Mehak Gupta, Thao-Ly T. Phan, H. Timothy Bunnell, and Rahmatollah Beheshti. 2021. Concurrent imputation and prediction on EHR data using bi-directional GANs: Bi-GANs for EHR imputation and prediction. In Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB’21). Association for Computing Machinery, New York, NY, Article 7, 9 pages.

Digital Library

[22]

Mehak Gupta, Thao-Ly T. Phan, Timothy Bunnell, and Rahmatollah Beheshti. 2019. Obesity prediction with EHR data: A deep learning approach with interpretable elements. arXiv preprint arXiv:1912.02655 (2019).

[23]

Robert Hammond, Rodoniki Athanasiadou, Silvia Curado, Yindalon Aphinyanaphongs, Courtney Abrams, Mary Jo Messito, Rachel Gross, Michelle Katzow, Melanie Jay, Narges Razavian, et al. 2019. Predicting childhood obesity using electronic health records and publicly available data. PloS One 14, 4 (2019), e0215571.

[24]

Jacqueline T. Hecht, Opal Jean Hood, Robert J. Schwartz, Jill C. Hennessey, Barbara A. Bernhardt, William A. Horton, John M. Opitz, and James F. Reynolds. 1988. Obesity in achondroplasia. American Journal of Medical Genetics 31, 3 (1988), 597–602.

[25]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735–1780.

Digital Library

[26]

Mohammed T. Hudda, Mary S. Fewtrell, Dalia Haroun, Sooky Lum, Jane E. Williams, Jonathan C.K. Wells, Richard D. Riley, Christopher G. Owen, Derek G. Cook, Alicja R. Rudnicka, et al. 2019. Development and validation of a prediction model for fat mass in children and adolescents: Meta-analysis using individual participant data. BMJ 366 (2019).

[27]

Zhuochen Jin, Shuyuan Cui, Shunan Guo, David Gotz, Jimeng Sun, and Nan Cao. 2020. Carepre: An intelligent clinical decision assistance system. ACM Transactions on Computing for Healthcare 1, 1 Article 6 (mar 2020), 20 pages.

Digital Library

[28]

Aaron S. Kelly, Sarah E. Barlow, Goutham Rao, Thomas H. Inge, Laura L. Hayman, Julia Steinberger, Elaine M. Urbina, Linda J. Ewing, and Stephen R. Daniels. 2013. Severe obesity in children and adolescents: Identification, associated health risks, and treatment approaches: A scientific statement from the American Heart Association. Circulation 128, 15 (2013), 1689–1712.

[29]

Josua Krause, Adam Perer, and Kenney Ng. 2016. Interacting with predictions: Visual inspection of black-box machine learning models. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 5686–5697.

Digital Library

[30]

Venkatanareshbabu Kuppili, Diwakar Tripathi, and Damodar Reddy Edla. 2020. Credit score classification using spiking extreme learning machine. Computational Intelligence 36, 2 (2020), 402–426.

[31]

Anil K. Lalwani, Karin Katz, Ying-Hua Liu, Sarah Kim, and Michael Weitzman. 2013. Obesity is associated with sensorineural hearing loss in adolescents. The Laryngoscope 123, 12 (2013), 3178–3184.

[32]

R. S. Levine, D. L. Dahly, and M. C. J. Rudolf. 2012. Identifying infants at risk of becoming obese: Can we and should we? Public Health 126, 2 (2012), 123–128.

[33]

Znaonui Liang, Gang Zhang, Jimmy Xiangji Huang, and Qmming Vivian Hu. 2014. Deep learning for healthcare decision making with EMRs. In 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM’14). IEEE, 556–559.

[34]

Todd Lingren, Vidhu Thaker, Cassandra Brady, Bahram Namjou, Stephanie Kennebeck, Jonathan Bickel, Nandan Patibandla, Yizhao Ni, Sara L. Van Driest, Lixin Chen, et al. 2016. Developing an algorithm to detect early childhood obesity in two tertiary pediatric medical centers. Applied Clinical Informatics 7, 3 (2016), 693–706.

[35]

Zachary C. Lipton, David Kale, and Randall Wetzel. 2016. Directly modeling missing data in sequences with RNNs: Improved classification of clinical time series. In Machine Learning for Healthcare Conference. PMLR, 253–270.

[36]

Roderick J. A. Little and Donald B. Rubin. 2019. Statistical Analysis with Missing Data. Vol. 793. John Wiley & Sons.

