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Li X, Cai QY, Luo X, Wang YH, Shao LZ, Luo SJ, Wang L, Wang YX, Lan X, Liu TH. Gestational diabetes mellitus aggravates adverse perinatal outcomes in women with intrahepatic cholestasis of pregnancy. Diabetol Metab Syndr 2024; 16:57. [PMID: 38429774 PMCID: PMC10908036 DOI: 10.1186/s13098-024-01294-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 02/17/2024] [Indexed: 03/03/2024] Open
Abstract
PURPOSE To evaluate the effect of intrahepatic cholestasis of pregnancy (ICP) with gestational diabetes mellitus (GDM) on perinatal outcomes and establish a prediction model of adverse perinatal outcomes in women with ICP. METHODS This multicenter retrospective cohort study included the clinical data of 2,178 pregnant women with ICP, including 1,788 women with ICP and 390 co-occurrence ICP and GDM. The data of all subjects were collected from hospital electronic medical records. Univariate and multivariate logistic regression analysis were used to compare the incidence of perinatal outcomes between ICP with GDM group and ICP alone group. RESULTS Baseline characteristics of the population revealed that maternal age (p < 0.001), pregestational weight (p = 0.01), pre-pregnancy BMI (p < 0.001), gestational weight gain (p < 0.001), assisted reproductive technology (ART) (p < 0.001), and total bile acid concentration (p = 0.024) may be risk factors for ICP with GDM. Furthermore, ICP with GDM demonstrated a higher association with both polyhydramnios (OR 2.66) and preterm labor (OR 1.67) compared to ICP alone. Further subgroup analysis based on the severity of ICP showed that elevated total bile acid concentrations were closely associated with an increased risk of preterm labour, meconium-stained amniotic fluid, and low birth weight in both ICP alone and ICP with GDM groups. ICP with GDM further worsened these outcomes, especially in women with severe ICP. The nomogram prediction model effectively predicted the occurrence of preterm labour in the ICP population. CONCLUSIONS ICP with GDM may result in more adverse pregnancy outcomes, which are associated with bile acid concentrations.
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Affiliation(s)
- Xia Li
- Department of Bioinformatics, School of Basic Medical Sciences , Chongqing Medical University, No.1 Yixueyuan Rd, Yuzhong District, 400016, Chongqing, China
- Joint International Research Laboratory of Reproduction & Development, Chongqing Medical University, 400016, Chongqing, China
| | - Qin-Yu Cai
- Joint International Research Laboratory of Reproduction & Development, Chongqing Medical University, 400016, Chongqing, China
- Department of Obstetrics, Women and Children's Hospital of Chongqing Medical University, 401147, Chongqing, China
| | - Xin Luo
- Joint International Research Laboratory of Reproduction & Development, Chongqing Medical University, 400016, Chongqing, China
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, 400016, Chongqing, China
| | - Yong-Heng Wang
- Department of Bioinformatics, School of Basic Medical Sciences , Chongqing Medical University, No.1 Yixueyuan Rd, Yuzhong District, 400016, Chongqing, China
- Joint International Research Laboratory of Reproduction & Development, Chongqing Medical University, 400016, Chongqing, China
| | - Li-Zhen Shao
- Department of Bioinformatics, School of Basic Medical Sciences , Chongqing Medical University, No.1 Yixueyuan Rd, Yuzhong District, 400016, Chongqing, China
- Joint International Research Laboratory of Reproduction & Development, Chongqing Medical University, 400016, Chongqing, China
| | - Shu-Juan Luo
- Department of Obstetrics, Women and Children's Hospital of Chongqing Medical University, 401147, Chongqing, China
| | - Lan Wang
- Department of Obstetrics, Women and Children's Hospital of Chongqing Medical University, 401147, Chongqing, China
| | - Ying-Xiong Wang
- Department of Bioinformatics, School of Basic Medical Sciences , Chongqing Medical University, No.1 Yixueyuan Rd, Yuzhong District, 400016, Chongqing, China
- Joint International Research Laboratory of Reproduction & Development, Chongqing Medical University, 400016, Chongqing, China
| | - Xia Lan
- Department of Obstetrics, Women and Children's Hospital of Chongqing Medical University, 401147, Chongqing, China.
| | - Tai-Hang Liu
- Department of Bioinformatics, School of Basic Medical Sciences , Chongqing Medical University, No.1 Yixueyuan Rd, Yuzhong District, 400016, Chongqing, China.
