{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T04:46:56Z","timestamp":1764996416349,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":43,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"funder":[{"name":"Washington University\/BJC HealthCare Big Ideas Healthcare Innovation Award"},{"DOI":"10.13039\/501100019814","name":"Fullgraf Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100019814","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NIH","award":["5T32GM108539-07"],"award-info":[{"award-number":["5T32GM108539-07"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539056","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"page":"3377-3387","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records"],"prefix":"10.1145","author":[{"given":"Hanyang","family":"Liu","sequence":"first","affiliation":[{"name":"Washington University in St. Louis, St. Louis, MO, USA"}]},{"given":"Sunny S.","family":"Lou","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, St. Louis, MO, USA"}]},{"given":"Benjamin C.","family":"Warner","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, St. Louis, MO, USA"}]},{"given":"Derek R.","family":"Harford","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, St. Louis, MO, USA"}]},{"given":"Thomas","family":"Kannampallil","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, St. Louis, MO, USA"}]},{"given":"Chenyang","family":"Lu","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, St. Louis, MO, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271","author":"Bai S.","year":"2018","unstructured":"Bai, S., Kolter, J. Z., and Koltun, V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/3320516.3320834"},{"key":"e_1_3_2_2_3_1","volume-title":"Subtypes in clinical burnout patients enrolled in an employee rehabilitation program: differences in burnout profiles, depression, and recovery\/resources-stress balance. BMC psychiatry 18, 1","author":"Bauernhofer K.","year":"2018","unstructured":"Bauernhofer, K., Bassa, D., Canazei, M., Jim\u00e9nez, P., Paechter, M., Papousek, I., Fink, A., and Weiss, E. M. Subtypes in clinical burnout patients enrolled in an employee rehabilitation program: differences in burnout profiles, depression, and recovery\/resources-stress balance. BMC psychiatry 18, 1 (2018), 1--13."},{"key":"e_1_3_2_2_4_1","first-page":"5","article-title":"Measures of electronic health record use in outpatient settings across vendors","volume":"28","author":"Baxter S. L.","year":"2021","unstructured":"Baxter, S. L., Apathy, N. C., Cross, D. A., Sinsky, C., and Hribar, M. R. Measures of electronic health record use in outpatient settings across vendors. JAMIA 28, 5 (2021), 955--959.","journal-title":"JAMIA"},{"key":"e_1_3_2_2_5_1","volume-title":"Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150","author":"Beltagy I.","year":"2020","unstructured":"Beltagy, I., Peters, M. E., and Cohan, A. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150 (2020)."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocaa338"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_2_8_1","volume-title":"NeurIPS Deep Learning and Representation Learning Workshop","author":"Chung J.","year":"2014","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. NeurIPS Deep Learning and Representation Learning Workshop (2014)."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/AVSS.2019.8909841"},{"key":"e_1_3_2_2_10_1","first-page":"1110","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Du Y.","year":"2015","unstructured":"Du, Y., Wang, W., and Wang, L. Hierarchical recurrent neural network for skeleton based action recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (2015), pp. 1110--1118."},{"key":"e_1_3_2_2_11_1","volume-title":"Automatic detection of front-line clinician hospital shifts: a novel use of electronic health record timestamp data. Applied clinical informatics 10, 01","author":"Dziorny A. C.","year":"2019","unstructured":"Dziorny, A. C., Orenstein, E. W., Lindell, R. B., Hames, N. A., Washington, N., and Desai, B. Automatic detection of front-line clinician hospital shifts: a novel use of electronic health record timestamp data. Applied clinical informatics 10, 01 (2019), 028--037."},{"key":"e_1_3_2_2_12_1","first-page":"1","article-title":"Understanding physician work and well-being through social network modeling using electronic health record data: a cohort study","author":"Escribe C.","year":"2022","unstructured":"Escribe, C., Eisenstat, S. A., and Palamara, K. Understanding physician work and well-being through social network modeling using electronic health record data: a cohort study. Journal of General Internal Medicine (2022), 1--8.","journal-title":"Journal of General Internal Medicine ("},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMsa1903759"},{"key":"e_1_3_2_2_14_1","first-page":"5","article-title":"Conceptual considerations for using ehr-based activity logs to measure clinician burnout and its effects","volume":"28","author":"Kannampallil T.","year":"2021","unstructured":"Kannampallil, T., Abraham, J., Lou, S. S., and Payne, P. R. Conceptual considerations for using ehr-based activity logs to measure clinician burnout and its effects. JAMIA 28, 5 (2021), 1032--1037.","journal-title":"JAMIA"},{"key":"e_1_3_2_2_15_1","volume-title":"Time2vec: Learning a vector representation of time. arXiv preprint arXiv:1907.05321","author":"Kazemi S. M.","year":"2019","unstructured":"Kazemi, S. M., Goel, R., Eghbali, S., Ramanan, J., Sahota, J., Thakur, S., Wu, S., Smyth, C., Poupart, P., and Brubaker, M. Time2vec: Learning a vector representation of time. arXiv preprint arXiv:1907.05321 (2019)."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.113"},{"key":"e_1_3_2_2_17_1","volume-title":"-F. An app developed for detecting nurse burnouts using the convolutional neural networks in microsoft excel: population-based questionnaire study. JMIR medical informatics 8, 5","author":"Lee Y.-L.","year":"2020","unstructured":"Lee, Y.-L., Chou, W., Chien, T.-W., Chou, P.-H., Yeh, Y.-T., and Lee, H.-F. An app developed for detecting nurse burnouts using the convolutional neural networks in microsoft excel: population-based questionnaire study. JMIR medical informatics 8, 5 (2020), e16528."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1106"},{"key":"e_1_3_2_2_19_1","volume-title":"A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019","author":"Lipton Z. C.","year":"2015","unstructured":"Lipton, Z. C., Berkowitz, J., and Elkan, C. A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019 (2015)."