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Belaghi RA. Prediction of preterm birth in multiparous women using logistic regression and machine learning approaches. Sci Rep 2024; 14:21967. [PMID: 39304672 DOI: 10.1038/s41598-024-60097-4] [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: 09/30/2023] [Accepted: 04/18/2024] [Indexed: 09/22/2024] Open
Abstract
To predict preterm birth (PTB) in multiparous women, comparing machine learning approaches with traditional logistic regression. A population-based cohort study was conducted using data from the Ontario Better Outcomes Registry and Network (BORN). The cohort included all multiparous women who delivered a singleton birth at 20-42 weeks' gestation in an Ontario hospital between April 1, 2012 and March 31, 2014. The primary outcome was PTB < 37 weeks, with spontaneous PTB analyzed as a secondary outcome. Stepwise logistic regression and the Boruta machine learning were used to select the important variables during the first and second trimester. For building prediction models, the whole data set were divided for the two independent parts: two-third for training the classifiers (Logistic regression, random forests, decision trees, and artificial neural networks) and one-third for model validation. Then, the training data set were balanced by random over sampling technique. The best hyper parameters were obtained by the tenfold cross validation. The performance of all models was evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operating characteristics (AUC). The cohort included 145,846 births, of which 8125 (5.57%) were preterm. In first-trimester models, the strongest predictors of PTB were previous PTB, preexisting diabetes, and abnormal pregnancy-associated plasma protein-A. In the testing data set, the highest predictive ability was seen for artificial neural networks, with an area under the receiver operating characteristic curve (AUC) of 68.8% (95% CI 67.6-70.1%). In second-trimester models, addition of infant sex, attendance at first-trimester appointment, medication exposure, and abnormal alpha-fetoprotein concentrations increased the AUC to 72.1% (95% CI 71.1-73.1%) with logistic regression. With the inclusion of the variable complications during pregnancy, the AUC increased to 80.5% (95% CI 79.6-81.5%) using logistic regression. For both overall and spontaneous PTB, during both the first and second trimesters, models yielded negative predictive values of 97%. Overall, machine learning and logistic regression produced similar performance for prediction of PTB. For overall and spontaneous PTB, both first- and second-trimester models provided negative predictive values of ~ 97%, higher than that of fetal fibronectin.
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Affiliation(s)
- Reza Arabi Belaghi
- Clinical Research Unit (CRU), CHEO Research Institute, University of Ottawa, Ottawa, Canada.
- Department of Obstetrics and Gynecology, McMaster University, 1280 Main Street W, Hamilton, ON, L8S 4L8, Canada.
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Das S, Erdman L, Brals D, Boczek B, Hasan SMT, Massara P, Alam MA, Fahim SM, Mahfuz M, Hoogendoorn M, Zuiderent-Jerak T, Bandsma RHJ, Ahmed T, Voskuijl W. Development of machine learning models predicting mortality using routinely collected observational health data from 0-59 months old children admitted to an intensive care unit in Bangladesh: critical role of biochemistry and haematology data. BMJ Paediatr Open 2024; 8:e002365. [PMID: 39038911 PMCID: PMC11409392 DOI: 10.1136/bmjpo-2023-002365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Accepted: 07/03/2024] [Indexed: 07/24/2024] Open
Abstract
INTRODUCTION Treatment in the intensive care unit (ICU) generates complex data where machine learning (ML) modelling could be beneficial. Using routine hospital data, we evaluated the ability of multiple ML models to predict inpatient mortality in a paediatric population in a low/middle-income country. METHOD We retrospectively analysed hospital record data from 0-59 months old children admitted to the ICU of Dhaka hospital of International Centre for Diarrhoeal Disease Research, Bangladesh. Five commonly used ML models- logistic regression, least absolute shrinkage and selection operator, elastic net, gradient boosting trees (GBT) and random forest (RF), were evaluated using the area under the receiver operating characteristic curve (AUROC). Top predictors were selected using RF mean decrease Gini scores as the feature importance values. RESULTS Data from 5669 children was used and was reduced to 3505 patients (10% death, 90% survived) following missing data removal. The mean patient age was 10.8 months (SD=10.5). The top performing models based on the validation performance measured by mean 10-fold cross-validation AUROC on the training data set were RF and GBT. Hyperparameters were selected using cross-validation and then tested in an unseen test set. The models developed used demographic, anthropometric, clinical, biochemistry and haematological data for mortality prediction. We found RF consistently outperformed GBT and predicted the mortality with AUROC of ≥0.87 in the test set when three or more laboratory measurements were included. However, after the inclusion of a fourth laboratory measurement, very minor predictive gains (AUROC 0.87 vs 0.88) resulted. The best predictors were the biochemistry and haematological measurements, with the top predictors being total CO2, potassium, creatinine and total calcium. CONCLUSIONS Mortality in children admitted to ICU can be predicted with high accuracy using RF ML models in a real-life data set using multiple laboratory measurements with the most important features primarily coming from patient biochemistry and haematology.