- Joint International Research Laboratory of Reproduction & Development, Chongqing Medical University, 400016, Chongqing, China.
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Yu QY, Lin Y, Zhou YR, Yang XJ, Hemelaar J. Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms. Front Big Data 2024; 7:1291196. [PMID: 38495848 PMCID: PMC10941650 DOI: 10.3389/fdata.2024.1291196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 02/12/2024] [Indexed: 03/19/2024] Open
Abstract
We aimed to develop, train, and validate machine learning models for predicting preterm birth (<37 weeks' gestation) in singleton pregnancies at different gestational intervals. Models were developed based on complete data from 22,603 singleton pregnancies from a prospective population-based cohort study that was conducted in 51 midwifery clinics and hospitals in Wenzhou City of China between 2014 and 2016. We applied Catboost, Random Forest, Stacked Model, Deep Neural Networks (DNN), and Support Vector Machine (SVM) algorithms, as well as logistic regression, to conduct feature selection and predictive modeling. Feature selection was implemented based on permutation-based feature importance lists derived from the machine learning models including all features, using a balanced training data set. To develop prediction models, the top 10%, 25%, and 50% most important predictive features were selected. Prediction models were developed with the training data set with 5-fold cross-validation for internal validation. Model performance was assessed using area under the receiver operating curve (AUC) values. The CatBoost-based prediction model after 26 weeks' gestation performed best with an AUC value of 0.70 (0.67, 0.73), accuracy of 0.81, sensitivity of 0.47, and specificity of 0.83. Number of antenatal care visits before 24 weeks' gestation, aspartate aminotransferase level at registration, symphysis fundal height, maternal weight, abdominal circumference, and blood pressure emerged as strong predictors after 26 completed weeks. The application of machine learning on pregnancy surveillance data is a promising approach to predict preterm birth and we identified several modifiable antenatal predictors.
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Affiliation(s)
- Qiu-Yan Yu
- National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Department of Preventive Medicine, School of Public Health, Wenzhou Medical University, Wenzhou, China
| | - Ying Lin
- Wenzhou Women and Children Health Guidance Center, Wenzhou, China
| | - Yu-Run Zhou
- Wenzhou Women and Children Health Guidance Center, Wenzhou, China
| | - Xin-Jun Yang
- Department of Preventive Medicine, School of Public Health, Wenzhou Medical University, Wenzhou, China
| | - Joris Hemelaar
- National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
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Sullivan BA, Beam K, Vesoulis ZA, Aziz KB, Husain AN, Knake LA, Moreira AG, Hooven TA, Weiss EM, Carr NR, El-Ferzli GT, Patel RM, Simek KA, Hernandez AJ, Barry JS, McAdams RM. Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities. J Perinatol 2024; 44:1-11. [PMID: 38097685 PMCID: PMC10872325 DOI: 10.1038/s41372-023-01848-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/21/2023] [Accepted: 11/30/2023] [Indexed: 12/17/2023]
Abstract
Artificial intelligence (AI) offers tremendous potential to transform neonatology through improved diagnostics, personalized treatments, and earlier prevention of complications. However, there are many challenges to address before AI is ready for clinical practice. This review defines key AI concepts and discusses ethical considerations and implicit biases associated with AI. Next we will review literature examples of AI already being explored in neonatology research and we will suggest future potentials for AI work. Examples discussed in this article include predicting outcomes such as sepsis, optimizing oxygen therapy, and image analysis to detect brain injury and retinopathy of prematurity. Realizing AI's potential necessitates collaboration between diverse stakeholders across the entire process of incorporating AI tools in the NICU to address testability, usability, bias, and transparency. With multi-center and multi-disciplinary collaboration, AI holds tremendous potential to transform the future of neonatology.
