Evidence map›Paper›PMID 35318360›Full record

ArticleScientific reports2022

Estimated date of delivery with electronic medical records by a hybrid GBDT-GRU model.

Yina Wu, Yichao Zhang, Xu Zou, Zhenming Yuan, Wensheng Hu, Sha Lu, Xiaoyan Sun, Yingfei Wu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 26% of its field
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

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

8 authors at 2 institutions in 1 country.

Yina Wu *Engineering Research Center of Mobile Health Management Ministry of Education, Hangzhou Normal University, Hangzhou, China.
Yichao Zhang *Engineering Research Center of Mobile Health Management Ministry of Education, Hangzhou Normal University, Hangzhou, China.
Xu ZouHangzhou Hele Tech. Co, Hangzhou, China.
Zhenming YuanEngineering Research Center of Mobile Health Management Ministry of Education, Hangzhou Normal University, Hangzhou, China.
Wensheng HuHangzhou Women's Hospital, Hangzhou, China.
Sha LuHangzhou Women's Hospital, Hangzhou, China.
Xiaoyan SunEngineering Research Center of Mobile Health Management Ministry of Education, Hangzhou Normal University, Hangzhou, China.
Yingfei WuEngineering Research Center of Mobile Health Management Ministry of Education, Hangzhou Normal University, Hangzhou, China. wyf@hznu.edu.cn.
Hangzhou Normal University · CNHangzhou Women’s Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An accurate estimated date of delivery (EDD) helps pregnant women make adequate preparations before delivery and avoid the panic of parturition. EDD is normally derived from some formulates or estimated by doctors based on last menstruation period and ultrasound examinations. This study attempted to combine antenatal examinations and electronic medical records to develop a hybrid model based on Gradient Boosting Decision Tree and Gated Recurrent Unit (GBDT-GRU). Besides exploring the features that affect the EDD, GBDT-GRU model obtained the results by dynamic prediction of different stages. The mean square error (MSE) and coefficient of determination (R

Indexed as

Electronic Health RecordsFemaleHumansPregnancyTime Factors

Identifiers

PMID35318360
PMCPMC8941136
OpenAlexW4220803947

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.