{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:49:19Z","timestamp":1760147359621,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T00:00:00Z","timestamp":1674691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1UL1TR001876-01","1RF1AG069121-01","I01 HX001145-02"],"award-info":[{"award-number":["1UL1TR001876-01","1RF1AG069121-01","I01 HX001145-02"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000738","name":"U.S. Department of Veterans Affairs","doi-asserted-by":"publisher","award":["1UL1TR001876-01","1RF1AG069121-01","I01 HX001145-02"],"award-info":[{"award-number":["1UL1TR001876-01","1RF1AG069121-01","I01 HX001145-02"]}],"id":[{"id":"10.13039\/100000738","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>Deep neural network (DNN) is a powerful technology that is being utilized by a growing number and range of research projects, including disease risk prediction models. One of the key strengths of DNN is its ability to model non-linear relationships, which include covariate interactions. We developed a novel method called interaction scores for measuring the covariate interactions captured by DNN models. As the method is model-agnostic, it can also be applied to other types of machine learning models. It is designed to be a generalization of the coefficient of the interaction term in a logistic regression; hence, its values are easily interpretable. The interaction score can be calculated at both an individual level and population level. The individual-level score provides an individualized explanation for covariate interactions. We applied this method to two simulated datasets and a real-world clinical dataset on Alzheimer\u2019s disease and related dementia (ADRD). We also applied two existing interaction measurement methods to those datasets for comparison. The results on the simulated datasets showed that the interaction score method can explain the underlying interaction effects, there are strong correlations between the population-level interaction scores and the ground truth values, and the individual-level interaction scores vary when the interaction was designed to be non-uniform. Another validation of our new method is that the interactions discovered from the ADRD data included both known and novel relationships.<\/jats:p>","DOI":"10.3390\/jpm13020217","type":"journal-article","created":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T01:27:58Z","timestamp":1674782878000},"page":"217","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Enhancing Clinical Data Analysis by Explaining Interaction Effects between Covariates in Deep Neural Network Models"],"prefix":"10.3390","volume":"13","author":[{"given":"Yijun","family":"Shao","sequence":"first","affiliation":[{"name":"Department of Clinical Research and Leadership, School of Medicine and Health Sciences, George Washington University, Washington, DC 20037, USA"},{"name":"Washington DC VA Medical Center, Washington, DC 20422, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6832-6424","authenticated-orcid":false,"given":"Ali","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Clinical Research and Leadership, School of Medicine and Health Sciences, George Washington University, Washington, DC 20037, USA"},{"name":"Washington DC VA Medical Center, Washington, DC 20422, USA"},{"name":"Department of Medicine, School of Medicine, Georgetown University, Washington, DC 20057, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edward Y.","family":"Zamrini","sequence":"additional","affiliation":[{"name":"Department of Clinical Research and Leadership, School of Medicine and Health Sciences, George Washington University, Washington, DC 20037, USA"},{"name":"Washington DC VA Medical Center, Washington, DC 20422, USA"},{"name":"Department of Neurology, School of Medicine, University of Utah, Salt Lake City, UT 84108, USA"},{"name":"Irvine Clinical Research, Irvine, CA 92614, USA"},{"name":"Cognitive Neurology Consulting, Newport Beach, CA 92614, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7189-3991","authenticated-orcid":false,"given":"Yan","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Clinical Research and Leadership, School of Medicine and Health Sciences, George Washington University, Washington, DC 20037, USA"},{"name":"Washington DC VA Medical Center, Washington, DC 20422, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph L.","family":"Goulet","sequence":"additional","affiliation":[{"name":"VA Connecticut Healthcare System, New Haven, CT 06516, USA"},{"name":"Department of Emergency Medicine, Yale School of Medicine, Yale University, New Haven, CT 06516, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8353-7473","authenticated-orcid":false,"given":"Qing","family":"Zeng-Treitler","sequence":"additional","affiliation":[{"name":"Department of Clinical Research and Leadership, School of Medicine and Health Sciences, George Washington University, Washington, DC 20037, USA"},{"name":"Washington DC VA Medical Center, Washington, DC 20422, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,26]]},"reference":[{"key":"ref_1","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the NIPS\u201912 25th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2410","DOI":"10.1109\/TASLP.2017.2756440","article-title":"Toward Human Parity in Conversational Speech Recognition","volume":"25","author":"Xiong","year":"2017","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of Go with deep neural networks and tree search","volume":"529","author":"Silver","year":"2016","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"570","DOI":"10.3348\/kjr.2017.18.4.570","article-title":"Deep Learning in Medical Imaging: General Overview","volume":"18","author":"Lee","year":"2017","journal-title":"Korean J. Radiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1093\/jamia\/ocy068","article-title":"Opportunities and challenges in developing deep learning models using electronic health records data: A systematic review","volume":"25","author":"Xiao","year":"2018","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"288","DOI":"10.26599\/BDMA.2019.9020007","article-title":"Clinical big data and deep learning: Applications, challenges, and future outlooks","volume":"2","author":"Yu","year":"2019","journal-title":"Big Data Min. Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s40708-020-00112-2","article-title":"Application of deep learning in detecting neurological disorders from magnetic resonance images: A survey on the detection of Alzheimer\u2019s disease, Parkinson\u2019s disease and schizophrenia","volume":"7","author":"Noor","year":"2020","journal-title":"Brain Inform."},{"key":"ref_8","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2015, January 7\u20139). Neural Machine Translation by Jointly Learning to Align and Translate. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016, January 13\u201317). Why should I trust you?: Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939778"},{"key":"ref_10","unstructured":"Shapley, L.S. (1951). Notes on the n-Person Game\u2014II: The Value of an n-Person Game, RAND Corporation."},{"key":"ref_11","unstructured":"Strumbelj, E., and Kononenko, I. (2014). Knowledge and Information Systems, Springer."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Binder, A., Montavon, G., Lapuschkin, S., M\u00fcller, K.-R., and Samek, W. (2016, January 2\u20135). Layer-wise relevance propagation for neural networks with local renormalization layers. Proceedings of the International Conference on Artificial Neural Networks, Aberdeen, Scotland.","DOI":"10.1007\/978-3-319-44781-0_8"},{"key":"ref_13","unstructured":"Shao, Y., Cheng, Y., Shah, R.U., Weir, C.R., Bray, B.E., and Zeng-Treitler, Q. (2019, January 12\u201313). Shedding Light on the Black Box: Explaining Deep Neural Network Prediction of Clinical Outcome. Proceedings of the ICHI 2019 21st International Conference on Health Informatics, Rome, Italy."},{"key":"ref_14","first-page":"371","article-title":"Interpretation of interaction: A review","volume":"1","author":"Cox","year":"2007","journal-title":"Ann. Appl. Stat."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1136\/oem.58.3.145","article-title":"Relation between exposure to asbestos and smoking jointly and the risk of lung cancer","volume":"58","author":"Lee","year":"2001","journal-title":"Occup. Environ. Med."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jaccard, J., and Turrisi, R. (2003). Interaction Effects in Multiple Regression, SAGE Publications, Inc.. [2nd ed.].","DOI":"10.4135\/9781412984522"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/S0167-9473(01)00016-0","article-title":"Interpreting neural-network results: A simulation study","volume":"37","author":"Intrator","year":"2001","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_18","unstructured":"Tsang, M., Liu, H., Purushotham, S., Murali, P., and Liu, Y. (2018, January 3\u20138). Neural Interaction Transparency (NIT): Disentangling Learned Interactions for Improved Interpretability. Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montreal, Canada."},{"key":"ref_19","unstructured":"Cui, T., Marttinen, P., and Kaski, S. (2019, January 13). Learning Global Pairwise Interactions with Bayesian Neural Networks. Proceedings of the 4th Workshop on Bayesian Deep Learning (NeurIPS 2019), Vancouver, Canada."},{"key":"ref_20","unstructured":"Paananen, T., Andersen, M.R., and Vehtari, A. (2019). Ranking variables and interactions using predictive uncertainty measures. arXiv."},{"key":"ref_21","unstructured":"Lundberg, S.M., Erion, G.G., and Lee, S.-I. (2019). Consistent Individualized Feature Attribution for Tree Ensembles. arXiv."},{"key":"ref_22","unstructured":"Molnar, C. (2022, November 15). Interpretable Machine Learning: A Guide for Making Black Box Models Interpretable. Available online: https:\/\/christophm.github.io\/interpretable-ml-book\/."},{"key":"ref_23","unstructured":"Greenwell, B.M., Boehmke, B.C., and McCarthy, A.J. (2022, November 15). A Simple and Effective Model-Based Variable Importance Measure. Available online: https:\/\/arxiv.org\/pdf\/1805.04755.pdf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"311","DOI":"10.3233\/JAD-191188","article-title":"Alzheimer\u2019s Disease and Alzheimer\u2019s Disease-Related Dementias in Older African American and White Veterans","volume":"75","author":"Cheng","year":"2020","journal-title":"J. Alzheimers Dis."},{"key":"ref_25","unstructured":"Guland, B., Wilder, D., Lantigua, R., Mayeux, R., Stern, Y., Chen, J., Cross, P., and Killeffer, E. (1997). Racial and Ethnic Differences in the Health of Older Americans, National Academies Press."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.trci.2018.08.009","article-title":"Racial and ethnic differences in trends in dementia prevalence and risk factors in the United States","volume":"4","author":"Chen","year":"2018","journal-title":"Alzheimers Dement."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"419","DOI":"10.2307\/2983440","article-title":"Model Uncertainty, Data Mining and Statistical Inference","volume":"158","author":"Chatfield","year":"1995","journal-title":"J. R. Statist. Soc. A"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"35","DOI":"10.3389\/fevo.2020.00035","article-title":"How Should We Quantify Uncertainty in Statistical Inference?","volume":"8","author":"Lele","year":"2020","journal-title":"Front. Ecol. Evol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1016\/j.trci.2019.10.006","article-title":"Machine learning models to predict onset of dementia: A label learning approach","volume":"5","author":"Nori","year":"2019","journal-title":"Alzheimers Dement."},{"key":"ref_30","first-page":"1138","article-title":"Predicting Diagnosis of Alzheimer\u2019s Disease and Related Dementias Using Administrative Claims","volume":"24","author":"Albrecht","year":"2018","journal-title":"J. Manag. Care Spec. Pharm."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"S329","DOI":"10.3233\/JAD-141413","article-title":"Risk factors and screening methods for detecting dementia: A narrative review","volume":"42","author":"Stephan","year":"2014","journal-title":"J. Alzheimers Dis."}],"container-title":["Journal of Personalized Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4426\/13\/2\/217\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:16:20Z","timestamp":1760120180000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4426\/13\/2\/217"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,26]]},"references-count":31,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["jpm13020217"],"URL":"https:\/\/doi.org\/10.3390\/jpm13020217","relation":{},"ISSN":["2075-4426"],"issn-type":[{"type":"electronic","value":"2075-4426"}],"subject":[],"published":{"date-parts":[[2023,1,26]]}}}