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Affiliation(s)
- Brynne A Sullivan
- Division of Neonatology, Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Kristyn Beam
- Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Zachary A Vesoulis
- Division of Newborn Medicine, Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA
| | - Khyzer B Aziz
- Division of Neonatology, Department of Pediatrics, Johns Hopkins University, Baltimore, MD, USA
| | - Ameena N Husain
- Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Lindsey A Knake
- Division of Neonatology, Department of Pediatrics, University of Iowa, Iowa City, IA, USA
| | - Alvaro G Moreira
- Division of Neonatology, Department of Pediatrics, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Thomas A Hooven
- Division of Newborn Medicine, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Elliott M Weiss
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
- Treuman Katz Center for Pediatric Bioethics and Palliative Care, Seattle Children's Research Institute, Seattle, WA, USA
| | - Nicholas R Carr
- Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - George T El-Ferzli
- Division of Neonatology, Department of Pediatrics, Ohio State University, Nationwide Children's Hospital, Columbus, OH, USA
| | - Ravi M Patel
- Division of Neonatology, Department of Pediatrics, Emory University School of Medicine and Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Kelsey A Simek
- Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Antonio J Hernandez
- Division of Neonatology, Department of Pediatrics, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - James S Barry
- Division of Neonatology, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Ryan M McAdams
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
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Khan W, Zaki N, Ghenimi N, Ahmad A, Bian J, Masud MM, Ali N, Govender R, Ahmed LA. Predicting preterm birth using explainable machine learning in a prospective cohort of nulliparous and multiparous pregnant women. PLoS One 2023; 18:e0293925. [PMID: 38150456 PMCID: PMC10752564 DOI: 10.1371/journal.pone.0293925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 10/21/2023] [Indexed: 12/29/2023] Open
Abstract
Preterm birth (PTB) presents a complex challenge in pregnancy, often leading to significant perinatal and long-term morbidities. "While machine learning (ML) algorithms have shown promise in PTB prediction, the lack of interpretability in existing models hinders their clinical utility. This study aimed to predict PTB in a pregnant population using ML models, identify the key risk factors associated with PTB through the SHapley Additive exPlanations (SHAP) algorithm, and provide comprehensive explanations for these predictions to assist clinicians in providing appropriate care. This study analyzed a dataset of 3509 pregnant women in the United Arab Emirates and selected 35 risk factors associated with PTB based on the existing medical and artificial intelligence literature. Six ML algorithms were tested, wherein the XGBoost model exhibited the best performance, with an area under the operator receiving curves of 0.735 and 0.723 for parous and nulliparous women, respectively. The SHAP feature attribution framework was employed to identify the most significant risk factors linked to PTB. Additionally, individual patient analysis was performed using the SHAP and the local interpretable model-agnostic explanation algorithms (LIME). The overall incidence of PTB was 11.23% (11 and 12.1% in parous and nulliparous women, respectively). The main risk factors associated with PTB in parous women are previous PTB, previous cesarean section, preeclampsia during pregnancy, and maternal age. In nulliparous women, body mass index at delivery, maternal age, and the presence of amniotic infection were the most relevant risk factors. The trained ML prediction model developed in this study holds promise as a valuable screening tool for predicting PTB within this specific population. Furthermore, SHAP and LIME analyses can assist clinicians in understanding the individualized impact of each risk factor on their patients and provide appropriate care to reduce morbidity and mortality related to PTB.
