FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4210249065> ?p ?o ?g. }

• W4210249065 abstract "<sec> <title>BACKGROUND</title> Preeclampsia and intrauterine growth restriction are placental dysfunction–related disorders (PDDs) that require a referral decision be made within a certain time period. An appropriate prediction model should be developed for these diseases. However, previous models did not demonstrate robust performances and/or they were developed from datasets with highly imbalanced classes. </sec> <sec> <title>OBJECTIVE</title> In this study, we developed a predictive model of PDDs by machine learning that uses features at 24-37 weeks’ gestation, including maternal characteristics, uterine artery (UtA) Doppler measures, soluble fms-like tyrosine kinase receptor-1 (sFlt-1), and placental growth factor (PlGF). </sec> <sec> <title>METHODS</title> A public dataset was taken from a prospective cohort study that included pregnant women with PDDs (66/95, 69%) and a control group (29/95, 31%). Preliminary selection of features was based on a statistical analysis using SAS 9.4 (SAS Institute). We used Weka (Waikato Environment for Knowledge Analysis) 3.8.3 (The University of Waikato, Hamilton, NZ) to automatically select the best model using its optimization algorithm. We also manually selected the best of 23 white-box models. Models, including those from recent studies, were also compared by interval estimation of evaluation metrics. We used the Matthew correlation coefficient (MCC) as the main metric. It is not overoptimistic to evaluate the performance of a prediction model developed from a dataset with a class imbalance. Repeated 10-fold cross-validation was applied. </sec> <sec> <title>RESULTS</title> The classification via regression model was chosen as the best model. Our model had a robust MCC (.93, 95% CI .87-1.00, vs .64, 95% CI .57-.71) and specificity (100%, 95% CI 100-100, vs 90%, 95% CI 90-90) compared to each metric of the best models from recent studies. The sensitivity of this model was not inferior (95%, 95% CI 91-100, vs 100%, 95% CI 92-100). The area under the receiver operating characteristic curve was also competitive (0.970, 95% CI 0.966-0.974, vs 0.987, 95% CI 0.980-0.994). Features in the best model were maternal weight, BMI, pulsatility index of the UtA, sFlt-1, and PlGF. The most important feature was the sFlt-1/PlGF ratio. This model used an M5P algorithm consisting of a decision tree and four linear models with different thresholds. Our study was also better than the best ones among recent studies in terms of the class balance and the size of the case class (66/95, 69%, vs 27/239, 11.3%). </sec> <sec> <title>CONCLUSIONS</title> Our model had a robust predictive performance. It was also developed to deal with the problem of a class imbalance. In the context of clinical management, this model may improve maternal mortality and neonatal morbidity and reduce health care costs. </sec>" @default.
• W4210249065 created "2022-02-08" @default.
• W4210249065 creator A5076017001 @default.
• W4210249065 creator A5081102186 @default.
• W4210249065 creator A5088672342 @default.
• W4210249065 date "2019-07-10" @default.
• W4210249065 modified "2023-09-30" @default.
• W4210249065 title "Prediction of Preeclampsia and Intrauterine Growth Restriction: Development of Machine Learning Models on a Prospective Cohort (Preprint)" @default.
• W4210249065 cites W1154833837 @default.
• W4210249065 cites W1789226101 @default.
• W4210249065 cites W1975775758 @default.
• W4210249065 cites W2092155853 @default.
• W4210249065 cites W2134543020 @default.
• W4210249065 cites W2145917688 @default.
• W4210249065 cites W2179914493 @default.
• W4210249065 cites W2356882517 @default.
• W4210249065 cites W2520664488 @default.
• W4210249065 cites W2551314871 @default.
• W4210249065 cites W2562251009 @default.
• W4210249065 cites W2569868133 @default.
• W4210249065 cites W2587482667 @default.
• W4210249065 cites W2593580368 @default.
• W4210249065 cites W2755916764 @default.
• W4210249065 cites W2765898078 @default.
• W4210249065 cites W2767897274 @default.
• W4210249065 cites W2771169143 @default.
• W4210249065 cites W2771340828 @default.
• W4210249065 cites W2790948098 @default.
• W4210249065 cites W2804462245 @default.
• W4210249065 cites W2806748015 @default.
• W4210249065 cites W2900398263 @default.
• W4210249065 cites W2900915766 @default.
• W4210249065 cites W2903359978 @default.
• W4210249065 cites W3020830612 @default.
• W4210249065 cites W3022297674 @default.
• W4210249065 doi "https://doi.org/10.2196/preprints.15411" @default.
• W4210249065 hasPublicationYear "2019" @default.
• W4210249065 type Work @default.
• W4210249065 citedByCount "0" @default.
• W4210249065 crossrefType "posted-content" @default.
• W4210249065 hasAuthorship W4210249065A5076017001 @default.
• W4210249065 hasAuthorship W4210249065A5081102186 @default.
• W4210249065 hasAuthorship W4210249065A5088672342 @default.
• W4210249065 hasBestOaLocation W42102490652 @default.
• W4210249065 hasConcept C119857082 @default.
• W4210249065 hasConcept C126322002 @default.
• W4210249065 hasConcept C154945302 @default.
• W4210249065 hasConcept C188816634 @default.
• W4210249065 hasConcept C41008148 @default.
• W4210249065 hasConcept C71924100 @default.
• W4210249065 hasConceptScore W4210249065C119857082 @default.
• W4210249065 hasConceptScore W4210249065C126322002 @default.
• W4210249065 hasConceptScore W4210249065C154945302 @default.
• W4210249065 hasConceptScore W4210249065C188816634 @default.
• W4210249065 hasConceptScore W4210249065C41008148 @default.
• W4210249065 hasConceptScore W4210249065C71924100 @default.
• W4210249065 hasLocation W42102490651 @default.
• W4210249065 hasLocation W42102490652 @default.
• W4210249065 hasOpenAccess W4210249065 @default.
• W4210249065 hasPrimaryLocation W42102490651 @default.
• W4210249065 hasRelatedWork W2748952813 @default.
• W4210249065 hasRelatedWork W2899084033 @default.
• W4210249065 hasRelatedWork W2961085424 @default.
• W4210249065 hasRelatedWork W3046775127 @default.
• W4210249065 hasRelatedWork W4205958290 @default.
• W4210249065 hasRelatedWork W4285260836 @default.
• W4210249065 hasRelatedWork W4286629047 @default.
• W4210249065 hasRelatedWork W4306321456 @default.
• W4210249065 hasRelatedWork W4306674287 @default.
• W4210249065 hasRelatedWork W4224009465 @default.
• W4210249065 isParatext "false" @default.
• W4210249065 isRetracted "false" @default.
• W4210249065 workType "article" @default.