Evidence map›Paper›PMID 40496744›Full record

ArticleData in brief2025

PERI_DEP: A dataset of mother's mental health in Pakistan.

Amna Zafar, Beenish Ayesha Akram, Muhammad Wasim, Maham, Ivan Miguel Pires, Paulo Jorge Coelho

Abstract read
In one paragraph

Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Amna ZafarDepartment of Computer Science, University of Engineering and Technology, Lahore, Pakistan.
Beenish Ayesha AkramDepartment of Computer Engineering, University of Engineering and Technology, Lahore, Pakistan.
Muhammad WasimDepartment of Computer Science, University of Management and Technology, Lahore (Sialkot Campus), Pakistan.
MahamDepartment of Computer Science, University of Engineering and Technology, Lahore, Pakistan.
Ivan Miguel PiresInstituto de Telecomunicações, Escola Superior de Tecnologia e Gestão de Águeda, Universidade de Aveiro, Águeda, Portugal.
Paulo Jorge CoelhoSchool of Technology and Management, Polytechnic University of Leiria, Leiria, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Perinatal depression (PND) represents a multifaceted mental health issue that impacts women throughout the perinatal period. Existing datasets have a class imbalance issue, resulting in biased outcomes. In Pakistan, we developed a novel dataset called PERI_DEP. This dataset leverages the Patient Health Questionnaire (PHQ-9), Edinburgh Postnatal Depression Scale (EPDS), and socio-demographic questionnaires to gather information about mental health state and socioeconomic details of women participants in urban and rural areas. Our novel PERI_DEP dataset contains 14,008 samples, and women from Lahore and Gujranwala participated. To tackle the issue of class imbalance, we employed Generative Adversarial Network (GAN) oversampling technique on our data. A key insight derived from this dataset is the comparative socio-demographic divide of women in rural and urban areas of Pakistan. PERI_DEP dataset enhances hospital capabilities by streamlining screening processes, customizing interventions, and enabling researchers to identify risk factors and develop new treatments accurately.

Indexed as

DepressionMental healthPerinatalPostnatalPrenatal

Identifiers

PMID40496744
PMCPMC12151247

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.