Research on the construction of prediction model for depressive symptom in the second and third trimester of pregnancy based on artificial neural network - Top-Institutions - MDSpire

Research on the construction of prediction model for depressive symptom in the second and third trimester of pregnancy based on artificial neural network

  • By

  • Wang, Liuyue

  • Zhou, Dandan

  • Liu, Yanhui

  • Liu, Zhiqun

  • Wan, Huan

  • April 29, 2026

  • 0 min

Share

Top Institutions in Obgyn

Brief introduction explaining scope and methodology.

  • #1

    Changsha Tertiary Hospital
    Changsha Tertiary Hospital

    Changsha, Hunan

    Key Differentiators

    • Psychiatry

  • #2

    Hengyang Tertiary Hospital
    Hengyang Tertiary Hospital

    Hengyang, Hunan

    Key Differentiators

    • Psychiatry

  • #3

    Cedars-Sinai
    Cedars-Sinai

    Los Angeles, California

    Key Differentiators

    • Psychiatry
    • Computational Biomedicine

  • #4

    Sichuan Provincial People’s Hospital
    Sichuan Provincial People’s Hospital

    Chengdu, Sichuan

    Key Differentiators

    • Psychiatry

  • #5

    Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
    Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology

    Wuhan, Hubei

    Key Differentiators

    • Psychiatry

Original Source(s)

Research on the construction of prediction model for depressive symptom in the second and third trimester of pregnancy based on artificial neural network

  • by Wang, Liuyue , Zhou, Dandan , Liu, Yanhui , Liu, Zhiqun , Wan, Huan

  • April 29, 2026

Related Content

Impact of Parental Depression on the Mental Health of Children: Insights into Intergenerational Dynamics

Staffing, Flexibility Top Nurses' List to Return

Two-thirds of former hospital nurses would consider coming back if workplace conditions improved, survey finds

Do Women Physicians Leave Practice Earlier Than Men?

Nationwide US analysis suggests higher attrition risk across specialties and younger exit ages among women physicians.