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Affiliation(s)
- Wasif Khan
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, UAE
- Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, Al Ain, UAE
| | - Nazar Zaki
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, UAE
- Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, Al Ain, UAE
| | - Nadirah Ghenimi
- Department Family Medicine, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, UAE
| | - Amir Ahmad
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, UAE
- Zayed Centre for Health Sciences, United Arab Emirates University, Al Ain, UAE
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Mohammad M. Masud
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, UAE
- Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, Al Ain, UAE
- Zayed Centre for Health Sciences, United Arab Emirates University, Al Ain, UAE
| | - Nasloon Ali
- Institute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, UAE
| | - Romona Govender
- Department Family Medicine, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, UAE
| | - Luai A. Ahmed
- Zayed Centre for Health Sciences, United Arab Emirates University, Al Ain, UAE
- Institute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, UAE
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Khan W, Zaki N, Ahmad A, Masud MM, Govender R, Rojas-Perilla N, Ali L, Ghenimi N, Ahmed LA. Node embedding-based graph autoencoder outlier detection for adverse pregnancy outcomes. Sci Rep 2023; 13:19817. [PMID: 37963898 PMCID: PMC10645849 DOI: 10.1038/s41598-023-46726-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 11/04/2023] [Indexed: 11/16/2023] Open
Abstract
Adverse pregnancy outcomes, such as low birth weight (LBW) and preterm birth (PTB), can have serious consequences for both the mother and infant. Early prediction of such outcomes is important for their prevention. Previous studies using traditional machine learning (ML) models for predicting PTB and LBW have encountered two important limitations: extreme class imbalance in medical datasets and the inability to account for complex relational structures between entities. To address these limitations, we propose a node embedding-based graph outlier detection algorithm to predict adverse pregnancy outcomes. We developed a knowledge graph using a well-curated representative dataset of the Emirati population and two node embedding algorithms. The graph autoencoder (GAE) was trained by applying a combination of original risk factors and node embedding features. Samples that were difficult to reconstruct at the output of GAE were identified as outliers considered representing PTB and LBW samples. Our experiments using LBW, PTB, and very PTB datasets demonstrated that incorporating node embedding considerably improved performance, achieving a 12% higher AUC-ROC compared to traditional GAE. Our study demonstrates the effectiveness of node embedding and graph outlier detection in improving the prediction performance of adverse pregnancy outcomes in well-curated population datasets.
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Affiliation(s)
- Wasif Khan
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates
| | - Nazar Zaki
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates.
- ASPIRE Precision Medicine Research Institute Abu Dhabi (ASPIREPMRIAD), Al Ain, United Arab Emirates.
| | - Amir Ahmad
- Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates
| | - Mohammad M Masud
- Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates
| | - Romana Govender
- Department of Family Medicine, College of Medicine and Health Sciences, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates
| | - Natalia Rojas-Perilla
- Department of Analytics in the Digital Era, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates
| | - Luqman Ali
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates
| | - Nadirah Ghenimi
- Department of Family Medicine, College of Medicine and Health Sciences, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates
| | - Luai A Ahmed
- Institute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates
- Zayed Centre for Health Sciences, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates
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S H, V MA. An idiosyncratic MIMBO-NBRF based automated system for child birth mode prediction. Artif Intell Med 2023; 143:102621. [PMID: 37673564 DOI: 10.1016/j.artmed.2023.102621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 05/11/2023] [Accepted: 07/01/2023] [Indexed: 09/08/2023]
Abstract
Predicting the mode of child birth is still remains one of the most complex and challenging tasks in ancient times. Also, there is no such strong methodologies are developed in the conventional works for birth mode prediction. Therefore, the proposed work objects to develop a novel and distinct optimization based machine learning technique for creating the child birth mode prediction system. This framework includes the modules of data imputation, feature selection, classification, and prediction. Initially, the data imputation process is performed to improve the quality of dataset by normalizing the attributes and filling the missed fields. Then, the Multivariate Intensified Mine Blast Optimization (MIMBO) technique is implemented to choose the best set of features by estimating the optimal function. After that, an integrated Naïve Bayes - Random Forest (NBRF) technique is developed by incorporating the functions of conventional NB and RF techniques. The novel contribution of this technique, a Bird Mating (BM) optimization technique is used in NBRF classifier for estimating the likelihood parameter to generate the Bayesian rules. The main idea of this paper is to develop a simple as well as efficient automated system with the use of hybrid machine learning model for predicting the mode of child birth. For this purpose, advanced algorithms such as MIMBO based feature selection, and NBRF based classification are implemented in this work. Due to the inclusion of MIMBO and BM optimization techniques, the performance of classifier is greatly improved with low computational burden and increased prediction accuracy. Moreover, the combination of proposed MIMBO-NBRF technique outperforms the existing child birth prediction methods with superior results in terms of average accuracy up to 99 %. In addition, some other parameters are also estimated and compared with the existing techniques for proving the overall superiority of the proposed framework.
