PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks
<h3>Background</h3><p dir="ltr">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...
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| مؤلفون آخرون: | , |
| منشور في: |
2022
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513519057108992 |
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| author | Rawan AlSaad (14159019) |
| author2 | Qutaibah Malluhi (3158757) Sabri Boughorbel (846228) |
| author2_role | author author |
| author_facet | Rawan AlSaad (14159019) Qutaibah Malluhi (3158757) Sabri Boughorbel (846228) |
| author_role | author |
| dc.creator.none.fl_str_mv | Rawan AlSaad (14159019) Qutaibah Malluhi (3158757) Sabri Boughorbel (846228) |
| dc.date.none.fl_str_mv | 2022-02-14T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1186/s13040-022-00289-8 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/PredictPTB_an_interpretable_preterm_birth_prediction_model_using_attention-based_recurrent_neural_networks/25659111 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Biochemistry and cell biology Genetics Mathematical sciences Numerical and computational mathematics Deep learning Predictive models Attention mechanism Electronic health record Preterm birth Pregnancy |
| dc.title.none.fl_str_mv | PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">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.</p><h3>Methods</h3><p dir="ltr">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.</p><h3>Results</h3><p dir="ltr">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).</p><h3>Conclusions</h3><p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: BioData Mining<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s13040-022-00289-8" target="_blank">https://dx.doi.org/10.1186/s13040-022-00289-8</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_0e5a295d6d1c4c9f5c3707f7caadc979 |
| identifier_str_mv | 10.1186/s13040-022-00289-8 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25659111 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networksRawan AlSaad (14159019)Qutaibah Malluhi (3158757)Sabri Boughorbel (846228)Biological sciencesBiochemistry and cell biologyGeneticsMathematical sciencesNumerical and computational mathematicsDeep learningPredictive modelsAttention mechanismElectronic health recordPreterm birthPregnancy<h3>Background</h3><p dir="ltr">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.</p><h3>Methods</h3><p dir="ltr">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.</p><h3>Results</h3><p dir="ltr">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).</p><h3>Conclusions</h3><p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: BioData Mining<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s13040-022-00289-8" target="_blank">https://dx.doi.org/10.1186/s13040-022-00289-8</a></p>2022-02-14T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1186/s13040-022-00289-8https://figshare.com/articles/journal_contribution/PredictPTB_an_interpretable_preterm_birth_prediction_model_using_attention-based_recurrent_neural_networks/25659111CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/256591112022-02-14T03:00:00Z |
| spellingShingle | PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks Rawan AlSaad (14159019) Biological sciences Biochemistry and cell biology Genetics Mathematical sciences Numerical and computational mathematics Deep learning Predictive models Attention mechanism Electronic health record Preterm birth Pregnancy |
| status_str | publishedVersion |
| 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 | Biological sciences Biochemistry and cell biology Genetics Mathematical sciences Numerical and computational mathematics Deep learning Predictive models Attention mechanism Electronic health record Preterm birth Pregnancy |