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Affiliation(s)
- Subhasish Das
- Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
- Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Lauren Erdman
- The Center for Computational Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Translational Medicine Program, The Hospital for Sick Children, Toronto, Ontario, Canada
- James M. Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center and University of Cincinnati School of Medicine, Cincinnati, Ohio, USA
| | - Daniella Brals
- Department of Global Health, Amsterdam Institute for Global Health and Development, Amsterdam, Netherlands
| | - Bartlomiej Boczek
- Department of Global Health, Amsterdam Institute for Global Health and Development, Amsterdam, Netherlands
| | - S M Tafsir Hasan
- Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Paraskevi Massara
- The Center for Computational Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Md Ashraful Alam
- The University of Queensland Poche Centre for Indigenous Health, Saint Lucia, Queensland, Australia
| | - Shah Mohammad Fahim
- Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
- Division of Nutritional Sciences, Cornell University, Ithaca, New York, USA
| | - Mustafa Mahfuz
- Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Mark Hoogendoorn
- Faculty of Science, Department of Computer Science, Vrije University, Amsterdam, The Netherlands
| | | | - Robert H J Bandsma
- Department of Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada
- Centre for Global Child Health, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Tahmeed Ahmed
- Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Wieger Voskuijl
- Department of Global Health, Amsterdam Institute for Global Health and Development, Amsterdam, Netherlands
- Amsterdam UMC, University of Amsterdam, Amsterdam Centre for Global Child Health & Emma Children's Hospital, Amsterdam, The Netherlands
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Halomoan Harahap T, Mansouri S, Salim Abdullah O, Uinarni H, Askar S, Jabbar TL, Hussien Alawadi A, Yaseen Hassan A. An artificial intelligence approach to predict infants' health status at birth. Int J Med Inform 2024; 183:105338. [PMID: 38211423 DOI: 10.1016/j.ijmedinf.2024.105338] [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: 11/18/2023] [Revised: 12/28/2023] [Accepted: 12/31/2023] [Indexed: 01/13/2024]
Abstract
BACKGROUND Machine learning could be used for prognosis/diagnosis of maternal and neonates' diseases by analyzing the data sets and profiles obtained from a pregnant mother. PURPOSE We aimed to develop a prediction model based on machine learning algorithms to determine important maternal characteristics and neonates' anthropometric profiles as the predictors of neonates' health status. METHODS This study was conducted among 1280 pregnant women referred to healthcare centers to receive antenatal care. We evaluated several machine learning methods, including support vector machine (SVM), Ensemble, K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Decision tree classifiers, to predict newborn health state. RESULTS The minimum redundancy-maximum relevance (MRMR) algorithm revealed that variables, including head circumference of neonates, pregnancy intention, and drug consumption history during pregnancy, were top-scored features for classifying normal and unhealthy infants. Among the different classification methods, the SVM classifier had the best performance. The average values of accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) in the test group were 75%, 75%, 76%, 76%, and 65%, respectively, for SVM model. CONCLUSION Machine learning methods can efficiently forecast the neonate's health status among pregnant women. This study proposed a new approach toward the integration of maternal data and neonate profiles to facilitate the prediction of neonates' health status.