[37]

Yannis Manios, Manolis Birbilis, George Moschonis, George Birbilis, Vassilis Mougios, Christos Lionis, George P. Chrousos, et al. 2013. Childhood obesity risk evaluation based on perinatal factors and family sociodemographic characteristics: CORE index. European Journal of Pediatrics 172, 4 (2013), 551–555.

[38]

Yannis Manios, Elpis Vlachopapadopoulou, George Moschonis, Feneli Karachaliou, Theodora Psaltopoulou, Dimitra Koutsouki, Gregory Bogdanis, Vilelmine Carayanni, Angelos Hatzakis, and Stefanos Michalacos. 2016. Utility and applicability of the “Childhood Obesity Risk Evaluation” (CORE)-index in predicting obesity in childhood and adolescence in Greece from early life: The “National Action Plan for Public Health.” European Journal of Pediatrics 175, 12 (2016), 1989–1996.

[39]

G. Maragatham and Shobana Devi. 2019. LSTM model for prediction of heart failure in big data. Journal of Medical Systems 43, 5 (2019), 1–13.

Digital Library

[40]

Juan F. Masa, Jean-Louis Pépin, Jean-Christian Borel, Babak Mokhlesi, Patrick B. Murphy, and Maria Ángeles Sánchez-Quiroga. 2019. Obesity hypoventilation syndrome. European Respiratory Review 28, 151 (2019).

[41]

Jing Mei, Shiwan Zhao, Feng Jin, Lingxiao Zhang, Haifeng Liu, Xiang Li, Guotong Xie, and M. Xu. 2017. Deep diabetologist: Learning to prescribe hypoglycemic medications with recurrent neural networks. Studies in Health Technology and Informatics 245 (2017), 1277–1277.

[42]

Babak Mokhlesi. 2010. Obesity hypoventilation syndrome: A state-of-the-art review. Respiratory Care 55, 10 (2010), 1347–1365.

[43]

Paulo Orlando Alves Monteiro and Cesar G. Victora. 2005. Rapid growth in infancy and childhood and obesity in later life–A systematic review. Obesity Reviews 6, 2 (2005), 143–154.

[44]

Anita Morandi, David Meyre, Stéphane Lobbens, Ken Kleinman, Marika Kaakinen, Sheryl L. Rifas-Shiman, Vincent Vatin, Stefan Gaget, Anneli Pouta, Anna-Liisa Hartikainen, et al. 2012. Estimation of newborn risk for child or adolescent obesity: Lessons from longitudinal birth cohorts. PloS One 7, 11 (2012), e49919.

[45]

Centers for Disease Control National Center for Health Statistics and Prevention. 2001. Data Table of BMI-for-age Charts. https://www.cdc.gov/growthcharts/html_charts/bmiagerev.htm.

[46]

Ken K. Ong and Ruth J. F. Loos. 2006. Rapid infancy weight gain and subsequent obesity: Systematic reviews and hopeful suggestions. Acta Paediatrica 95, 8 (2006), 904–908.

[47]

Mei-Chen Ou-Yang, Yao Sun, Melissa Liebowitz, Chih-Cheng Chen, Min-Lin Fang, Weiwei Dai, Tang-Wei Chuang, and Jyu-Lin Chen. 2020. Accelerated weight gain, prematurity, and the risk of childhood obesity: A meta-analysis and systematic review. PloS One 15, 5 (2020), e0232238.

[48]

Sarah Harvey O’Brien, Richard Holubkov, and Evelyn Cohen Reis. 2004. Identification, evaluation, and management of obesity in an academic primary care center. Pediatrics 114, 2 (2004), e154–e159.

[49]

Z. Pei, Claudia Flexeder, E. Fuertes, Elisabeth Thiering, Berthold Koletzko, C. Cramer, Dietrich Berdel, Irina Lehmann, Carl-Peter Bauer, and Joachim Heinrich. 2013. Early life risk factors of being overweight at 10 years of age: Results of the German birth cohorts GINIplus and LISAplus. European Journal of Clinical Nutrition 67, 8 (2013), 855–862.

[50]

Mor Peleg, Samson Tu, Jonathan Bury, Paolo Ciccarese, John Fox, Robert A. Greenes, Richard Hall, Peter D. Johnson, Neill Jones, Anand Kumar, et al. 2003. Comparing computer-interpretable guideline models: A case-study approach. Journal of the American Medical Informatics Association 10, 1 (2003), 52–68.

