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PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks

BACKGROUND: Early identification of pregnant women at risk for preterm birth (PTB), a major cause of infant mortality and morbidity, has a significant potential to improve prenatal care. However, we lack effective predictive models which can accurately forecast PTB and complement these predictions w...

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Detalles Bibliográficos
Autores principales: AlSaad, Rawan, Malluhi, Qutaibah, Boughorbel, Sabri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842907/
https://www.ncbi.nlm.nih.gov/pubmed/35164820
http://dx.doi.org/10.1186/s13040-022-00289-8
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author AlSaad, Rawan
Malluhi, Qutaibah
Boughorbel, Sabri
author_facet AlSaad, Rawan
Malluhi, Qutaibah
Boughorbel, Sabri
author_sort AlSaad, Rawan
collection PubMed
description BACKGROUND: Early identification of pregnant women at risk for preterm birth (PTB), a major cause of infant mortality and morbidity, has a significant potential to improve prenatal care. However, we lack effective predictive models which can accurately forecast PTB and complement these predictions with appropriate interpretations for clinicians. In this work, we introduce a clinical prediction model (PredictPTB) which combines variables (medical codes) readily accessible through electronic health record (EHR) to accurately predict the risk of preterm birth at 1, 3, 6, and 9 months prior to delivery. METHODS: The architecture of PredictPTB employs recurrent neural networks (RNNs) to model the longitudinal patient’s EHR visits and exploits a single code-level attention mechanism to improve the predictive performance, while providing temporal code-level and visit-level explanations for the prediction results. We compare the performance of different combinations of prediction time-points, data modalities, and data windows. We also present a case-study of our model’s interpretability illustrating how clinicians can gain some transparency into the predictions. RESULTS: Leveraging a large cohort of 222,436 deliveries, comprising a total of 27,100 unique clinical concepts, our model was able to predict preterm birth with an ROC-AUC of 0.82, 0.79, 0.78, and PR-AUC of 0.40, 0.31, 0.24, at 1, 3, and 6 months prior to delivery, respectively. Results also confirm that observational data modalities (such as diagnoses) are more predictive for preterm birth than interventional data modalities (e.g., medications and procedures). CONCLUSIONS: Our results demonstrate that PredictPTB can be utilized to achieve accurate and scalable predictions for preterm birth, complemented by explanations that directly highlight evidence in the patient’s EHR timeline.
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spelling pubmed-88429072022-02-16 PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks AlSaad, Rawan Malluhi, Qutaibah Boughorbel, Sabri BioData Min Research BACKGROUND: Early identification of pregnant women at risk for preterm birth (PTB), a major cause of infant mortality and morbidity, has a significant potential to improve prenatal care. However, we lack effective predictive models which can accurately forecast PTB and complement these predictions with appropriate interpretations for clinicians. In this work, we introduce a clinical prediction model (PredictPTB) which combines variables (medical codes) readily accessible through electronic health record (EHR) to accurately predict the risk of preterm birth at 1, 3, 6, and 9 months prior to delivery. METHODS: The architecture of PredictPTB employs recurrent neural networks (RNNs) to model the longitudinal patient’s EHR visits and exploits a single code-level attention mechanism to improve the predictive performance, while providing temporal code-level and visit-level explanations for the prediction results. We compare the performance of different combinations of prediction time-points, data modalities, and data windows. We also present a case-study of our model’s interpretability illustrating how clinicians can gain some transparency into the predictions. RESULTS: Leveraging a large cohort of 222,436 deliveries, comprising a total of 27,100 unique clinical concepts, our model was able to predict preterm birth with an ROC-AUC of 0.82, 0.79, 0.78, and PR-AUC of 0.40, 0.31, 0.24, at 1, 3, and 6 months prior to delivery, respectively. Results also confirm that observational data modalities (such as diagnoses) are more predictive for preterm birth than interventional data modalities (e.g., medications and procedures). CONCLUSIONS: Our results demonstrate that PredictPTB can be utilized to achieve accurate and scalable predictions for preterm birth, complemented by explanations that directly highlight evidence in the patient’s EHR timeline. BioMed Central 2022-02-14 /pmc/articles/PMC8842907/ /pubmed/35164820 http://dx.doi.org/10.1186/s13040-022-00289-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
AlSaad, Rawan
Malluhi, Qutaibah
Boughorbel, Sabri
PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks
title PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks
title_full PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks
title_fullStr PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks
title_full_unstemmed PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks
title_short PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks
title_sort predictptb: an interpretable preterm birth prediction model using attention-based recurrent neural networks
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842907/
https://www.ncbi.nlm.nih.gov/pubmed/35164820
http://dx.doi.org/10.1186/s13040-022-00289-8
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