142 related articles for article (PubMed ID: 35460742)
1. Machine learning in the prediction of postpartum depression: A review.
Cellini P; Pigoni A; Delvecchio G; Moltrasio C; Brambilla P
J Affect Disord; 2022 Jul; 309():350-357. PubMed ID: 35460742
[TBL] [Abstract][Full Text] [Related]
2. Development and validation of a machine learning-based postpartum depression prediction model: A nationwide cohort study.
Hochman E; Feldman B; Weizman A; Krivoy A; Gur S; Barzilay E; Gabay H; Levy J; Levinkron O; Lawrence G
Depress Anxiety; 2021 Apr; 38(4):400-411. PubMed ID: 33615617
[TBL] [Abstract][Full Text] [Related]
3. Machine learning prediction models for postpartum depression: A multicenter study in Japan.
Matsuo S; Ushida T; Emoto R; Moriyama Y; Iitani Y; Nakamura N; Imai K; Nakano-Kobayashi T; Yoshida S; Yamashita M; Matsui S; Kajiyama H; Kotani T
J Obstet Gynaecol Res; 2022 Jul; 48(7):1775-1785. PubMed ID: 35438215
[TBL] [Abstract][Full Text] [Related]
4. Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study.
Hurwitz E; Butzin-Dozier Z; Master H; O'Neil ST; Walden A; Holko M; Patel RC; Haendel MA
JMIR Mhealth Uhealth; 2024 May; 12():e54622. PubMed ID: 38696234
[TBL] [Abstract][Full Text] [Related]
5. Development and validation of a machine learning algorithm for predicting the risk of postpartum depression among pregnant women.
Zhang Y; Wang S; Hermann A; Joly R; Pathak J
J Affect Disord; 2021 Jan; 279():1-8. PubMed ID: 33035748
[TBL] [Abstract][Full Text] [Related]
6. Application of machine learning in predicting the risk of postpartum depression: A systematic review.
Zhong M; Zhang H; Yu C; Jiang J; Duan X
J Affect Disord; 2022 Dec; 318():364-379. PubMed ID: 36055532
[TBL] [Abstract][Full Text] [Related]
7. Family history of psychiatric disorders as a risk factor for maternal postpartum depression: a systematic review protocol.
Kjeldsen MZ; Bricca A; Liu X; Frokjaer VG; Madsen KB; Munk-Olsen T
Syst Rev; 2022 Apr; 11(1):68. PubMed ID: 35422027
[TBL] [Abstract][Full Text] [Related]
8. Predictive validity of the Postpartum Depression Predictors Inventory-Revised (PDPI-R): A longitudinal study with Portuguese women.
Alves S; Fonseca A; Canavarro MC; Pereira M
Midwifery; 2019 Feb; 69():113-120. PubMed ID: 30496938
[TBL] [Abstract][Full Text] [Related]
9. Machine Learning Models for the Prediction of Postpartum Depression: Application and Comparison Based on a Cohort Study.
Zhang W; Liu H; Silenzio VMB; Qiu P; Gong W
JMIR Med Inform; 2020 Apr; 8(4):e15516. PubMed ID: 32352387
[TBL] [Abstract][Full Text] [Related]
10. Using Electronic Health Records and Machine Learning to Predict Postpartum Depression.
Wang S; Pathak J; Zhang Y
Stud Health Technol Inform; 2019 Aug; 264():888-892. PubMed ID: 31438052
[TBL] [Abstract][Full Text] [Related]
11. Sociodemographic and delivery risk factors for developing postpartum depression in a sample of 3233 mothers from the Czech ELSPAC study.
Fiala A; Švancara J; Klánová J; Kašpárek T
BMC Psychiatry; 2017 Mar; 17(1):104. PubMed ID: 28327118
[TBL] [Abstract][Full Text] [Related]
12. Comparison of Methods to Reduce Bias From Clinical Prediction Models of Postpartum Depression.
Park Y; Hu J; Singh M; Sylla I; Dankwa-Mullan I; Koski E; Das AK
JAMA Netw Open; 2021 Apr; 4(4):e213909. PubMed ID: 33856478
[TBL] [Abstract][Full Text] [Related]
13. An optimization for postpartum depression risk assessment and preventive intervention strategy based machine learning approaches.
Liu H; Dai A; Zhou Z; Xu X; Gao K; Li Q; Xu S; Feng Y; Chen C; Ge C; Lu Y; Zou J; Wang S
J Affect Disord; 2023 May; 328():163-174. PubMed ID: 36758872
[TBL] [Abstract][Full Text] [Related]
14. Using clinical decision support as a means of implementing a universal postpartum depression screening program.
Loudon H; Nentin F; Silverman ME
Arch Womens Ment Health; 2016 Jun; 19(3):501-5. PubMed ID: 26669601
[TBL] [Abstract][Full Text] [Related]
15. Postpartum depression and affecting factors in primary care.
Oztora S; Arslan A; Caylan A; Dagdeviren HN
Niger J Clin Pract; 2019 Jan; 22(1):85-91. PubMed ID: 30666025
[TBL] [Abstract][Full Text] [Related]
16. Implementation of Routine Postpartum Depression Screening and Care Initiation Across a Multispecialty Health Care Organization: An 18-Month Retrospective Analysis.
Lind A; Richter S; Craft C; Shapiro AC
Matern Child Health J; 2017 Jun; 21(6):1234-1239. PubMed ID: 28150173
[TBL] [Abstract][Full Text] [Related]
17. Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes.
Woldaregay AZ; Årsand E; Walderhaug S; Albers D; Mamykina L; Botsis T; Hartvigsen G
Artif Intell Med; 2019 Jul; 98():109-134. PubMed ID: 31383477
[TBL] [Abstract][Full Text] [Related]
18. An Integrative Review of Postpartum Depression in Rural U.S. Communities.
Mollard E; Hudson DB; Ford A; Pullen C
Arch Psychiatr Nurs; 2016 Jun; 30(3):418-24. PubMed ID: 27256951
[TBL] [Abstract][Full Text] [Related]
19. Postpartum Depression Screening: A Review for Psychiatrists.
Smith EK; Gopalan P; Glance JB; Azzam PN
Harv Rev Psychiatry; 2016; 24(3):173-87. PubMed ID: 27148910
[TBL] [Abstract][Full Text] [Related]
20. Longitudinal effects of dysfunctional perfectionism and avoidant personality style on postpartum mental disorders: Pathways through antepartum depression and anxiety.
Oddo-Sommerfeld S; Hain S; Louwen F; Schermelleh-Engel K
J Affect Disord; 2016 Feb; 191():280-8. PubMed ID: 26688497
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
