Goldenberg RL, Culhane JF, Iams JD, Romero R. Epidemiology and causes of preterm birth. Lancet Lond Engl. 2008;371:75–84.
Article
Google Scholar
Blencowe H, Cousens S, Oestergaard MZ, Chou D, Moller A-B, Narwal R, et al. National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications. Lancet Lond Engl. 2012;379:2162–72.
Article
Google Scholar
Barros FC, Papageorghiou AT, Victora CG, Noble JA, Pang R, Iams J, et al. The distribution of clinical phenotypes of preterm birth syndrome. JAMA Pediatr. 2015;169:220–10.
Article
PubMed
Google Scholar
Callaghan WM, MacDorman MF, Rasmussen SA, Qin C, Lackritz EM. The contribution of preterm birth to infant mortality rates in the United States. Pediatrics. 2006;118:1566–73.
Article
PubMed
Google Scholar
Liu L, Oza S, Hogan D, Chu Y, Perin J, Zhu J, et al. Global, regional, and national causes of under-5 mortality in 2000–15: an updated systematic analysis with implications for the Sustainable Development Goals. Lancet. 2016;388:3027–35.
Article
PubMed
PubMed Central
Google Scholar
Romero R, Dey SK, Fisher SJ. Preterm labor: one syndrome, many causes. Science. 2014;345:760–5.
Article
CAS
PubMed
PubMed Central
Google Scholar
Iams J, Goldenberg R, Meis P, Mercer B, Moawad A, Das A, et al. The length of the cervix and the risk of spontaneous premature delivery. New Engl J Med. 1996;334:567–73.
Article
CAS
PubMed
Google Scholar
Fuchs F, Monet B, Ducruet T, Chaillet N, Audibert F. Effect of maternal age on the risk of preterm birth: a large cohort study. PLoS One. 2018;13:e0191002.
Article
PubMed
PubMed Central
CAS
Google Scholar
Mercer BM, Goldenberg RL, Moawad AH, Meis PJ, Iams JD, Das AF, et al. The preterm prediction study: effect of gestational age and cause of preterm birth on subsequent obstetric outcome. Am J Obstet Gynecol. 1999;181:1216–21.
Article
CAS
PubMed
Google Scholar
Mazaki-Tovi S, Romero R, Kusanovic JP, Erez O, Pineles BL, Gotsch F, et al. Recurrent preterm birth. Semin Perinatol. 2007;31:142–58.
Article
PubMed
PubMed Central
Google Scholar
Ananth CV, Kirby RS, Vintzileos AM. Recurrence of preterm birth in twin pregnancies in the presence of a prior singleton preterm birth. J Maternal Fetal Neonatal Med. 2008;21:289–95.
Article
Google Scholar
Muglia LJ, Katz M. The enigma of spontaneous preterm birth. N Engl J Med. 2010;362:529–35.
Article
CAS
PubMed
Google Scholar
Auger N, Le TUN, Park AL, Luo Z-C. Association between maternal comorbidity and preterm birth by severity and clinical subtype: retrospective cohort study. BMC Pregnancy Childbirth. 2011;11:75.
Article
Google Scholar
Carter M, Fowler S, Holden A, Xenakis E, Dudley D. The late preterm birth rate and its association with comorbidities in a population-based study. Am J Perinatol. 2011;28:703–8.
Article
PubMed
Google Scholar
Francesca L, Laura M, Giuseppe R, Francesco DA, Ersilia B, Leonardo P, et al. Biomarkers for predicting spontaneous preterm birth: an umbrella systematic review. J Matern Fetal Neonatal Med. 2019;0:726–34.
Google Scholar
Dabi Y, Nedellec S, Bonneau C, Trouchard B, Rouzier R, Benachi A. Clinical validation of a model predicting the risk of preterm delivery. PLoS One. 2017;12:e0171801.
Article
PubMed
PubMed Central
CAS
Google Scholar
Ngo TTM, Moufarrej MN, Rasmussen M-LH, Camunas-Soler J, Pan W, Okamoto J, et al. Noninvasive blood tests for fetal development predict gestational age and preterm delivery. Science. 2018;360:1133–6.
Article
CAS
PubMed
PubMed Central
Google Scholar
Tarca AL, Pataki BÁ, Romero R, Sirota M, Guan Y, Kutum R, et al. Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth. Cell Rep Med. 2021;2:100323.
Article
CAS
PubMed
PubMed Central
Google Scholar
Stelzer IA, Ghaemi MS, Han X, Ando K, Hédou JJ, Feyaerts D, et al. Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset. Sci Transl Med. 2021;13:eabd9898.
