Time-to-Birth Prediction Models and the Influence of Expert Opinions

Vandewiele, Gilles; Dehaene, Isabelle; Janssens, Olivier; Ongenae, Femke; Backere, Femke De; Turck, Filip De; Roelens, Kristien; Hoecke, Sofie Van; Demeester, Thomas

doi:10.1007/978-3-030-21642-9_36

Cited by 3 publications

(5 citation statements)

References 16 publications

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“…Of those 16, 3 articles were not eligible due to not studying/developing an RSS ( n = 1) and not analyzing the RSS predictive value of PTB ( n = 2). Therefore, in total, 56 studies [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 ] were included in this review. Figure 1 shows the PRISMA flow diagram, demonstrating the study selection process.…”

Section: Resultsmentioning

confidence: 99%

Risk Scoring Systems for Preterm Birth and Their Performance: A Systematic Review

Ferreira

Bernardes

Gonçalves

2023

JCM

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Introduction: Nowadays, the risk stratification of preterm birth (PTB) and its prediction remain a challenge. Many risk factors associated with PTB have been identified, and risk scoring systems (RSSs) have been developed to face this challenge. The objectives of this systematic review were to identify RSSs for PTB, the variables they consist of, and their performance. Materials and methods: Two databases were searched, and two authors independently performed the screening and eligibility phases. Records studying an RSS, based on specified variables, with an evaluation of the predictive value for PTB, were considered eligible. Reference lists of eligible studies and review articles were also searched. Data from the included studies were extracted. Results: A total of 56 studies were included in this review. The most frequently incorporated variables in the RSS included in this review were maternal age, weight, history of smoking, history of previous PTB, and cervical length. The performance measures varied widely among the studies, with sensitivity ranging between 4.2% and 92.0% and area under the curve (AUC) between 0.59 and 0.95. Conclusions: Despite the recent technological and scientifical evolution with a better understanding of variables related to PTB and the definition of new ultrasonographic parameters and biomarkers associated with PTB, the RSS’s ability to predict PTB remains poor in most situations, thus compromising the integration of a single RSS in clinical practice. The development of new RSSs, the identification of new variables associated with PTB, and the elaboration of a large reference dataset might be a step forward to tackle the problem of PTB.

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Section: Resultsmentioning

confidence: 99%

Risk Scoring Systems for Preterm Birth and Their Performance: A Systematic Review

Ferreira

Bernardes

Gonçalves

2023

JCM

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“…Mortality and long-term complications can be prevented with cost-effective interventions, however, because the pathophysiology and etiology of preterm labor are not yet fully understood, deciding timely on the appropriate intervention is hard. In this setting, clinical decision support models can be important for helping clinicians to identify women at higher risk of premature delivery, so that they can offer prophylactic interventions and help guide antenatal management decisions, much of which depend on the estimated timeto-delivery [9,10,11,12].…”

Section: Decision Support For Preterm Birthmentioning

confidence: 99%

“…Several efforts have already been made to assess the potential of predictive models of PTB risk [30,31,32,33,34]. These models are based on a large number of variables, including the gestational age, clinical history, cervical length, blood pressure, results of biomarker tests, and many others.…”

Section: Related Workmentioning

confidence: 99%

Clinical information extraction for preterm birth risk prediction

Sterckx

Vandewiele

Dehaene

et al. 2020

Journal of Biomedical Informatics

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This paper contributes to the pursuit of leveraging unstructured medical notes to structured clinical decision making. In particular, we present a pipeline for clinical information extraction from medical notes related to preterm birth, and discuss the main challenges as well as its potential for clinical practice. A large collection of medical notes, created by staff during hospitalizations of patients who were at risk of delivering preterm, was gathered and analyzed. Based on an annotated collection of notes, we trained and evaluated information extraction components to discover clinical entities such as symptoms, events, anatomical sites and procedures, as well as attributes linked to these clinical entities. In a retrospective study, we show that these are highly informative for clinical decision support models that are trained to predict whether delivery is likely to occur within specific time windows, in combination with structured information from electronic health records.

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“…The code is available on Github under an open license 6 . The TPEHGDB dataset is available from Physionet 7 .…”

Section: Conflict Of Interest Statementmentioning

confidence: 99%

“…As preterm labor is currently not yet fully understood, a gynecologist experiences difficulties to assess whether a patient presenting with symptoms suggestive for preterm labor, will actually deliver preterm or not. In order to support these experts in their assessment, several studies have already investigated the added value of a predictive model [2,3,4,5,6]. These models are based on a large number of variables extracted from clinical sources, such as the electronic health record, including the gestational age, results of a biomarker, cervical length, clinical history.…”

Section: Introductionmentioning

confidence: 99%

Overly Optimistic Prediction Results on Imbalanced Data: a Case Study of Flaws and Benefits when Applying Over-sampling

Vandewiele,

Dehaene,

Kovács

et al. 2020

Preprint

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Information extracted from electrohysterography recordings could potentially prove to be an interesting additional source of information to estimate the risk on preterm birth. Recently, a large number of studies have reported near-perfect results to distinguish between recordings of patients that will deliver term or preterm using a public resource, called the Term/Preterm Electrohysterogram database. However, we argue that these results are overly optimistic due to a methodological flaw being made. In this work, we focus on one specific type of methodological flaw: applying over-sampling before partitioning the data into mutually exclusive training and testing sets. We show how this causes the results to be biased using two artificial datasets and reproduce results of studies in which this flaw was identified. Moreover, we evaluate the actual impact of over-sampling on predictive performance, when applied prior to data partitioning, using the same methodologies of related studies, to provide a realistic view of these methodologies' generalization capabilities. We make our research reproducible by providing all the code under an open license.

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Time-to-Birth Prediction Models and the Influence of Expert Opinions

Cited by 3 publications

References 16 publications

Risk Scoring Systems for Preterm Birth and Their Performance: A Systematic Review

Risk Scoring Systems for Preterm Birth and Their Performance: A Systematic Review

Clinical information extraction for preterm birth risk prediction

Overly Optimistic Prediction Results on Imbalanced Data: a Case Study of Flaws and Benefits when Applying Over-sampling

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