},{"key":"e_1_3_2_2_20_1","volume-title":"Learning to diagnose with lstm recurrent neural networks. arXiv preprint arXiv:1511.03677","author":"Lipton Z. C.","year":"2015","unstructured":"Lipton, Z. C., Kale, D. C., Elkan, C., and Wetzel, R. Learning to diagnose with lstm recurrent neural networks. arXiv preprint arXiv:1511.03677 (2015)."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/321"},{"key":"e_1_3_2_2_22_1","volume-title":"Temporal associations between ehr-derived workload, burnout, and errors: a prospective cohort study. J. General Internal Med. (in press)","author":"Lou S.","year":"2022","unstructured":"Lou, S., Lew, D., Harford, D., Lu, C., Evanoff, B., Duncan, J., and Kannampallil, T. Temporal associations between ehr-derived workload, burnout, and errors: a prospective cohort study. J. General Internal Med. (in press) (2022)."},{"key":"e_1_3_2_2_23_1","volume-title":"Effect of clinician attention switching on workload and wrong-patient errors. British Journal of Anaesthesia","author":"Lou S. S.","year":"2022","unstructured":"Lou, S. S., Kim, S., Harford, D., Warner, B. C., Payne, P. R., Abraham, J., and Kannampallil, T. Effect of clinician attention switching on workload and wrong-patient errors. British Journal of Anaesthesia (2022)."},{"key":"e_1_3_2_2_24_1","volume-title":"Predicting physician burnout using clinical activity logs: model performance and lessons learned. Journal of Biological Informatics","author":"Lou S. S.","year":"2022","unstructured":"Lou, S. S., Liu, H., Warner, B. C., Harford, D. R., Lu, C., and Kannampallil, T. Predicting physician burnout using clinical activity logs: model performance and lessons learned. Journal of Biological Informatics (2022)."},{"key":"e_1_3_2_2_25_1","volume-title":"Taking action against clinician burnout: a systems approach to professional well-being","author":"Medicine N. A. S. E.","year":"2019","unstructured":"Medicine, N. A. S. E. Taking action against clinician burnout: a systems approach to professional well-being. National Academies Press (2019)."},{"key":"e_1_3_2_2_26_1","volume-title":"Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781","author":"Mikolov T.","year":"2013","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)."},{"key":"e_1_3_2_2_27_1","volume-title":"Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K. Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499","author":"Oord A.","year":"2016","unstructured":"Oord, A. v. d., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K. Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499 (2016)."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2019.2896659"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2021.100879"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109896"},{"key":"e_1_3_2_2_31_1","volume-title":"Burnout and career satisfaction among american surgeons. Annals of surgery 250, 3","author":"Shanafelt T. D.","year":"2009","unstructured":"Shanafelt, T. D., Balch, C. M., Bechamps, G. J., Russell, T., Dyrbye, L., Satele, D., and Collicott, P. Burnout and career satisfaction among american surgeons. Annals of surgery 250, 3 (2009), 463--471."},{"key":"e_1_3_2_2_32_1","volume-title":"Burnout and satisfaction with work-life balance among us physicians relative to the general us population. Archives of internal medicine 172, 18","author":"Shanafelt T. D.","year":"2012","unstructured":"Shanafelt, T. D., Boone, S., Tan, L., Dyrbye, L. N., Sotile, W., Satele, D., West, C. P., Sloan, J., and Oreskovich, M. R. Burnout and satisfaction with work-life balance among us physicians relative to the general us population. Archives of internal medicine 172, 18 (2012), 1377--1385."},{"key":"e_1_3_2_2_33_1","first-page":"4","article-title":"Metrics for assessing physician activity using electronic health record log data","volume":"27","author":"Sinsky C. A.","year":"2020","unstructured":"Sinsky, C. A., Rule, A., Cohen, G., Arndt, B. G., Shanafelt, T. D., Sharp, C. D., et al. Metrics for assessing physician activity using electronic health record log data. JAMIA 27, 4 (2020), 639--643.","journal-title":"JAMIA"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014983"},{"key":"e_1_3_2_2_35_1","first-page":"2","article-title":"Personalized multitask learning for predicting tomorrow's mood, stress, and health","volume":"11","author":"Taylor S.","year":"2017","unstructured":"Taylor, S., Jaqes, N., Nosakhare, E., Sano, A., and Picard, R. Personalized multitask learning for predicting tomorrow's mood, stress, and health. IEEE Transactions on Affective Computing 11, 2 (2017), 200--213.","journal-title":"IEEE Transactions on Affective Computing"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40596-017-0849-3"},{"key":"e_1_3_2_2_37_1","volume-title":"Attention is all you need. NeurIPS","author":"Vaswani A.","year":"2017","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. Attention is all you need. NeurIPS (2017)."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"crossref","unstructured":"Wang Z. Yan W. and Oates T. Time series classification from scratch with deep neural networks: A strong baseline. In IJCNN (2017) IEEE pp. 1578--1585.","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1515\/med-2018-0039"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1174"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313747"},{"key":"e_1_3_2_2_42_1","first-page":"863","volume-title":"Proceedings of the 25th ACM international conference on Multimedia","author":"Zhao B.","year":"2019","unstructured":"Zhao, B., Li, X., and Lu, X. Hierarchical recurrent neural network for video summarization. In Proceedings of the 25th ACM international conference on Multimedia (2019), pp. 863--871."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Washington DC USA","acronym":"KDD '22"},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539056","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539056","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:50Z","timestamp":1750183790000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539056"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":43,"alternative-id":["10.1145\/3534678.3539056","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539056","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}