{"title":"预测妊娠期抑郁症首次发病:将机器学习方法应用于患者报告数据。","authors":"Tamar Krishnamurti, Samantha Rodriguez, Bryan Wilder, Priya Gopalan, Hyagriv N. Simhan","doi":"10.1007/s00737-024-01474-w","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><p>To develop a machine learning algorithm, using patient-reported data from early pregnancy, to predict later onset of first time moderate-to-severe depression.</p><h3>Methods</h3><p>A sample of 944 U.S. patient participants from a larger longitudinal observational cohortused a prenatal support mobile app from September 2019 to April 2022. Participants self-reported clinical and social risk factors during first trimester initiation of app use and completed voluntary depression screenings in each trimester. Several machine learning algorithms were applied to self-reported data, including a novel algorithm for causal discovery. Training and test datasets were built from a randomized 80/20 data split. Models were evaluated on their predictive accuracy and their simplicity (i.e., fewest variables required for prediction).</p><h3>Results</h3><p>Among participants, 78% identified as white with an average age of 30 [IQR 26–34]; 61% had income ≥ $50,000; 70% had a college degree or higher; and 49% were nulliparous. All models accurately predicted first time moderate-severe depression using first trimester baseline data (AUC 0.74–0.89, sensitivity 0.35–0.81, specificity 0.78–0.95). Several predictors were common across models, including anxiety history, partnered status, psychosocial factors, and pregnancy-specific stressors. The optimal model used only 14 (26%) of the possible variables and had excellent accuracy (AUC = 0.89, sensitivity = 0.81, specificity = 0.83). When food insecurity reports were included among a subset of participants, demographics, including race and income, dropped out and the model became more accurate (AUC = 0.93) and simpler (9 variables).</p><h3>Conclusion</h3><p>A relatively small amount of self-report data produced a highly predictive model of first time depression among pregnant individuals.</p></div>","PeriodicalId":8369,"journal":{"name":"Archives of Women's Mental Health","volume":"27 6","pages":"1019 - 1031"},"PeriodicalIF":3.2000,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00737-024-01474-w.pdf","citationCount":"0","resultStr":"{\"title\":\"Predicting first time depression onset in pregnancy: applying machine learning methods to patient-reported data\",\"authors\":\"Tamar Krishnamurti, Samantha Rodriguez, Bryan Wilder, Priya Gopalan, Hyagriv N. Simhan\",\"doi\":\"10.1007/s00737-024-01474-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Purpose</h3><p>To develop a machine learning algorithm, using patient-reported data from early pregnancy, to predict later onset of first time moderate-to-severe depression.</p><h3>Methods</h3><p>A sample of 944 U.S. patient participants from a larger longitudinal observational cohortused a prenatal support mobile app from September 2019 to April 2022. Participants self-reported clinical and social risk factors during first trimester initiation of app use and completed voluntary depression screenings in each trimester. Several machine learning algorithms were applied to self-reported data, including a novel algorithm for causal discovery. Training and test datasets were built from a randomized 80/20 data split. Models were evaluated on their predictive accuracy and their simplicity (i.e., fewest variables required for prediction).</p><h3>Results</h3><p>Among participants, 78% identified as white with an average age of 30 [IQR 26–34]; 61% had income ≥ $50,000; 70% had a college degree or higher; and 49% were nulliparous. All models accurately predicted first time moderate-severe depression using first trimester baseline data (AUC 0.74–0.89, sensitivity 0.35–0.81, specificity 0.78–0.95). Several predictors were common across models, including anxiety history, partnered status, psychosocial factors, and pregnancy-specific stressors. The optimal model used only 14 (26%) of the possible variables and had excellent accuracy (AUC = 0.89, sensitivity = 0.81, specificity = 0.83). When food insecurity reports were included among a subset of participants, demographics, including race and income, dropped out and the model became more accurate (AUC = 0.93) and simpler (9 variables).</p><h3>Conclusion</h3><p>A relatively small amount of self-report data produced a highly predictive model of first time depression among pregnant individuals.</p></div>\",\"PeriodicalId\":8369,\"journal\":{\"name\":\"Archives of Women's Mental Health\",\"volume\":\"27 6\",\"pages\":\"1019 - 1031\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s00737-024-01474-w.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Archives of Women's Mental Health\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s00737-024-01474-w\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PSYCHIATRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archives of Women's Mental Health","FirstCategoryId":"3","ListUrlMain":"https://link.springer.com/article/10.1007/s00737-024-01474-w","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PSYCHIATRY","Score":null,"Total":0}
Predicting first time depression onset in pregnancy: applying machine learning methods to patient-reported data
Purpose
To develop a machine learning algorithm, using patient-reported data from early pregnancy, to predict later onset of first time moderate-to-severe depression.
Methods
A sample of 944 U.S. patient participants from a larger longitudinal observational cohortused a prenatal support mobile app from September 2019 to April 2022. Participants self-reported clinical and social risk factors during first trimester initiation of app use and completed voluntary depression screenings in each trimester. Several machine learning algorithms were applied to self-reported data, including a novel algorithm for causal discovery. Training and test datasets were built from a randomized 80/20 data split. Models were evaluated on their predictive accuracy and their simplicity (i.e., fewest variables required for prediction).
Results
Among participants, 78% identified as white with an average age of 30 [IQR 26–34]; 61% had income ≥ $50,000; 70% had a college degree or higher; and 49% were nulliparous. All models accurately predicted first time moderate-severe depression using first trimester baseline data (AUC 0.74–0.89, sensitivity 0.35–0.81, specificity 0.78–0.95). Several predictors were common across models, including anxiety history, partnered status, psychosocial factors, and pregnancy-specific stressors. The optimal model used only 14 (26%) of the possible variables and had excellent accuracy (AUC = 0.89, sensitivity = 0.81, specificity = 0.83). When food insecurity reports were included among a subset of participants, demographics, including race and income, dropped out and the model became more accurate (AUC = 0.93) and simpler (9 variables).
Conclusion
A relatively small amount of self-report data produced a highly predictive model of first time depression among pregnant individuals.
期刊介绍:
Archives of Women’s Mental Health is the official journal of the International Association for Women''s Mental Health, Marcé Society and the North American Society for Psychosocial Obstetrics and Gynecology (NASPOG). The exchange of knowledge between psychiatrists and obstetrician-gynecologists is one of the major aims of the journal. Its international scope includes psychodynamics, social and biological aspects of all psychiatric and psychosomatic disorders in women. The editors especially welcome interdisciplinary studies, focussing on the interface between psychiatry, psychosomatics, obstetrics and gynecology. Archives of Women’s Mental Health publishes rigorously reviewed research papers, short communications, case reports, review articles, invited editorials, historical perspectives, book reviews, letters to the editor, as well as conference abstracts. Only contributions written in English will be accepted. The journal assists clinicians, teachers and researchers to incorporate knowledge of all aspects of women’s mental health into current and future clinical care and research.