{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T20:11:05Z","timestamp":1793131865589,"version":"4.5.0"},"reference-count":46,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["U01 TR003629"],"award-info":[{"award-number":["U01 TR003629"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Postpartum depression is a common mental health condition that can occur up to one year after childbirth. Recent studies have increasingly used machine learning techniques to predict its occurrence; however, few have comprehensively explored the use of electronic health record data, particularly in tertiary care settings where such data can be fragmented. Methods: We analyzed electronic health record data from 12,284 women who delivered at The Birth Center at Atrium Health Wake Forest Baptist Medical Center, excluding those with missing data or no prenatal or postpartum visits. To define the target variable, we examined different combinations of depression screening tools (Edinburgh Postnatal Depression Scale and Patient Health Questionnaire-9), along with diagnosis codes specific to postpartum depression. We then trained a random forest classification model to predict postpartum depression. Results: The model achieved an area under the receiver operating characteristic curve of 0.733 \u00b1 0.008, which is comparable to previous studies. Adding socioeconomic features from census tract data did not improve predictive performance, underscoring the importance of individual-level data. Incorporating national survey data, such as the Pregnancy Risk Assessment Monitoring System, also did not improve performance due to limited overlap in data features. Interestingly, model performance was slightly lower among Hispanic patients (area under the curve = 0.713 \u00b1 0.040), although this difference was not statistically significant (p = 0.17), likely due to the small sample size. A similar, but statistically significant trend was observed in the larger national survey dataset (area under the curve = 0.699 \u00b1 0.019 for Hispanic patients versus 0.735 \u00b1 0.010 for White patients, p &lt; 0.01). Conclusions: While our model demonstrates moderate predictive capability, further validation and prospective testing are needed before clinical implementation. This work also identified an optimal approach for digital phenotyping postpartum depression in electronic health record data and highlighted key gaps in data quality and completeness. These findings emphasize the importance of robust data when developing predictive models for real-world clinical use.<\/jats:p>","DOI":"10.3390\/jcm14186644","type":"journal-article","created":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T12:08:03Z","timestamp":1758542883000},"page":"6644","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Building a Machine Learning Model to Predict Postpartum Depression from Electronic Health Records in a Tertiary Care Setting"],"prefix":"10.3390","volume":"14","author":[{"given":"Zhitu","family":"Ma","sequence":"first","affiliation":[{"name":"Clinical and Translational Science Institute, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Horvath","sequence":"additional","affiliation":[{"name":"Clinical and Translational Science Institute, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David Michael","family":"Stamilio","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Section of Maternal-Fetal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kobby","family":"Sekyere","sequence":"additional","affiliation":[{"name":"Clinical and Translational Science Institute, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2421-8229","authenticated-orcid":false,"given":"Metin Nafi","family":"Gurcan","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, Z., Liu, J., Shuai, H., Cai, Z., Fu, X., Liu, Y., Xiao, X., Zhang, W., Krabbendam, E., and Liu, S. (2021). Mapping global prevalence of depression among postpartum women. Transl. Psychiatry, 11.","DOI":"10.1038\/s41398-021-01692-1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1097\/AOG.0000000000005200","article-title":"Screening and Diagnosis of Mental Health Conditions During Pregnancy and Postpartum","volume":"141","author":"Simas","year":"2023","journal-title":"Obstet. Gynecol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1590\/2237-6089-2016-0034","article-title":"Screening and diagnosing postpartum depression: When and how?","volume":"39","author":"Moraes","year":"2017","journal-title":"Trends Psychiatry Psychother."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.jad.2023.02.056","article-title":"Physical health and comorbid anxiety and depression across the first year postpartum in Ireland (MAMMI study): A longitudinal population-based study","volume":"328","author":"Hannon","year":"2023","journal-title":"J. Affect. Disord."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1080\/00221325.2023.2276264","article-title":"Depression during the postpartum period and impacts on parent\u2013child relationships: A narrative review","volume":"185","author":"Frankel","year":"2024","journal-title":"J. Genet. Psychol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wierzbinska, E.M., and Susser, L.C. (2025). Postpartum Depression and Child Mental Health Outcomes: Evidenced-Based Interventions. Curr. Treat. Options Psychiatry, 12.","DOI":"10.1007\/s40501-025-00347-y"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1097\/00006199-200109000-00004","article-title":"Predictors of postpartum depression: An update","volume":"50","author":"Beck","year":"2001","journal-title":"Nurs. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.jad.2018.07.063","article-title":"The association between body mass index and postpartum depression: A population-based study","volume":"240","author":"Silverman","year":"2018","journal-title":"J. Affect. Disord."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Matsumura, K., Hamazaki, K., Tsuchida, A., Kasamatsu, H., Inadera, H., Japan, E., and Children\u2019s Study, G. (2019). Education level and risk of postpartum depression: Results from the Japan Environment and Children\u2019s Study (JECS). BMC Psychiatry, 19.","DOI":"10.1186\/s12888-019-2401-3"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"209","DOI":"10.2188\/jea.JE20210117","article-title":"Association Between Mode of Delivery and Postpartum Depression: The Japan Environment and Children\u2019s Study (JECS)","volume":"33","author":"Baba","year":"2023","journal-title":"J. Epidemiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"291","DOI":"10.2174\/1389202922666210705124359","article-title":"Machine learning in healthcare","volume":"22","author":"Habehh","year":"2021","journal-title":"Curr. Genom."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.jad.2022.08.070","article-title":"Application of machine learning in predicting the risk of postpartum depression: A systematic review","volume":"318","author":"Zhong","year":"2022","journal-title":"J. Affect. Disord."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shin, D., Lee, K.J., Adeluwa, T., and Hur, J. (2020). Machine Learning-Based Predictive Modeling of Postpartum Depression. J. Clin. Med., 9.","DOI":"10.3390\/jcm9092899"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Park, Y., Hu, J., Singh, M., Sylla, I., Dankwa-Mullan, I., Koski, E., and Das, A.K. (2021). Comparison of Methods to Reduce Bias from Clinical Prediction Models of Postpartum Depression. JAMA Netw. Open, 4.","DOI":"10.1001\/jamanetworkopen.2021.3909"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1089\/tmj.2014.0113","article-title":"A Mobile Health Application to Predict Postpartum Depression Based on Machine Learning","volume":"21","author":"Tortajada","year":"2015","journal-title":"Telemed. J. E Health"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, W., Liu, H., Silenzio, V.M.B., Qiu, P., and Gong, W. (2020). Machine Learning Models for the Prediction of Postpartum Depression: Application and Comparison Based on a Cohort Study. JMIR Med. Inf., 8.","DOI":"10.2196\/15516"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Williams, B.A. (2021). Constructing epidemiologic cohorts from electronic health record data. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph182413193"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Amit, G., Girshovitz, I., Marcus, K., Zhang, Y., Pathak, J., Bar, V., and Akiva, P. (2021). Estimation of postpartum depression risk from electronic health records using machine learning. BMC Pregnancy Childbirth, 21.","DOI":"10.1186\/s12884-021-04087-8"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.jclinepi.2020.09.036","article-title":"PCORnet\u00ae 2020: Current state, accomplishments, and future directions","volume":"129","author":"Forrest","year":"2021","journal-title":"J. Clin. Epidemiol."},{"key":"ref_20","first-page":"888","article-title":"Using Electronic Health Records and Machine Learning to Predict Postpartum Depression","volume":"264","author":"Wang","year":"2019","journal-title":"Stud. Health Technol. Inf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jad.2020.09.113","article-title":"Development and validation of a machine learning algorithm for predicting the risk of postpartum depression among pregnant women","volume":"279","author":"Zhang","year":"2021","journal-title":"J. Affect. Disord."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Andersson, S., Bathula, D.R., Iliadis, S.I., Walter, M., and Skalkidou, A. (2021). Predicting women with depressive symptoms postpartum with machine learning methods. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-86368-y"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1002\/da.23123","article-title":"Development and validation of a machine learning-based postpartum depression prediction model: A nationwide cohort study","volume":"38","author":"Hochman","year":"2021","journal-title":"Depress. Anxiety"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1176\/appi.ajp.2007.06111893","article-title":"Clinically identified maternal depression before, during, and after pregnancies ending in live births","volume":"164","author":"Dietz","year":"2007","journal-title":"Am. J. Psychiatry"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1017\/S0033291715002913","article-title":"Does depression diagnosis and antidepressant prescribing vary by location? Analysis of ethnic density associations using a large primary-care dataset","volume":"46","author":"Schofield","year":"2016","journal-title":"Psychol. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1111\/j.1600-0447.2009.01363.x","article-title":"A systematic review of studies validating the Edinburgh Postnatal Depression Scale in antepartum and postpartum women","volume":"119","author":"Gibson","year":"2009","journal-title":"Acta Psychiatr. Scand."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Post, M.J., Van Der Putten, P., and Van Rijn, J.N. (2016, January 13\u201315). Does feature selection improve classification? A large scale experiment in OpenML. Proceedings of the Advances in Intelligent Data Analysis XV: 15th International Symposium, IDA 2016, Stockholm, Sweden.","DOI":"10.1007\/978-3-319-46349-0_14"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, R.