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Affiliation(s)
- Tua Halomoan Harahap
- Education of Mathematics, Universitas Muhammadiyah Sumatera Utara, Medan, Indonesia.
| | - Sofiene Mansouri
- Department of Biomedical Technology, College of Applied Medical Sciences in Al-Kharj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia; University of Tunis El Manar, Higher Institute of Medical Technologies of Tunis, Laboratory of Biophysics and Medical Technologies, Tunis, Tunisia.
| | - Omar Salim Abdullah
- Ministry of Education, Baqubah, Iraq; Bilad Alrafidain University College, Baquhah, Iraq.
| | - Herlina Uinarni
- Department of Anatomy, School of Medicine and Health Sciences, Atma Jaya Catholic University of Indonesia, Indonesia; Radiology Department of Pantai Indah Kapuk Hospital Jakarta, Indonesia.
| | - Shavan Askar
- Erbil Polytechnic University, Erbil Technical Engineering College, Information System Engineering Department, Erbil, Iraq.
| | - Thaer L Jabbar
- College of Pharmacy, Al- Ayen University, Thi-Qar, Iraq.
| | - Ahmed Hussien Alawadi
- Medical Laboratory Technique College, The Islamic University, Najaf, Iraq.; Medical Laboratory Technique College, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq; Medical Laboratory Technique College, The Islamic University of Babylon, Babylon, Iraq.
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Tesfie TK, Anlay DZ, Abie B, Chekol YM, Gelaw NB, Tebeje TM, Animut Y. Nomogram to predict risk of neonatal mortality among preterm neonates admitted with sepsis at University of Gondar Comprehensive Specialized Hospital: risk prediction model development and validation. BMC Pregnancy Childbirth 2024; 24:139. [PMID: 38360591 PMCID: PMC10868119 DOI: 10.1186/s12884-024-06306-4] [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: 06/15/2023] [Accepted: 01/29/2024] [Indexed: 02/17/2024] Open
Abstract
BACKGROUND Mortality in premature neonates is a global public health problem. In developing countries, nearly 50% of preterm births ends with death. Sepsis is one of the major causes of death in preterm neonates. Risk prediction model for mortality in preterm septic neonates helps for directing the decision making process made by clinicians. OBJECTIVE We aimed to develop and validate nomogram for the prediction of neonatal mortality. Nomograms are tools which assist the clinical decision making process through early estimation of risks prompting early interventions. METHODS A three year retrospective follow up study was conducted at University of Gondar Comprehensive Specialized Hospital and a total of 603 preterm neonates with sepsis were included. Data was collected using KoboCollect and analyzed using STATA version 16 and R version 4.2.1. Lasso regression was used to select the most potent predictors and to minimize the problem of overfitting. Nomogram was developed using multivariable binary logistic regression analysis. Model performance was evaluated using discrimination and calibration. Internal model validation was done using bootstrapping. Net benefit of the nomogram was assessed through decision curve analysis (DCA) to assess the clinical relevance of the model. RESULT The nomogram was developed using nine predictors: gestational age, maternal history of premature rupture of membrane, hypoglycemia, respiratory distress syndrome, perinatal asphyxia, necrotizing enterocolitis, total bilirubin, platelet count and kangaroo-mother care. The model had discriminatory power of 96.7% (95% CI: 95.6, 97.9) and P-value of 0.165 in the calibration test before and after internal validation with brier score of 0.07. Based on the net benefit analysis the nomogram was found better than treat all and treat none conditions. CONCLUSION The developed nomogram can be used for individualized mortality risk prediction with excellent performance, better net benefit and have been found to be useful in clinical practice with contribution in preterm neonatal mortality reduction by giving better emphasis for those at high risk.
