{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T15:56:19Z","timestamp":1776268579304,"version":"3.50.1"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T00:00:00Z","timestamp":1671408000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T00:00:00Z","timestamp":1671408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Care during pregnancy, childbirth and puerperium are fundamental to avoid pathologies for the mother and her baby. However, health issues can occur during this period, causing misfortunes, such as the death of the fetus or neonate. Predictive models of fetal and infant deaths are important technological tools that can help to reduce mortality indexes. The main goal of this work is to present a systematic review of literature focused on computational models to predict mortality, covering stillbirth, perinatal, neonatal, and infant deaths, highlighting their methodology and the description of the proposed computational models.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We conducted a systematic review of literature, limiting the search to the last 10 years of publications considering the five main scientific databases as source.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>From 671 works, 18 of them were selected as primary studies for further analysis. We found that most of works are focused on prediction of neonatal deaths, using machine learning models (more specifically Random Forest). The top five most common features used to train models are birth weight, gestational age, sex of the child, Apgar score and mother\u2019s age. Having predictive models for preventing mortality during and post-pregnancy not only improve the mother\u2019s quality of life, as well as it can be a powerful and low-cost tool to decrease mortality ratios.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Based on the results of this SRL, we can state that scientific efforts have been done in this area, but there are many open research opportunities to be developed by the community.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-022-02082-3","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T14:03:45Z","timestamp":1671458625000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature"],"prefix":"10.1186","volume":"22","author":[{"given":"Elisson da","family":"Silva Rocha","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Flavio Leandro","family":"de Morais Melo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria Eduarda Ferro","family":"de Mello","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Barbara","family":"Figueiroa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vanderson","family":"Sampaio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patricia Takako","family":"Endo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,19]]},"reference":[{"key":"2082_CR1","unstructured":"UNICEF. A neglected tragedy: the global burden of stillbirths. Report of the UN Inter-agency Group for Child Mortality Estimation, 2020. https:\/\/www.unicef.org\/reports\/neglected-tragedy-global-burden-of-stillbirths-2020 (2021\/10\/20)."},{"issue":"2","key":"2082_CR2","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1002\/uog.20100","volume":"53","author":"F D\u2019Antonio","year":"2019","unstructured":"D\u2019Antonio F, Odibo A, Berghella V, Khalil A, Hack K, Saccone G, Prefumo F, Buca D, Liberati M, Pagani G, et al. Perinatal mortality, timing of delivery and prenatal management of monoamniotic twin pregnancy: systematic review and meta-analysis. Ultrasound Obstet Gynecol. 2019;53(2):166\u201374.","journal-title":"Ultrasound Obstet Gynecol"},{"key":"2082_CR3","unstructured":"World Health Organization. Newborn Mortality. 2022. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/levels-and-trends-in-child-mortality-report-2021 (2022\/05\/20)"},{"key":"2082_CR4","unstructured":"World Health Organization. Number of infant deaths (between birth and 11 months). 2022. https:\/\/www.who.int\/data\/gho\/data\/indicators\/indicator-details\/GHO\/number-of-infant-deaths (2022\/05\/20)"},{"issue":"9","key":"2082_CR5","doi-asserted-by":"publisher","first-page":"0222566","DOI":"10.1371\/journal.pone.0222566","volume":"14","author":"T Tekelab","year":"2019","unstructured":"Tekelab T, Chojenta C, Smith R, Loxton D. The impact of antenatal care on neonatal mortality in sub-Saharan Africa: a systematic review and meta-analysis. PLoS ONE. 2019;14(9):0222566.","journal-title":"PLoS ONE"},{"key":"2082_CR6","doi-asserted-by":"crossref","unstructured":"Blanco E, Marin M, Nu\u00f1ez L, Retamal E, Ossa X, Woolley KE, Oludotun T, Bartington SE, Delgado-Saborit JM, Harrison RM, et al. Adverse pregnancy and perinatal outcomes in Latin America and the Caribbean: systematic review and meta-analysis. Rev Panam Salud P\u00fablica 2022;46.","DOI":"10.26633\/RPSP.2022.21"},{"key":"2082_CR7","unstructured":"United Nations. The Sustainable Development Goals Report 2019, UN, New York, 2019. https:\/\/unstats.un.org\/sdgs\/report\/2019\/The-Sustainable-Development-Goals-Report-2019.pdf (2021\/10\/21)."},{"key":"2082_CR8","volume-title":"The millennium development goals report","author":"United Nations","year":"2015","unstructured":"United Nations. The millennium development goals report. New York: United Nations; 2015."},{"key":"2082_CR9","first-page":"174550652110461","volume":"17","author":"R Ramakrishnan","year":"2021","unstructured":"Ramakrishnan R, Rao S, He J-R. Perinatal health predictors using artificial intelligence: a review. Womens Health. 2021;17:17455065211046132.","journal-title":"Womens Health"},{"issue":"11","key":"2082_CR10","doi-asserted-by":"publisher","first-page":"2026750","DOI":"10.1001\/jamanetworkopen.2020.26750","volume":"3","author":"VV Shukla","year":"2020","unstructured":"Shukla VV, Eggleston B, Ambalavanan N, McClure EM, Mwenechanya M, Chomba E, Bose C, Bauserman M, Tshefu A, Goudar SS, et al. Predictive modeling for perinatal mortality in resource-limited settings. JAMA Netw Open. 2020;3(11):2026750\u20132026750.","journal-title":"JAMA Netw Open"},{"key":"2082_CR11","doi-asserted-by":"publisher","first-page":"8","DOI":"10.12688\/gatesopenres.12796.1","volume":"2","author":"Z Hoodbhoy","year":"2018","unstructured":"Hoodbhoy Z, Hasan B, Jehan F, Bijnens B, Chowdhury D. Machine learning from fetal flow waveforms to predict adverse perinatal outcomes: a study protocol. Gates Open Res. 2018;2:8.","journal-title":"Gates Open Res"},{"issue":"10","key":"2082_CR12","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2020-040132","volume":"10","author":"IB Mboya","year":"2020","unstructured":"Mboya IB, Mahande MJ, Mohammed M, Obure J, Mwambi HG. Prediction of perinatal death using machine learning models: a birth registry-based cohort study in northern Tanzania. BMJ Open. 2020;10(10): 040132.","journal-title":"BMJ Open"},{"key":"2082_CR13","doi-asserted-by":"crossref","unstructured":"Qureshi H, Khan M, Quadri SMA, Hafiz R. Association of pre-pregnancy weight and weight gain with perinatal mortality. In: Proceedings of the 8th international conference on frontiers of information technology; 2010. pp. 1\u20136.","DOI":"10.1145\/1943628.1943656"},{"issue":"1","key":"2082_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-62210-9","volume":"10","author":"E Malacova","year":"2020","unstructured":"Malacova E, Tippaya S, Bailey HD, Chai K, Farrant BM, Gebremedhin AT, Leonard H, Marinovich ML, Nassar N, Phatak A, et al. Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980\u20132015. Sci Rep. 2020;10(1):1\u20138.","journal-title":"Sci Rep"},{"issue":"1","key":"2082_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13755-020-00105-9","volume":"8","author":"A Koivu","year":"2020","unstructured":"Koivu A, Sairanen M. Predicting risk of stillbirth and preterm pregnancies with machine learning. Health Inf Sci Syst. 2020;8(1):1\u201312.","journal-title":"Health Inf Sci Syst"},{"issue":"4","key":"2082_CR16","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1159\/000516891","volume":"118","author":"C Mangold","year":"2021","unstructured":"Mangold C, Zoretic S, Thallapureddy K, Moreira A, Chorath K, Moreira A. Machine learning models for predicting neonatal mortality: a systematic review. Neonatology. 2021;118(4):394\u2013405.","journal-title":"Neonatology"},{"key":"2082_CR17","unstructured":"WHO. Stillbirths. 2015. http:\/\/www.who.int\/maternal_child_adolescent\/epidemiology\/stillbirth\/en\/ (2021\/10\/20)."},{"key":"2082_CR18","unstructured":"WHO. Neonatal and perinatal mortality: country, regional and global estimates; 2006."},{"key":"2082_CR19","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.ejogrb.2020.11.015","volume":"256","author":"K Kelly","year":"2021","unstructured":"Kelly K, Meaney S, Leitao S, O\u2019Donoghue K. A review of stillbirth definitions: a rationale for change. Eur J Obstet Gynecol Reprod Biol. 2021;256:235\u201345.","journal-title":"Eur J Obstet Gynecol Reprod Biol"},{"key":"2082_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104521","volume":"134","author":"S Baker","year":"2021","unstructured":"Baker S, Xiang W, Atkinson I. Hybridized neural networks for non-invasive and continuous mortality risk assessment in preterm infants. Comput Biol Med. 2021;134: 104521.","journal-title":"Comput Biol Med"},{"issue":"3","key":"2082_CR21","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.artmed.2014.10.001","volume":"62","author":"FR Cerqueira","year":"2014","unstructured":"Cerqueira FR, Ferreira TG, de Paiva Oliveira A, Augusto DA, Krempser E, Barbosa HJC, Franceschini SdCC, de Freitas BAC, Gomes AP. Siqueira-Batista R Nicesim: an open-source simulator based on machine learning techniques to support medical research on prenatal and perinatal care decision making. Artif Intell Med. 2014;62(3):193\u2013201.","journal-title":"Artif Intell Med"},{"issue":"1","key":"2082_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-021-01497-8","volume":"21","author":"A Sheikhtaheri","year":"2021","unstructured":"Sheikhtaheri A, Zarkesh MR, Moradi R, Kermani F. Prediction of neonatal deaths in NICUs: development and validation of machine learning models. BMC Med Inform Decis Mak. 2021;21(1):1\u201314.","journal-title":"BMC Med Inform Decis Mak"},{"issue":"1","key":"2082_CR23","doi-asserted-by":"publisher","first-page":"004","DOI":"10.1093\/jamiaopen\/ooab004","volume":"4","author":"Y Sun","year":"2021","unstructured":"Sun Y, Kaur R, Gupta S, Paul R, Das R, Cho SJ, Anand S, Boutilier JJ, Saria S, Palma J, et al. Development and validation of high definition phenotype-based mortality prediction in critical care units. JAMIA Open. 2021;4(1):004.","journal-title":"JAMIA Open"},{"issue":"8","key":"2082_CR24","doi-asserted-by":"publisher","first-page":"695","DOI":"10.3390\/jpm11080695","volume":"11","author":"J-F Hsu","year":"2021","unstructured":"Hsu J-F, Chang Y-F, Cheng H-J, Yang C, Lin C-Y, Chu S-M, Huang H-R, Chiang M-C, Wang H-C, Tsai M-H. Machine learning approaches to predict in-hospital mortality among neonates with clinically suspected sepsis in the neonatal intensive care unit. J Personal Med. 2021;11(8):695.","journal-title":"J Personal Med"},{"issue":"1","key":"2082_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-31920-6","volume":"8","author":"M Podda","year":"2018","unstructured":"Podda M, Bacciu D, Micheli A, Bell\u00f9 R, Placidi G, Gagliardi L. A machine learning approach to estimating preterm infants survival: development of the preterm infants survival assessment (PISA) predictor. Sci Rep. 2018;8(1):1\u20139.","journal-title":"Sci Rep"},{"key":"2082_CR26","doi-asserted-by":"publisher","first-page":"123347","DOI":"10.1109\/ACCESS.2020.3006710","volume":"8","author":"J Jaskari","year":"2020","unstructured":"Jaskari J, Myll\u00e4rinen J, Leskinen M, Rad AB, Hollm\u00e9n J, Andersson S, S\u00e4rkk\u00e4 S. Machine learning methods for neonatal mortality and morbidity classification. IEEE Access. 2020;8:123347\u201358.","journal-title":"IEEE Access"},{"issue":"1","key":"2082_CR27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-79139-8","volume":"11","author":"J Lee","year":"2021","unstructured":"Lee J, Cai J, Li F, Vesoulis ZA. Predicting mortality risk for preterm infants using random forest. Sci Rep. 2021;11(1):1\u20139.","journal-title":"Sci Rep"},{"key":"2082_CR28","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.jss.2017.09.002","volume":"221","author":"JN Cooper","year":"2018","unstructured":"Cooper JN, Minneci PC, Deans KJ. Postoperative neonatal mortality prediction using superlearning. J Surg Res. 2018;221:311\u20139.","journal-title":"J Surg Res"},{"key":"2082_CR29","doi-asserted-by":"crossref","unstructured":"Valter R, Santiago S, Ramos R, Oliveira M, Andrade LOM, de HC Barreto IC. Data mining and risk analysis supporting decision in Brazilian public health systems. In: 2019 IEEE international conference on e-health Networking, Application & Services (HealthCom). IEEE; 2019. pp. 1\u20136","DOI":"10.1109\/HealthCom46333.2019.9009439"},{"key":"2082_CR30","doi-asserted-by":"publisher","first-page":"1785","DOI":"10.1007\/s10618-020-00728-2","volume":"35","author":"A Saravanou","year":"2021","unstructured":"Saravanou A, Noelke C, Huntington N, Acevedo-Garcia D, Gunopulos D. Predictive modeling of infant mortality. Data Mining Knowl Discov. 2021;35:1785\u2013807.","journal-title":"Data Mining Knowl Discov"},{"issue":"1","key":"2082_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12887-021-02788-9","volume":"21","author":"AF Batista","year":"2021","unstructured":"Batista AF, Diniz CS, Bonilha EA, Kawachi I, Chiavegatto Filho AD. Neonatal mortality prediction with routinely collected data: a machine learning approach. BMC Pediatr. 2021;21(1):1\u20136.","journal-title":"BMC Pediatr"},{"key":"2082_CR32","doi-asserted-by":"crossref","unstructured":"Hajipour M, Taherpour N, Fateh H, Yousefi E, Etemad K, Zolfizadeh F, Rajabi A, Valadbeigi T, Mehrabi Y. Predictive factors of infant mortality using data mining in Iran. J Compr Pediatr 2021;12(1).","DOI":"10.5812\/compreped.108575"},{"key":"2082_CR33","doi-asserted-by":"crossref","unstructured":"AlShwaish WM, Alabdulhafith MI. Mortality prediction based on imbalanced new born and perinatal period data. Mortality 2019;10(8).","DOI":"10.14569\/IJACSA.2019.0100808"},{"issue":"4","key":"2082_CR34","first-page":"1","volume":"5","author":"D Ramyachitra","year":"2014","unstructured":"Ramyachitra D, Manikandan P. Imbalanced dataset classification and solutions: a review. Int J Comput Bus Res. 2014;5(4):1\u201329.","journal-title":"Int J Comput Bus Res"},{"key":"2082_CR35","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.1016\/j.ins.2019.10.048","volume":"512","author":"T Pan","year":"2020","unstructured":"Pan T, Zhao J, Wu W, Yang J. Learning imbalanced datasets based on smote and Gaussian distribution. Inf Sci. 2020;512:1214\u201333.","journal-title":"Inf Sci"},{"issue":"2","key":"2082_CR36","first-page":"539","volume":"39","author":"X-Y Liu","year":"2008","unstructured":"Liu X-Y, Wu J, Zhou Z-H. Exploratory undersampling for class-imbalance learning. IEEE Trans Syst Man Cybern Part B Cybern. 2008;39(2):539\u201350.","journal-title":"IEEE Trans Syst Man Cybern Part B Cybern"},{"key":"2082_CR37","doi-asserted-by":"crossref","unstructured":"Phung S, Kumar A, Kim J. A deep learning technique for imputing missing healthcare data. In: 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE; 2019. pp. 6513\u20136516","DOI":"10.1109\/EMBC.2019.8856760"},{"key":"2082_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2019.103375","volume":"112","author":"B Remeseiro","year":"2019","unstructured":"Remeseiro B, Bolon-Canedo V. A review of feature selection methods in medical applications. Comput Biol Med. 2019;112: 103375.","journal-title":"Comput Biol Med"},{"issue":"6","key":"2082_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3136625","volume":"50","author":"J Li","year":"2017","unstructured":"Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J, Liu H. Feature selection: a data perspective. ACM Compu Surv. 2017;50(6):1\u201345.","journal-title":"ACM Compu Surv"},{"key":"2082_CR40","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.enbuild.2017.04.078","volume":"147","author":"J-H Choi","year":"2017","unstructured":"Choi J-H. Investigation of the correlation of building energy use intensity estimated by six building performance simulation tools. Energy Build. 2017;147:14\u201326.","journal-title":"Energy Build"},{"key":"2082_CR41","unstructured":"Yu T, Zhu H. Hyper-parameter optimization: a review of algorithms and applications. arXiv preprint arXiv:2003.05689 (2020)"},{"key":"2082_CR42","unstructured":"Liashchynskyi P, Liashchynskyi P. Grid search, random search, genetic algorithm: a big comparison for NAS. 2019. arXiv preprint arXiv:1912.06059."