-C., Dewi, C., Huang, S.-W., and Caraka, R.E. (2020). Selecting critical features for data classification based on machine learning methods. J. Big Data, 7.","DOI":"10.1186\/s40537-020-00327-4"},{"key":"ref_29","unstructured":"G\u00e9ron, A. (2022). Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O\u2019Reilly Media, Inc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1093\/oxfordjournals.pan.a004868","article-title":"Logistic regression in rare events data","volume":"9","author":"King","year":"2001","journal-title":"Political Anal."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE international conference on computer vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1007\/s11524-006-9094-x","article-title":"The development of a standardized neighborhood deprivation index","volume":"83","author":"Messer","year":"2006","journal-title":"J. Urban. Health"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Flanagan, B.E., Gregory, E.W., Hallisey, E.J., Heitgerd, J.L., and Lewis, B. (2011). A social vulnerability index for disaster management. J. Homel. Secur. Emerg. Manag., 8.","DOI":"10.2202\/1547-7355.1792"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Maroko, A.R., Doan, T.M., Arno, P.S., Hubel, M., Yi, S., and Viola, D. (2016). Integrating social determinants of health with treatment and prevention: A new tool to assess local area deprivation. Prev. Chronic Dis., 13.","DOI":"10.5888\/pcd13.160221"},{"key":"ref_35","unstructured":"Solar, O., and Irwin, A. (2010). A Conceptual Framework for Action on the Social Determinants of Health, WHO Document Production Services."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Salas-Gonz\u00e1lez, J., Heredia-Rizo, A.M., Fricke-Comellas, H., Chimenti, R.L., and Casuso-Holgado, M.J. (2025). Patterns of pain perception in individuals with anxiety or depressive disorders: A systematic review and meta-analysis of experimental pain research. J. Pain, 13.","DOI":"10.1016\/j.jpain.2025.105530"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.jad.2021.12.121","article-title":"Risk factors associated with postpartum depressive symptoms: A multinational study","volume":"301","author":"Bradshaw","year":"2022","journal-title":"J. Affect. Disord."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mitchell-Jones, N., Lawson, K., Bobdiwala, S., Farren, J.A., Tobias, A., Bourne, T., and Bottomley, C. (2020). Association between hyperemesis gravidarum and psychological symptoms, psychosocial outcomes and infant bonding: A two-point prospective case\u2013control multicentre survey study in an inner city setting. BMJ Open, 10.","DOI":"10.1136\/bmjopen-2020-039715"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wong, E.F., Saini, A.K., Accortt, E.E., Wong, M.S., Moore, J.H., and Bright, T.J. (2024). Evaluating Bias-Mitigated Predictive Models of Perinatal Mood and Anxiety Disorders. JAMA Netw. Open, 7.","DOI":"10.1001\/jamanetworkopen.2024.38152"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.jad.2022.04.093","article-title":"Machine learning in the prediction of postpartum depression: A review","volume":"309","author":"Cellini","year":"2022","journal-title":"J. Affect. Disord."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2729","DOI":"10.11591\/eei.v13i4.7185","article-title":"Recent advancements in postpartum depression prediction through machine learning approaches: A systematic review","volume":"13","author":"Fazraningtyas","year":"2024","journal-title":"Bull. Electr. Eng. Inform."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Huang, Y., Alvernaz, S., Kim, S.J., Maki, P., Dai, Y., and Penalver Bernabe, B. (2024). Predicting Prenatal Depression and Assessing Model Bias Using Machine Learning Models. Biol Psychiatry Glob Open Sci, 4.","DOI":"10.1016\/j.bpsgos.2024.100376"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1111\/j.1552-6909.2011.01254.x","article-title":"Postpartum depression and help-seeking behaviors in immigrant Hispanic women","volume":"40","author":"Callister","year":"2011","journal-title":"J. Obstet. Gynecol. Neonatal Nurs."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"American College of Obstetricians and Gynecologists (2023). Treatment and Management of Mental Health Conditions During Pregnancy and Postpartum. Obs. Gynecol., 141, 1262\u20131268.","DOI":"10.1097\/AOG.0000000000005202"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chen, N., and Pan, J. (2022). The causal effect of delivery volume on severe maternal morbidity: An instrumental variable analysis in Sichuan, China. BMJ Glob. Health, 7.","DOI":"10.1136\/bmjgh-2022-008428"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, Q., Jiang, Q., Yang, Y., and Pan, J. (2022). The burden of travel for care and its influencing factors in China: An inpatient-based study of travel time. J. Transp. Health, 25.","DOI":"10.1016\/j.jth.2022.101353"}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/18\/6644\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:46:32Z","timestamp":1760035592000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/18\/6644"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"references-count":46,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["jcm14186644"],"URL":"https:\/\/doi.org\/10.3390\/jcm14186644","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,20]]}}}