[51]

Eliana Miller Perrin, Kori B. Flower, and Alice S. Ammerman. 2004. Body mass index charts: Useful yet underused. Journal of Pediatrics 144, 4 (2004), 455–460.

[52]

Trang Pham, Truyen Tran, Dinh Phung, and Svetha Venkatesh. 2016. Deepcare: A deep dynamic memory model for predictive medicine. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 30–41.

Digital Library

[53]

Ramin Ramazi, Christine Perndorfer, Emily Soriano, Jean-Philippe Laurenceau, and Rahmatollah Beheshti. 2019. Multi-modal predictive models of diabetes progression. In Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. 253–258.

Digital Library

[54]

Ramin Ramazi, Christine Perndorfer, Emily C. Soriano, Jean-Philippe Laurenceau, and Rahmatollah Beheshti. 2021. Predicting progression patterns of type 2 diabetes using multi-sensor measurements. Smart Health 21 (2021), 100206.

[55]

Ilkka Rautiainen and Sami Äyrämö. 2022. Predicting overweight and obesity in later life from childhood data: A review of predictive modeling approaches. Computational Sciences and Artificial Intelligence in Industry (2022), 203–220.

[56]

Sarah A. Redsell, Stephen Weng, Judy A. Swift, Dilip Nathan, and Cris Glazebrook. 2016. Validation, optimal threshold determination, and clinical utility of the infant risk of overweight checklist for early prevention of child overweight. Childhood Obesity 12, 3 (2016), 202–209.

[57]

Elizabeth Reifsnider, Alma R. Flores-Vela, Diana Beckman-Mendez, Hoang Nguyen, Colleen Keller, and Shannon Dowdall-Smith. 2006. Perceptions of children’s body sizes among mothers living on the Texas-Mexico border (La Frontera). Public Health Nursing 23, 6 (2006), 488–495.

[58]

Jacob O. Robson, Sofia G. Verstraete, Stephen Shiboski, Melvin B. Heyman, and Janet M. Wojcicki. 2016. A risk score for childhood obesity in an urban latino cohort. Journal of Pediatrics 172 (2016), 29–34.

[59]

Krushnapriya Sahoo, Bishnupriya Sahoo, Ashok Kumar Choudhury, Nighat Yasin Sofi, Raman Kumar, and Ajeet Singh Bhadoria. 2015. Childhood obesity: Causes and consequences. Journal of Family Medicine and Primary Care 4, 2 (2015), 187.

[60]

Celine Saint-Laurent, Laura Garde-Etayo, and Elvire Gouze. 2019. Obesity in achondroplasia patients: from evidence to medical monitoring. Orphanet Journal of Rare Diseases 14, 1 (2019), 1–9.

[61]

Gillian Santorelli, Emily S. Petherick, John Wright, Brad Wilson, Haider Samiei, Noël Cameron, and William Johnson. 2013. Developing prediction equations and a mobile phone application to identify infants at risk of obesity. PLoS One 8, 8 (2013), e71183.

[62]

Benjamin Shickel, Patrick James Tighe, Azra Bihorac, and Parisa Rashidi. 2017. Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE Journal of Biomedical and Health Informatics 22, 5 (2017), 1589–1604.

[63]

Andrew J. Steele, Spiros C. Denaxas, Anoop D. Shah, Harry Hemingway, and Nicholas M. Luscombe. 2018. Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease. PloS One 13, 8 (2018), e0202344.

[64]

Nicolas Stettler, Babette S. Zemel, Shiriki Kumanyika, and Virginia A. Stallings. 2002. Infant weight gain and childhood overweight status in a multicenter, cohort study. Pediatrics 109, 2 (2002), 194–199.

[65]

Marinka Steur, Henriette A. Smit, C. Maarten A. Schipper, Salome Scholtens, Marjan Kerkhof, Johan C. De Jongste, Annemien Haveman-Nies, Bert Brunekreef, and Alet H. Wijga. 2011. Predicting the risk of newborn children to become overweight later in childhood: The PIAMA birth cohort study. International Journal of Pediatric Obesity 6, 2 Part 2 (2011), e170–e178.