Article
CAS
PubMed
PubMed Central
Google Scholar
Schaaf JM, Ravelli ACJ, Mol BWJ, Abu-Hanna A. Development of a prognostic model for predicting spontaneous singleton preterm birth. Eur J Obstet Gynecol Reprod Biol. 2012;164:150–5.
Article
PubMed
Google Scholar
Morken NH, Källen K, Jacobsson B. Predicting risk of spontaneous preterm delivery in women with a singleton pregnancy. Paediatr Perinat Epidemiol. 2014;28:11–22.
Article
PubMed
Google Scholar
Weber A, Darmstadt GL, Gruber S, Foeller ME, Carmichael SL, Stevenson DK, et al. Application of machine-learning to predict early spontaneous preterm birth among nulliparous non-Hispanic black and white women. Ann Epidemiol. 2018;28:783–789.e1.
Article
PubMed
Google Scholar
Baer RJ, McLemore MR, Adler N, Oltman SP, Chambers BD, Kuppermann M, et al. Pre-pregnancy or first-trimester risk scoring to identify women at high risk of preterm birth. Eur J Obstet Gynecol. 2018;231:235–40.
Article
Google Scholar
Tucker CM, Berrien K, Menard MK, Herring AH, Daniels J, Rowley DL, et al. Predicting preterm birth among women screened by North Carolina’s pregnancy medical home program. Matern Child Health J. 2015;19:2438–52.
Article
PubMed
PubMed Central
Google Scholar
Suff N, Story L, Shennan A. The prediction of preterm delivery: what is new? Semin Fetal Neonat M. 2018;24:27–32.
Article
Google Scholar
Abul-Husn NS, Kenny EE. Personalized medicine and the power of electronic health records. Cell. 2019;177:58–69.
Article
CAS
PubMed
PubMed Central
Google Scholar
Paquette AG, Hood L, Price ND, Sadovsky Y. Deep phenotyping during pregnancy for predictive and preventive medicine. Sci Transl Med. 2020;12:eaay1059.
Article
PubMed
PubMed Central
Google Scholar
Artzi NS, Shilo S, Hadar E, Rossman H, Barbash-Hazan S, Ben-Haroush A, et al. Prediction of gestational diabetes based on nationwide electronic health records. Nat Med. 2020;26:71–6.
Article
CAS
PubMed
Google Scholar
Ravizza S, Huschto T, Adamov A, Böhm L, Büsser A, Flöther FF, et al. Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data. Nat Med. 2019;25:57–9.
Article
CAS
PubMed
Google Scholar
Li R, Chen Y, Ritchie MD, Moore JH. Electronic health records and polygenic risk scores for predicting disease risk. Nat Publ Group. 2020;31:1–10.
CAS
Google Scholar
Zhang G, Feenstra B, Bacelis J, Liu X, Muglia LM, Juodakis J, et al. Genetic associations with gestational duration and spontaneous preterm birth. New Engl J Med. 2017;377:1156–67.
Article
CAS
PubMed
Google Scholar
Tomašev N, Glorot X, Rae JW, Zielinski M, Askham H, Saraiva A, et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature. 2019;572:116–9.
Article
PubMed
PubMed Central
CAS
Google Scholar
Xiao C, Choi E, Sun J. Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. J Am Med Inform Assn. 2018;25:1419–28.
Article
Google Scholar
Zhao J, Feng Q, Wu P, Lupu RA, Wilke RA, Wells QS, et al. Learning from longitudinal data in electronic health record and genetic data to improve cardiovascular event prediction. Sci Rep. 2019;9:1–10.
CAS
Google Scholar
Goldstein BA, Navar AM, Pencina MJ, Ioannidis JPA. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inform Assoc. 2017;24:198–208.
Article
PubMed
Google Scholar
Aung MT, Yu Y, Ferguson KK, Cantonwine DE, Zeng L, McElrath TF, et al. Prediction and associations of preterm birth and its subtypes with eicosanoid enzymatic pathways and inflammatory markers. Sci Rep. 2019;9:17049.
Article
PubMed
PubMed Central
CAS
Google Scholar
Rittenhouse KJ, Vwalika B, Keil A, Winston J, Stoner M, Price JT, et al. Improving preterm newborn identification in low-resource settings with machine learning. PLoS One. 2019;14:e0198919.
Article
CAS
PubMed
PubMed Central
Google Scholar
Fergus P, Cheung P, Hussain A, Al-Jumeily D, Dobbins C, Iram S. Prediction of preterm deliveries from EHG signals using machine learning. PLoS One. 2013;8:e77154.