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Affiliation(s)
- Hemalatha S
- Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600 119, Tamilnadu, India.
| | - Maria Anu V
- Department of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamilnadu, India
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Malani SN, Shrivastava D, Raka MS. A Comprehensive Review of the Role of Artificial Intelligence in Obstetrics and Gynecology. Cureus 2023; 15:e34891. [PMID: 36925982 PMCID: PMC10013256 DOI: 10.7759/cureus.34891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 02/12/2023] [Indexed: 03/18/2023] Open
Abstract
The exponential growth of artificial intelligence (AI) has fascinated its application in various fields and so in the field of healthcare. Technological advancements in theories and learning algorithms and the availability of processing through huge datasets have created a breakthrough in the medical field with computing systems. AI can potentially drive clinicians and practitioners with appropriate decisions in managing cases and reaching a diagnosis, so its application is extensively spread in the medical field. Thus, computerized algorithms have made predictions so simple and accurate. This is because AI can proffer information accurately even to many patients. Furthermore, the subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. Despite numerous challenges, AI implementation in obstetrics and gynecology is found to have a spellbound development. Therefore, this review propounds exploring the implementation of AI in obstetrics and gynecology to improve the outcomes and clinical experience. In that context, the evolution and progress of AI, the role of AI in ultrasound diagnosis in distinct phases of pregnancy, clinical benefits, preterm birth postpartum period, and applications of AI in gynecology are elucidated in this review with future recommendations.
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Affiliation(s)
- Sagar N Malani
- Department of Obstetrics and Gynecology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Deepti Shrivastava
- Department of Obstetrics and Gynecology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Mayur S Raka
- Department of Obstetrics and Gynecology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
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Identifying the Early Signs of Preterm Birth from U.S. Birth Records Using Machine Learning Techniques. INFORMATION 2022. [DOI: 10.3390/info13070310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Preterm birth (PTB) is the leading cause of infant mortality in the U.S. and globally. The goal of this study is to increase understanding of PTB risk factors that are present early in pregnancy by leveraging statistical and machine learning (ML) techniques on big data. The 2016 U.S. birth records were obtained and combined with two other area-level datasets, the Area Health Resources File and the County Health Ranking. Then, we applied logistic regression with elastic net regularization, random forest, and gradient boosting machines to study a cohort of 3.6 million singleton deliveries to identify generalizable PTB risk factors. The response variable is preterm birth, which includes spontaneous and indicated PTB, and we performed a binary classification. Our results show that the most important predictors of preterm birth are gestational and chronic hypertension, interval since last live birth, and history of a previous preterm birth, which explains 10.92, 5.98, and 5.63% of the predictive power, respectively. Parents' education is one of the influential variables in predicting PTB, explaining 7.89% of the predictive power. The relative importance of race declines when parents are more educated or have received adequate prenatal care. The gradient boosting machines outperformed with an AUC of 0.75 (sensitivity: 0.64, specificity: 0.73) for the validation dataset. In this study, we compare our results with seminal and most related studies to demonstrate the superiority of our results. The application of ML techniques improved the performance measures in the prediction of preterm birth. The results emphasize the importance of socioeconomic factors such as parental education as one of the most important indicators of preterm birth. More research is needed on these mechanisms through which socioeconomic factors affect biological responses.
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