[66]

A. Pediatric Learning Health System. 2021. PEDSnet: A Pedistric Learning Health System. https://pedsnet.org.

[67]

A. Pediatric Learning Health System. 2021. PEDSnet Common Data Model. https://pedsnet.org/data/common-data-model.

[68]

Diwakar Tripathi, Damodar Reddy Edla, and Ramalingaswamy Cheruku. 2018. Hybrid credit scoring model using neighborhood rough set and multi-layer ensemble classification. Journal of Intelligent & Fuzzy Systems 34, 3 (2018), 1543–1549.

Digital Library

[69]

Diwakar Tripathi, Damodar Reddy Edla, Ramalingaswamy Cheruku, and Venkatanareshbabu Kuppili. 2019. A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification. Computational Intelligence 35, 2 (2019), 371–394.

[70]

Jelena Vekic, Aleksandra Zeljkovic, Aleksandra Stefanovic, Zorana Jelic-Ivanovic, and Vesna Spasojevic-Kalimanovska. 2019. Obesity and dyslipidemia. Metabolism 92 (2019), 71–81.

[71]

Jennifer Vuong, Yuelin Qiu, Myanh La, Gwen Clarke, Dorine W. Swinkels, and George Cembrowski. 2014. Reference intervals of complete blood count constituents are highly correlated to waist circumference: Should obese patients have their own “normal values”? American Journal of Hematology 89, 7 (2014), 671–677.

[72]

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–e421.

[73]

Nilmini Wickramasinghe. 2017. Deepr: A convolutional net for medical records. (2017).

[74]

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), 847–862.

[75]

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 ’19). IEEE, 1–2.

[76]

Matthew D. Zeiler. 2012. ADADELTA: An adaptive learning rate method. ArXiv abs/1212.5701 (2012).

[77]

Jinghe Zhang, Kamran Kowsari, James H. Harrison, Jennifer M. Lobo, and Laura E. Barnes. 2018. Patient2vec: A personalized interpretable deep representation of the longitudinal electronic health record. IEEE Access 6 (2018), 65333–65346.

[78]

Zeyu Zheng and Karen Ruggiero. 2017. Using machine learning to predict obesity in high school students. In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM’17). IEEE, 2132–2138.

[79]

Nida Ziauddeen, Paul J. Roderick, Nicholas S. Macklon, and Nisreen A. Alwan. 2018. Predicting childhood overweight and obesity using maternal and early life risk factors: A systematic review. Obesity Reviews 19, 3 (2018), 302–312.

Cited By

Yağmur N(2024)A hybrid approach to obesity level determination with decision tree and pelican optimization algorithmJournal of Scientific Reports-A10.59313/jsr-a.1447814(97-109)Online publication date: 30-Jun-2024
https://doi.org/10.59313/jsr-a.1447814
Ayub HKhan MShehryar Ali Naqvi SFaseeh MKim JMehmood AKim Y(2024)Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable CitiesBioengineering10.3390/bioengineering1106053311:6(533)Online publication date: 23-May-2024
https://doi.org/10.3390/bioengineering11060533
Alanazi SAbdollahian MTafakori LAlmulaihan kALruwili SALenazi O(2024)Predicting age at onset of childhood obesity using regression, Random Forest, Decision Tree, and K-Nearest Neighbour—A case study in Saudi ArabiaPLOS ONE10.1371/journal.pone.030840819:9(e0308408)Online publication date: 26-Sep-2024
https://doi.org/10.1371/journal.pone.0308408
Show More Cited By

Index Terms

Obesity Prediction with EHR Data: A Deep Learning Approach with Interpretable Elements
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

Recommendations

Predicting opioid overdose risk of patients with opioid prescriptions using electronic health records based on temporal deep learning
Graphical abstract

Display Omitted
Highlights
- We train a model to predict opioid overdose in the future based on past electronic health records.
- The model is neural network based and requires minimal clinical knowledge.
- Sequential deep learning model improves the prediction of ...
Abstract
The US is experiencing an opioid epidemic, and opioid overdose is causing more than 100 deaths per day. Early identification of patients at high risk of Opioid Overdose (OD) can help to make targeted preventative interventions. We aim to build a ...
Applying interpretable deep learning models to identify chronic cough patients using EHR data
Highlights
- Evaluated language-based data representation with different learning models to predict chronic cough patients using EHR data.
- The best approach can gain 0.952 and 0.930 sensitivity and specificity, respectively.
- Identify chronic ...
Abstract Background and Objective
Chronic cough (CC) affects approximately 10% of adults. Many disease states are associated with chronic cough, such as asthma, upper airway cough syndrome, bronchitis, and gastroesophageal reflux disease. The lack of an ...
An integrated LSTM-HeteroRGNN model for interpretable opioid overdose risk prediction
Abstract
Opioid overdose (OD) has become a leading cause of accidental death in the United States, and overdose deaths reached a record high during the COVID-19 pandemic. Combating the opioid crisis requires targeting high-need populations by identifying ...
Highlights
- A multi-stage deep learning model for opioid overdose risk prediction was developed.
- The model is based on LSTM and Heterogeneous Relational Graph Neural Network.
- The combined model can learn the temporal progression of disease ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Computing for Healthcare

