Evidence map›Paper›PMID 36691200›Full record

ArticleBMJ open2022

Develop an ADR prediction system of Chinese herbal injections containing Panax notoginseng saponin: a nested case-control study using machine learning.

Xing-Wei Wu, Jia-Ying Zhang, Huan Chang, Xue-Wu Song, Ya-Lin Wen, En-Wu Long, Rong-Sheng Tong

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

Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
1.9field-weighted citation impact, top 14% 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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Article
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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

7 authors at 1 institution in 1 country.

Xing-Wei WuPharmacy, University of Electronic Science and Technology of China Sichuan Provincial People's Hospital, Chengdu, Sichuan, China.
Jia-Ying ZhangPharmacy, Chengdu First People's Hospital, Chengdu, Sichuan, China.
Huan ChangPharmacy, University of Electronic Science and Technology of China Sichuan Provincial People's Hospital, Chengdu, Sichuan, China.
Xue-Wu SongPharmacy, University of Electronic Science and Technology of China Sichuan Provincial People's Hospital, Chengdu, Sichuan, China.
Ya-Lin WenPharmacy, University of Electronic Science and Technology of China Sichuan Provincial People's Hospital, Chengdu, Sichuan, China.
En-Wu LongPharmacy, University of Electronic Science and Technology of China Sichuan Provincial People's Hospital, Chengdu, Sichuan, China.
Rong-Sheng TongPharmacy, University of Electronic Science and Technology of China Sichuan Provincial People's Hospital, Chengdu, Sichuan, China 318004031@qq.com.ORCID 0000-0003-2206-4390
University of Electronic Science and Technology of China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aimed to develop an adverse drug reactions (ADR) antecedent prediction system using machine learning algorithms to provide the reference for security usage of Chinese herbal injections containing Panax notoginseng saponin in clinical practice.

designA nested case-control study.

settingNational Center for ADR Monitoring and the Electronic Medical Record (EMR) system.

participantsAll patients were from five medical institutions in Sichuan Province from January 2010 to December 2018. MAIN OUTCOMES/MEASURES: Data of patients with ADR who used Chinese herbal injections containing Panax notoginseng saponin were collected from the National Center for ADR Monitoring. A nested case-control study was used to randomly match patients without ADR from the EMR system by the ratio of 1:4. Eighteen machine learning algorithms were applied for the development of ADR prediction models. Area under curve (AUC), accuracy, precision, recall rate and F1 value were used to evaluate the predictive performance of the model. An ADR prediction system was established by the best model selected from the 1080 models.

resultsA total of 530 patients from five medical institutions were included, and 1080 ADR prediction models were developed. Among these models, the AUC of the best capable one was 0.9141 and the accuracy was 0.8947. According to the best model, a prediction system, which can provide early identification of patients at risk for the ADR of Panax notoginseng saponin, has been established.

conclusionThe prediction system developed based on the machine learning model in this study had good predictive performance and potential clinical application.

Indexed as

Drug-Related Side Effects and Adverse ReactionsPanax notoginsengSaponinsCase-Control StudiesHumansMachine LearningSaponinsadverse eventsherbal medicinetoxicity

Identifiers

PMID36691200
PMCPMC9462100
OpenAlexW4294957923

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.