Evidence map›Paper›PMID 30602368›Full record

ArticleJournal of translational medicine2019

Prediction of postoperative complications of pediatric cataract patients using data mining.

Kai Zhang, Xiyang Liu, Jiewei Jiang, Wangting Li, Shuai Wang, Lin Liu, Xiaojing Zhou, Liming Wang

Open access · goldAbstract read
In one paragraph

Article in Journal of translational medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
5.8field-weighted citation impact, top 4% 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

13 citing papers in PubMed, 43 citations in OpenAlex.

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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

8 authors at 3 institutions in 1 country.

Kai ZhangSchool of Computer Science and Technology, Xidian University, No.2 South Taibai Rd, Xi'an, 710071, China.
Xiyang LiuSchool of Computer Science and Technology, Xidian University, No.2 South Taibai Rd, Xi'an, 710071, China. xyliu@xidian.edu.cn.
Jiewei JiangSchool of Computer Science and Technology, Xidian University, No.2 South Taibai Rd, Xi'an, 710071, China.
Wangting LiState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China.
Shuai WangSchool of Software, Xidian University, Xi'an, 710071, China.
Lin LiuSchool of Computer Science and Technology, Xidian University, No.2 South Taibai Rd, Xi'an, 710071, China.
Xiaojing ZhouSchool of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China.
Liming WangSchool of Computer Science and Technology, Xidian University, No.2 South Taibai Rd, Xi'an, 710071, China.
Xidian University · CNSun Yat-sen University · CNNorthwestern Polytechnical University · CN

Funding

Guangdong Provincial Natural Science Foundation 2013B020400003Guangdong Provincial Natural Science Foundation 2014TQ01R573Guangdong Provincial Natural Science Foundation YQ2015006National Key Research and Development Program of China 2018YFC0116500NSFC 61472311NSFC 91546101
6 · The paper itself

Abstract

backgroundThe common treatment for pediatric cataracts is to replace the cloudy lens with an artificial one. However, patients may suffer complications (severe lens proliferation into the visual axis and abnormal high intraocular pressure; SLPVA and AHIP) within 1 year after surgery and factors causing these complications are unknown.

methodsApriori algorithm is employed to find association rules related to complications. We use random forest (RF) and Naïve Bayesian (NB) to predict the complications with datasets preprocessed by SMOTE (synthetic minority oversampling technique). Genetic feature selection is exploited to find real features related to complications.

resultsAverage classification accuracies in three binary classification problems are over 75%. Second, the relationship between the classification performance and the number of random forest tree is studied. Results show except for gender and age at surgery (AS); other attributes are related to complications. Except for the secondary IOL placement, operation mode, AS and area of cataracts; other attributes are related to SLPVA. Except for the gender, operation mode, and laterality; other attributes are related to the AHIP. Next, the association rules related to the complications are mined out. Then additional 50 data were used to test the performance of RF and NB, both of then obtained the accuracies of over 65% for three classification problems. Finally, we developed a webserver to assist doctors.

conclusionsThe postoperative complications of pediatric cataracts patients can be predicted. Then the factors related to the complications are found. Finally, the association rules that is about the complications can provide reference to doctors.

Indexed as

Data MiningAlgorithmsArea Under CurveBayes TheoremCataract ExtractionChildChild, PreschoolClinical Decision-MakingFemaleHumansInfantMalePostoperative ComplicationsPrognosisROC CurveAssociation rules miningGenetic feature selectionMedical decision making systemNaïve BayesianRandom forest

Identifiers

PMID30602368
PMCPMC6317183
OpenAlexW2908170048

What OpenQuestion holds

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LicenceCC BY
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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.