ACM Transactions on Computing for Healthcare Volume 3, Issue 3

July 2022

251 pages

EISSN:2637-8051

DOI:10.1145/3514183

Editors:
Insup Lee
University of Pennsylvania, USA
,
John A. Stankovic
University of Virginia, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2022

Accepted: 01 December 2021

Revised: 01 November 2021

Received: 01 January 2020

Published in HEALTH Volume 3, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

27
Total Citations
View Citations
1,468
Total Downloads

Downloads (Last 12 months)535
Downloads (Last 6 weeks)62

Reflects downloads up to 16 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Yağmur N(2024)A hybrid approach to obesity level determination with decision tree and pelican optimization algorithmJournal of Scientific Reports-A10.59313/jsr-a.1447814(97-109)Online publication date: 30-Jun-2024
https://doi.org/10.59313/jsr-a.1447814
Ayub HKhan MShehryar Ali Naqvi SFaseeh MKim JMehmood AKim Y(2024)Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable CitiesBioengineering10.3390/bioengineering1106053311:6(533)Online publication date: 23-May-2024
https://doi.org/10.3390/bioengineering11060533
Alanazi SAbdollahian MTafakori LAlmulaihan kALruwili SALenazi O(2024)Predicting age at onset of childhood obesity using regression, Random Forest, Decision Tree, and K-Nearest Neighbour—A case study in Saudi ArabiaPLOS ONE10.1371/journal.pone.030840819:9(e0308408)Online publication date: 26-Sep-2024
https://doi.org/10.1371/journal.pone.0308408
Liu HWu YChau PChung TFong D(2024)Prediction of adolescent weight status by machine learning: a population-based studyBMC Public Health10.1186/s12889-024-18830-124:1Online publication date: 20-May-2024
https://doi.org/10.1186/s12889-024-18830-1
Tawfik HSingh BKhater T(2024)Interpretable RNN for Prediction & Understanding of Childhood Obesity: A Scenario from the UK Millennium Cohort StudyProceedings of the 2024 8th International Conference on Cloud and Big Data Computing10.1145/3694860.3694867(49-53)Online publication date: 15-Aug-2024
https://dl.acm.org/doi/10.1145/3694860.3694867
Jeong JLee IKim SKam TLee SLee E(2024)DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning FrameworkIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2024.335658028:4(2282-2293)Online publication date: Apr-2024
https://doi.org/10.1109/JBHI.2024.3356580
Liu GShen PMa ZHan MLu N(2024)STAIN: Spatial-Temporal Adversarial Network for Multivariate Time Series Imputation2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA)10.1109/ICIEA61579.2024.10664886(1-6)Online publication date: 5-Aug-2024
https://doi.org/10.1109/ICIEA61579.2024.10664886
Gou HSong HTian ZLiu Y(2024)Prediction models for children/adolescents with obesity/overweight: A systematic review and meta-analysisPreventive Medicine10.1016/j.ypmed.2023.107823179(107823)Online publication date: Feb-2024
https://doi.org/10.1016/j.ypmed.2023.107823
Gupta MEckrich DBunnell HPhan TBeheshti R(2024)Reliable prediction of childhood obesity using only routinely collected EHRs may be possibleObesity Pillars10.1016/j.obpill.2024.10012812(100128)Online publication date: Dec-2024
https://doi.org/10.1016/j.obpill.2024.100128
Khater TTawfik HSingh B(2024)Explainable Artificial Intelligence for Investigating the Effect of Lifestyle Factors on ObesityIntelligent Systems with Applications10.1016/j.iswa.2024.200427(200427)Online publication date: Aug-2024
https://doi.org/10.1016/j.iswa.2024.200427
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents