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

AlSaad, Rawan; Malluhi, Qutaibah M.; Boughorbel, Sabri

doi:10.1186/s13040-022-00289-8

Cited by 11 publications

(7 citation statements)

References 48 publications

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“…Compared to previously presented methods for the prediction of PTB, the results in terms of ROC-AUC have been similar. However, we notice an increase of the PR-AUC to ~0.72 compared to other applications [7,8] with the ensemble methods in our models.…”

Section: Discussion and Future Workmentioning

confidence: 52%

“…Taking into account the pregnant woman's medical history, current pregnancy information, and predictive clinical tests, the QUiPP application is able to predict her likelihood of giving PTB within clinically significant timeframes. In another study [7], authors proposed the PredictPTB model, a deep learning model, similarly able to predict PTB using routinely collected data from electronic health records (EHRs). Moreover, an intelligent mechanism based on the SVM algorithm has been introduced [8], capable of predicting PTB among others.…”

Section: Introductionmentioning

confidence: 99%

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Towards an Explainable AI-Based Tool to Predict Preterm Birth

Kokkinidis

Logaras

Rigas

et al. 2023

Caring Is Sharing – Exploiting the Value in Data for Health and Innovation

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Preterm birth (PTB) is defined as delivery occurring before 37 weeks of gestation. In this paper, Artificial Intelligence (AI)-based predictive models are adapted to accurately estimate the probability of PTB. In doing so, pregnant women’ objective results and variables extracted from the screening procedure in combination with demographics, medical history, social history, and other medical data are used. A dataset consisting of 375 pregnant women is used and a number of alternative Machine Learning (ML) algorithms are applied to predict PTB. The ensemble voting model produced the best results across all performance metrics with an area under the curve (ROC-AUC) of approximately 0.84 and a precision–recall curve (PR-AUC) of approximately 0.73. An attempt to provide clinicians with an explanation of the prediction is performed to increase trustworthiness.

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Section: Discussion and Future Workmentioning

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Towards an Explainable AI-Based Tool to Predict Preterm Birth

Kokkinidis

Logaras

Rigas

et al. 2023

Caring Is Sharing – Exploiting the Value in Data for Health and Innovation

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“…Consequently, RETAIN can detect both influential nurse visits and clinical features [16]. Several studies have demonstrated the clinical utility of the RETAIN model in diverse clinical contexts [18][19][20][21][22][23]. AlSaad et al [21] have shown a simplified version of the RETAIN architecture that significantly predicted preterm birth and enabled individual-level prediction explanations at the visitation level and medical code level (International Classification of Diseases, Ninth Revision [ICD-9] or ICD-10 codes).…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have demonstrated the clinical utility of the RETAIN model in diverse clinical contexts [18][19][20][21][22][23]. AlSaad et al [21] have shown a simplified version of the RETAIN architecture that significantly predicted preterm birth and enabled individual-level prediction explanations at the visitation level and medical code level (International Classification of Diseases, Ninth Revision [ICD-9] or ICD-10 codes). Rasmy et al [23] have adapted a language model that combined the RETAIN model with two independent EHR databases; this model achieved a high degree of accuracy in predicting both heart failure and the onset of pancreatic cancer.…”

Section: Introductionmentioning

confidence: 99%

Predicting Postoperative Hospital Stays Using Nursing Narratives and the Reverse Time Attention (RETAIN) Model: Retrospective Cohort Study

Han,

Kim,

et al. 2023

JMIR Med Inform

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Background Nursing narratives are an intriguing feature in the prediction of short-term clinical outcomes. However, it is unclear which nursing narratives significantly impact the prediction of postoperative length of stay (LOS) in deep learning models. Objective Therefore, we applied the Reverse Time Attention (RETAIN) model to predict LOS, entering nursing narratives as the main input. Methods A total of 354 patients who underwent ovarian cancer surgery at the Seoul National University Bundang Hospital from 2014 to 2020 were retrospectively enrolled. Nursing narratives collected within 3 postoperative days were used to predict prolonged LOS (≥10 days). The physician’s assessment was conducted based on a retrospective review of the physician’s note within the same period of the data model used. Results The model performed better than the physician’s assessment (area under the receiver operating curve of 0.81 vs 0.58; P=.02). Nursing narratives entered on the first day were the most influential predictors in prolonged LOS. The likelihood of prolonged LOS increased if the physician had to check the patient often and if the patient received intravenous fluids or intravenous patient-controlled analgesia late. Conclusions The use of the RETAIN model on nursing narratives predicted postoperative LOS effectively for patients who underwent ovarian cancer surgery. These findings suggest that accurate and interpretable deep learning information obtained shortly after surgery may accurately predict prolonged LOS.

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“…Identifying mothers that are at a higher risk through quantifying risk factors of preterm delivery at a population level helps clinicians to take preventive measures and mitigate the risks [ 12 , 13 ]. Traditionally, the identification of such risk factors is done through prospective studies.…”

Section: Introductionmentioning

confidence: 99%

Analysis of risk factors progression of preterm delivery using electronic health records

et al. 2022

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Background Preterm deliveries have many negative health implications on both mother and child. Identifying the population level factors that increase the risk of preterm deliveries is an important step in the direction of mitigating the impact and reducing the frequency of occurrence of preterm deliveries. The purpose of this work is to identify preterm delivery risk factors and their progression throughout the pregnancy from a large collection of Electronic Health Records (EHR). Results The study cohort includes about 60,000 deliveries in the USA with the complete medical history from EHR for diagnoses, medications and procedures. We propose a temporal analysis of risk factors by estimating and comparing risk ratios and variable importance at different time points prior to the delivery event. We selected the following time points before delivery: 0, 12 and 24 week(s) of gestation. We did so by conducting a retrospective cohort study of patient history for a selected set of mothers who delivered preterm and a control group of mothers that delivered full-term. We analyzed the extracted data using logistic regression and random forests models. The results of our analyses showed that the highest risk ratio and variable importance corresponds to history of previous preterm delivery. Other risk factors were identified, some of which are consistent with those that are reported in the literature, others need further investigation. Conclusions The comparative analysis of the risk factors at different time points showed that risk factors in the early pregnancy related to patient history and chronic condition, while the risk factors in late pregnancy are specific to the current pregnancy. Our analysis unifies several previously reported studies on preterm risk factors. It also gives important insights on the changes of risk factors in the course of pregnancy. The code used for data analysis will be made available on github.

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

Cited by 11 publications

References 48 publications

Towards an Explainable AI-Based Tool to Predict Preterm Birth

Towards an Explainable AI-Based Tool to Predict Preterm Birth

Predicting Postoperative Hospital Stays Using Nursing Narratives and the Reverse Time Attention (RETAIN) Model: Retrospective Cohort Study

Analysis of risk factors progression of preterm delivery using electronic health records

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