Article
CAS
PubMed
PubMed Central
Google Scholar
Chen T, Guestrin C. Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International conference on knowledge discovery and data mining. 2016. p. 785–94.
Hastie T, Tibshirani R, Friedman J. The elements of statistical learning, data mining, inference, and prediction; 2009. https://doi.org/10.1007/978-0-387-84858-7.
Book
Google Scholar
Corey KM, Kashyap S, Lorenzi E, Lagoo-Deenadayalan SA, Heller K, Whalen K, et al. Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): a retrospective, single-site study. PLoS Med. 2018;15:e1002701.
Article
PubMed
PubMed Central
Google Scholar
Jing L, Cerna AEU, Good CW, Sauers NM, Schneider G, Hartzel DN, et al. A machine learning approach to management of heart failure populations. Jacc Hear Fail. 2020;8:578–87.
Article
Google Scholar
Carter J, Seed PT, Watson HA, David AL, Sandall J, Shennan AH, et al. Development and validation of predictive models for QUiPP App v.2: tool for predicting preterm birth in women with symptoms of threatened preterm labor. Ultrasound Obstet Gynecol. 2020;55:357–67.
Article
CAS
PubMed
Google Scholar
Vogel JP, Chawanpaiboon S, Moller A-B, Watananirun K, Bonet M, Lumbiganon P. The global epidemiology of preterm birth. Best Pract Res Cl Ob. 2018;52:3–12.
Article
Google Scholar
Smith GCS, Pell JP. Teenage pregnancy and risk of adverse perinatal outcomes associated with first and second births: population based retrospective cohort study. Obstet Gynecol Surv. 2002;57:136–7.
Article
Google Scholar
Waldenström U, Aasheim V, Nilsen ABV, Rasmussen S, Pettersson HJ, Schytt E, et al. Adverse pregnancy outcomes related to advanced maternal age compared with smoking and being overweight. Obstet Gynecol. 2014;123:104–12.
Article
PubMed
Google Scholar
Carolan M. Maternal age ≥45 years and maternal and perinatal outcomes: a review of the evidence. Midwifery. 2013;29:479–89.
Article
PubMed
Google Scholar
Ray JG, Vermeulen MJ, Shapiro JL, Kenshole AB. Maternal and neonatal outcomes in pregestational and gestational diabetes mellitus, and the influence of maternal obesity and weight gain: the DEPOSIT study. Qjm Int J Med. 2001;94:347–56.
Article
CAS
Google Scholar
Whiteman V, Salinas A, Weldeselasse HE, August EM, Mbah AK, Aliyu MH, et al. Impact of sickle cell disease and thalassemias in infants on birth outcomes. Eur J Obstet Gyn R B. 2013;170:324–8.
Article
Google Scholar
Umesawa M, Kobashi G. Epidemiology of hypertensive disorders in pregnancy: prevalence, risk factors, predictors and prognosis. Hypertens Res. 2017;40:213–20.
Article
PubMed
Google Scholar
Koullali B, Oudijk MA, Nijman TAJ, Mol BWJ, Pajkrt E. Risk assessment and management to prevent preterm birth. Semin Fetal Neonatal Med. 2016;21:80–8.
Article
CAS
PubMed
Google Scholar
Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, et al., editors. Advances in Neural Information Processing Systems 30: Curran Associates, Inc.; 2017. p. 4765–74.
Google Scholar
Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell. 2020;2:56–67.
Article
PubMed
PubMed Central
Google Scholar
Davis J, Goadrich M. The relationship between Precision-Recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine learning. 2006. p. 233–24 .
Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ, Adams T, et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng. 2018;2:749–60.
Article
PubMed
PubMed Central
Google Scholar
Creanga AA, Berg CJ, Syverson C, Seed K, Bruce FC, Callaghan WM. Pregnancy-related mortality in the United States, 2006–2010. Obstet Gynecol. 2015;125:5–12.
Article
PubMed
Google Scholar
Hirshberg A, Srinivas SK. Epidemiology of maternal morbidity and mortality. Semin Perinatol. 2017;41:332–7.
Article
PubMed
Google Scholar
Kopitar L, Kocbek P, Cilar L, Sheikh A, Stiglic G. Early detection of type 2 diabetes mellitus using machine learning-based prediction models. Sci Rep-Uk. 2020;10:11981.
Article
CAS
Google Scholar
Yan L, Zhang H-T, Goncalves J, Xiao Y, Wang M, Guo Y, et al. An interpretable mortality prediction model for COVID-19 patients. Nat Mach Intell. 2020;2:283–8.
Article
Google Scholar
Couronné R, Probst P, Boulesteix A-L. Random forest versus logistic regression: a large-scale benchmark experiment. Bmc Bioinformatics. 2018;19:270.
Article
PubMed
PubMed Central
Google Scholar
Gao C, Osmundson S, Edwards DRV, Jackson GP, Malin BA, Chen Y. Deep learning predicts extreme preterm birth from electronic health records. J Biomed Inform. 2019;100:103334.
Article
PubMed
PubMed Central
Google Scholar
Torchin H, Ancel P-Y. Epidemiology and risk factors of preterm birth. J De Gynecol Obstetrique Et Biologie De La Reprod. 2016;45:1213–30.
Article
CAS
Google Scholar
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44–56.
Article
CAS
PubMed
Google Scholar
He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25:30–6.
Article
CAS
PubMed
PubMed Central
Google Scholar
Esplin MS. The importance of clinical phenotype in understanding and preventing spontaneous preterm birth. Am J Perinatol. 2016;33:236–44.
Article
PubMed
Google Scholar
Manuck TA, Esplin MS, Biggio J, Bukowski R, Parry S, Zhang H, et al. The phenotype of spontaneous preterm birth: application of a clinical phenotyping tool. Am J Obstet Gynecol. 2015;212:487.e1–487.e11.
Article
Google Scholar
Phelan M, Bhavsar NA, Goldstein BA. Illustrating informed presence bias in electronic health records data: how patient interactions with a health system can impact inference. Egems Wash Dc. 2017;5:22.
PubMed
PubMed Central
Google Scholar
Outcomes I of M (US) C on UPB and AH, Behrman RE, Butler AS. Preterm birth: causes, consequences, and prevention. 2007. https://doi.org/10.17226/11622.
Kukhareva PV, Caverly TJ, Li H, Katki HA, Cheung LC, Reese TJ, et al. Inaccuracies in electronic health records smoking data and a potential approach to address resulting underestimation in determining lung cancer screening eligibility. J Am Med Inform Assoc. 2022. https://doi.org/10.1093/jamia/ocac020.
Garies S, Cummings M, Quan H, McBrien K, Drummond N, Manca D, et al. Methods to improve the quality of smoking records in a primary care EMR database: exploring multiple imputation and pattern-matching algorithms. Bmc Med Inform Decis. 2020;20:56.
Article
Google Scholar
Moutquin J-M. Classification and heterogeneity of preterm birth. BJOG. 2003;110:30–3.
Article
PubMed
Google Scholar
Phillips C, Velji Z, Hanly C, Metcalfe A. Risk of recurrent spontaneous preterm birth: a systematic review and meta-analysis. BMJ Open. 2017;7:e015402.
Article
PubMed
PubMed Central
Google Scholar
Shah NH, Milstein A, Bagley SC. Making machine learning models clinically useful. JAMA. 2019;322:1351–2.
Article
PubMed
Google Scholar
Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med. 2018;178:1544.
Article
PubMed
PubMed Central
Google Scholar
Weng C, Shah N, Hripcsak G. Deep phenotyping: embracing complexity and temporality—towards scalability, portability, and interoperability. J Biomed Inform. 2020;105:103433.
Article
PubMed
PubMed Central
Google Scholar
Bergstra J, Yamins D, Cox D. Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures. In: International conference on machine learning; 2013. p. 115–23.
Google Scholar
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.
Google Scholar
Soysal E, Wang J, Jiang M, Wu Y, Pakhomov S, Liu H, et al. CLAMP – a toolkit for efficiently building customized clinical natural language processing pipelines. J Am Med Inform Assn. 2017;25:331–6.
Article
Google Scholar
Marees AT, de Kluiver H, Stringer S, Vorspan F, Curis E, Marie‐Claire C, et al. A tutorial on conducting genome‐wide association studies: Quality control and statistical analysis. Int J Methods Psychiatr Res. 2018;27(2):e1608.
Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience. 2015;4:7.
Article
PubMed
PubMed Central
CAS
Google Scholar
Euesden J, Lewis CM, O’Reilly PF. PRSice: polygenic risk score software. Bioinformatics. 2015;31:1466–8.
Article
CAS
PubMed
Google Scholar
Choi SW, O’Reilly PF. PRSice-2: polygenic risk score software for biobank-scale data. Gigascience. 2019:giz082.
McInnes L, Healy J, Melville J. UMAP: Uniform manifold approximation and projection for dimension reduction. arXiv preprint. 2018;arXiv:1802.03426.
McInnes L, Healy J, Astels S. hdbscan: Hierarchical density based clustering. J Open Source Softw. 2017;2(11):205.
Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17:261–72.
Article
CAS
PubMed
PubMed Central
Google Scholar