Evidence map›Paper›PMID 37521888›Full record

ArticleBelitung nursing journal2022

Human-In-The-Loop application design for early detection of pregnancy danger signs.

Melyana Nurul Widyawati, Ery Hadiyani Puji Astuti, Kurnianingsih

Abstract read
In one paragraph

Article in Belitung nursing journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

What it found

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Melyana Nurul WidyawatiDepartment of Midwifery, Poltekkes Kemenkes Semarang, Indonesia.ORCID https://orcid.org/0000-0003-2039-2892
Ery Hadiyani Puji AstutiDepartment of Midwifery, Poltekkes Kemenkes Semarang, Indonesia.ORCID https://orcid.org/0000-0002-2917-9038
KurnianingsihDepartment of Electrical Engineering, Politeknik Negeri Semarang, Indonesia.ORCID https://orcid.org/0000-0001-7339-7449

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Pregnancy period is a period for mothers to empower themselves to be safe and comfortable. Pregnant women must acquire pregnancy-related information, such as warning signs of pregnancy, to avoid severe complications and even death during pregnancy and childbirth. Therefore, developing an application for pregnant women would be very helpful. Objective: This study aimed to apply a Human-In-The-Loop design with an android application to detect pregnancy risk early and avoid maternal morbidity and mortality. Methods: Data were collected from the cohort of 5324 pregnant women at the community health centers in the West Lombok District from 2020 to February 2021. The data included age, parity, height, inter-pregnancy interval, hemoglobin levels, upper arm circumference, previous diseases, and bleeding history. A decision tree was employed in the developed Human-In-The-Loop mobile application for identifying pregnancy danger signs. The midwife (human-in-the-loop) reviewed and clarified the data to generate the final detection and made a recommendation. Results: The ordinal regression model revealed that older patients who have more parity, lower height, the distance of children <2 years, hemoglobin <11 g/dl, upper arm circumference (UPC) <23.5 cm, have positive HBsAg, have HIV disease, have a history of diabetes mellitus (DM), have a history of hypertension, positive protein urine, and have other diseases are more likely to have a high maternal risk. The decision tree outperformed and obtained a high accuracy of 92% ± 0.0351 compared to the nine individual classifiers (Nearest Neighbors, Random Forest, Neural Net, AdaBoost, Gaussian Naïve Bayes, Bagging, Extra Tree, Gradient Boosting, and Stacking). Conclusion: The Human-In-The-Loop mobile app developed in this study can be used by healthcare professionals, especially midwives and nurses, to detect danger indications early in pregnancy, accurately diagnose the high risk of pregnancy, and provide treatment and care recommendations during pregnancy and childbirth.

Indexed as

early detectionHuman-In-The-Loopmachine learningmidwiferymobile applicationnursingpregnancy danger signs

Identifiers

PMID37521888
PMCPMC10386816

What OpenQuestion holds

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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.