},{"key":"2082_CR43","unstructured":"Frazier PI. A tutorial on Bayesian optimization. 2018. arXiv preprint arXiv:1807.02811"},{"issue":"3","key":"2082_CR44","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1109\/TPAMI.2009.187","volume":"32","author":"JD Rodriguez","year":"2009","unstructured":"Rodriguez JD, Perez A, Lozano JA. Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE Trans Pattern Anal Mach Intell. 2009;32(3):569\u201375.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"2082_CR45","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s11222-009-9153-8","volume":"21","author":"T Fushiki","year":"2011","unstructured":"Fushiki T. Estimation of prediction error by using k-fold cross-validation. Stat Comput. 2011;21(2):137\u201346.","journal-title":"Stat Comput"},{"issue":"2","key":"2082_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","volume":"5","author":"M Hossin","year":"2015","unstructured":"Hossin M, Sulaiman MN. A review on evaluation metrics for data classification evaluations. Int J Data Mining Knowl Manag Process. 2015;5(2):1.","journal-title":"Int J Data Mining Knowl Manag Process"},{"key":"2082_CR47","doi-asserted-by":"crossref","unstructured":"Tharwat A. Classification assessment methods. Appl Comput Inform. 2020.","DOI":"10.1016\/j.aci.2018.08.003"},{"key":"2082_CR48","doi-asserted-by":"crossref","unstructured":"Davis J, Goadrich M. The relationship between precision\u2013recall and roc curves. In: Proceedings of the 23rd international conference on machine learning. 2006. pp. 233\u201340","DOI":"10.1145\/1143844.1143874"},{"key":"2082_CR49","doi-asserted-by":"publisher","first-page":"307","DOI":"10.3389\/fpubh.2017.00307","volume":"5","author":"R Trevethan","year":"2017","unstructured":"Trevethan R. Sensitivity, specificity, and predictive values: foundations, pliabilities, and pitfalls in research and practice. Front Public Health. 2017;5:307.","journal-title":"Front Public Health"},{"issue":"1","key":"2082_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12864-019-6413-7","volume":"21","author":"D Chicco","year":"2020","unstructured":"Chicco D, Jurman G. The advantages of the Matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC Genomics. 2020;21(1):1\u201313.","journal-title":"BMC Genomics"},{"issue":"11","key":"2082_CR51","doi-asserted-by":"publisher","first-page":"1389","DOI":"10.1109\/LSP.2014.2337313","volume":"21","author":"X Sun","year":"2014","unstructured":"Sun X, Xu W. Fast implementation of Delong\u2019s algorithm for comparing the areas under correlated receiver operating characteristic curves. IEEE Signal Process Lett. 2014;21(11):1389\u201393.","journal-title":"IEEE Signal Process Lett"},{"key":"2082_CR52","unstructured":"Shier R. Mathematics learning support centre: Statistics. 2004."},{"issue":"3","key":"2082_CR53","doi-asserted-by":"publisher","first-page":"0173461","DOI":"10.1371\/journal.pone.0173461","volume":"12","author":"AS Trudell","year":"2017","unstructured":"Trudell AS, Tuuli MG, Colditz GA, Macones GA, Odibo AO. A stillbirth calculator: development and internal validation of a clinical prediction model to quantify stillbirth risk. PLoS ONE. 2017;12(3):0173461.","journal-title":"PLoS ONE"},{"issue":"1","key":"2082_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12884-019-2243-5","volume":"19","author":"F Kidus","year":"2019","unstructured":"Kidus F, Woldemichael K, Hiko D. Predictors of neonatal mortality in Assosa zone, western Ethiopia: a matched case control study. BMC Pregnancy Childbirth. 2019;19(1):1\u201313.","journal-title":"BMC Pregnancy Childbirth"},{"issue":"6","key":"2082_CR55","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1111\/aogs.14136","volume":"100","author":"T Ushida","year":"2021","unstructured":"Ushida T, Moriyama Y, Nakatochi M, Kobayashi Y, Imai K, Nakano-Kobayashi T, Nakamura N, Hayakawa M, Kajiyama H, Kotani T, et al. Antenatal prediction models for short-and medium-term outcomes in preterm infants. Acta Obstet Gynecol Scand. 2021;100(6):1089\u201396.","journal-title":"Acta Obstet Gynecol Scand"},{"issue":"5","key":"2082_CR56","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1038\/s41372-020-0650-0","volume":"40","author":"JS McLeod","year":"2020","unstructured":"McLeod JS, Menon A, Matusko N, Weiner GM, Gadepalli SK, Barks J, Mychaliska GB, Perrone EE. Comparing mortality risk models in VLBW and preterm infants: systematic review and meta-analysis. J Perinatol. 2020;40(5):695\u2013703.","journal-title":"J Perinatol"},{"issue":"10","key":"2082_CR57","doi-asserted-by":"publisher","first-page":"1950017","DOI":"10.1142\/S0218001419500174","volume":"33","author":"X Huang","year":"2019","unstructured":"Huang X, Wu L, Ye Y. A review on dimensionality reduction techniques. Int J Pattern Recognit Artif Intell. 2019;33(10):1950017.","journal-title":"Int J Pattern Recognit Artif Intell"},{"issue":"1","key":"2082_CR58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-79139-8","volume":"11","author":"Y Zhu","year":"2021","unstructured":"Zhu Y, Brettin T, Xia F, Partin A, Shukla M, Yoo H, Evrard YA, Doroshow JH, Stevens RL. Converting tabular data into images for deep learning with convolutional neural networks. Sci Rep. 2021;11(1):1\u201311.","journal-title":"Sci Rep"},{"issue":"1","key":"2082_CR59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-37186-2","volume":"9","author":"A Sharma","year":"2019","unstructured":"Sharma A, Vans E, Shigemizu D, Boroevich KA, Tsunoda T. Deepinsight: a methodology to transform a non-image data to an image for convolution neural network architecture. Sci Rep. 2019;9(1):1\u20137.","journal-title":"Sci Rep"},{"issue":"1","key":"2082_CR60","doi-asserted-by":"publisher","first-page":"0010061","DOI":"10.1371\/journal.pntd.0010061","volume":"16","author":"SR da Silva Neto","year":"2022","unstructured":"da Silva Neto SR, Tabosa Oliveira T, Teixeira IV, Aguiar de Oliveira SB, Souza Sampaio V, Lynn T, Endo PT. Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: a systematic review. PLoS Negl Trop Dis. 2022;16(1):0010061.","journal-title":"PLoS Negl Trop Dis"},{"issue":"3","key":"2082_CR61","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1093\/jamiaopen\/ooaa034","volume":"3","author":"A Choudhury","year":"2020","unstructured":"Choudhury A, Renjilian E, Asan O. Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review. JAMIA Open. 2020;3(3):459\u201371.","journal-title":"JAMIA Open"},{"issue":"1","key":"2082_CR62","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s12978-018-0475-x","volume":"15","author":"SE Geller","year":"2018","unstructured":"Geller SE, Koch AR, Garland CE, MacDonald EJ, Storey F, Lawton B. A global view of severe maternal morbidity: moving beyond maternal mortality. Reprod Health. 2018;15(1):31\u201343.","journal-title":"Reprod Health"},{"key":"2082_CR63","doi-asserted-by":"crossref","unstructured":"Manik H, Siregar MFG, Rochadi RK, Sudaryati E, Yustina I, Triyoga RS. Maternal mortality classification for health promotive in dairi using machine learning approach. In: IOP conference series: materials science and engineering, vol 851. IOP Publishing; 2020, p. 012055.","DOI":"10.1088\/1757-899X\/851\/1\/012055"},{"issue":"1","key":"2082_CR64","first-page":"33","volume":"23","author":"M Dawodi","year":"2020","unstructured":"Dawodi M, Wada T, Baktash JA. Applicability of ICT, data mining and machine learning to reduce maternal mortality and morbidity: case study Afghanistan. Int Inf Inst (Tokyo) Inf. 2020;23(1):33\u201345.","journal-title":"Int Inf Inst (Tokyo) Inf"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-022-02082-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-022-02082-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-022-02082-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T15:04:41Z","timestamp":1671462281000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-022-02082-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,19]]},"references-count":64,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["2082"],"URL":"https:\/\/doi.org\/10.1186\/s12911-022-02082-3","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,19]]},"assertion":[{"value":"13 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"334"}}