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Affiliation(s)
- Tigabu Kidie Tesfie
- Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
| | - Degefaye Zelalem Anlay
- School of Nursing, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Birhanu Abie
- Department of Pediatrics and Child Health, School of Medicine, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Yazachew Moges Chekol
- Department of Health Information Technology, Mizan Aman College of Health Science, Mizan Aman, Ethiopia
| | - Negalgn Byadgie Gelaw
- Department of Public Health, Mizan Aman College of Health Science, Mizan Aman, Ethiopia
| | - Tsion Mulat Tebeje
- School of Public Health, College of Medicine and Health Sciences, Dilla University, Dilla, Ethiopia
| | - Yaregal Animut
- Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
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Demirci GM, Kittler PM, Phan HTT, Gordon AD, Flory MJ, Parab SM, Tsai CL. Predicting mental and psychomotor delay in very pre-term infants using machine learning. Pediatr Res 2024; 95:668-678. [PMID: 37500755 PMCID: PMC10899098 DOI: 10.1038/s41390-023-02713-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 04/25/2023] [Accepted: 06/15/2023] [Indexed: 07/29/2023]
Abstract
BACKGROUND Very preterm infants are at elevated risk for neurodevelopmental delays. Earlier prediction of delays allows timelier intervention and improved outcomes. Machine learning (ML) was used to predict mental and psychomotor delay at 25 months. METHODS We applied RandomForest classifier to data from 1109 very preterm infants recruited over 20 years. ML selected key predictors from 52 perinatal and 16 longitudinal variables (1-22 mo assessments). SHapley Additive exPlanations provided model interpretability. RESULTS Balanced accuracy with perinatal variables was 62%/61% (mental/psychomotor). Top predictors of mental and psychomotor delay overlapped and included: birth year, days in hospital, antenatal MgSO4, days intubated, birth weight, abnormal cranial ultrasound, gestational age, mom's age and education, and intrauterine growth restriction. Highest balanced accuracy was achieved with 19-month follow-up scores and perinatal variables (72%/73%). CONCLUSIONS Combining perinatal and longitudinal data, ML modeling predicted 24 month mental/psychomotor delay in very preterm infants ½ year early, allowing intervention to start that much sooner. Modeling using only perinatal features fell short of clinical application. Birth year's importance reflected a linear decline in predicting delay as birth year became more recent. IMPACT Combining perinatal and longitudinal data, ML modeling was able to predict 24 month mental/psychomotor delay in very preterm infants ½ year early (25% of their lives) potentially advancing implementation of intervention services. Although cognitive/verbal and fine/gross motor delays require separate interventions, in very preterm infants there is substantial overlap in the risk factors that can be used to predict these delays. Birth year has an important effect on ML prediction of delay in very preterm infants, with those born more recently (1989-2009) being increasing less likely to be delayed, perhaps reflecting advances in medical practice.
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Affiliation(s)
- Gözde M Demirci
- Computer Science Department, The Graduate Center of the City University of NY, New York, NY, USA
| | - Phyllis M Kittler
- Department of Infant Development, NYS Institute for Basic Research in Developmental Disabilities, Staten Island, NY, USA
- Pediatrics, Richmond University Medical Center, Staten Island, NY, USA
| | - Ha T T Phan
- Department of Infant Development, NYS Institute for Basic Research in Developmental Disabilities, Staten Island, NY, USA
- Pediatrics, Richmond University Medical Center, Staten Island, NY, USA
| | - Anne D Gordon
- Department of Infant Development, NYS Institute for Basic Research in Developmental Disabilities, Staten Island, NY, USA
- Pediatrics, Richmond University Medical Center, Staten Island, NY, USA
| | - Michael J Flory
- Department of Infant Development, NYS Institute for Basic Research in Developmental Disabilities, Staten Island, NY, USA
| | - Santosh M Parab
- Pediatrics, Richmond University Medical Center, Staten Island, NY, USA
| | - Chia-Ling Tsai
- Computer Science Department, Queens College of the City University of NY, Flushing, NY, USA.
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Yoon SJ, Kim D, Park SH, Han JH, Lim J, Shin JE, Eun HS, Lee SM, Park MS. Prediction of Postnatal Growth Failure in Very Low Birth Weight Infants Using a Machine Learning Model. Diagnostics (Basel) 2023; 13:3627. [PMID: 38132211 PMCID: PMC10743090 DOI: 10.3390/diagnostics13243627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/04/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023] Open
Abstract
Accurate prediction of postnatal growth failure (PGF) can be beneficial for early intervention and prevention. We aimed to develop a machine learning model to predict PGF at discharge among very low birth weight (VLBW) infants using extreme gradient boosting. A total of 729 VLBW infants, born between 2013 and 2017 in four hospitals, were included. PGF was defined as a decrease in z-score between birth and discharge that was greater than 1.28. Feature selection and addition were performed to improve the accuracy of prediction at four different time points, including 0, 7, 14, and 28 days after birth. A total of 12 features with high contribution at all time points by feature importance were decided upon, and good performance was shown as an area under the receiver operating characteristic curve (AUROC) of 0.78 at 7 days. After adding weight change to the 12 features-which included sex, gestational age, birth weight, small for gestational age, maternal hypertension, respiratory distress syndrome, duration of invasive ventilation, duration of non-invasive ventilation, patent ductus arteriosus, sepsis, use of parenteral nutrition, and reach at full enteral nutrition-the AUROC at 7 days after birth was shown as 0.84. Our prediction model for PGF performed well at early detection. Its potential clinical application as a supplemental tool could be helpful for reducing PGF and improving child health.
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Affiliation(s)
- So Jin Yoon
- Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.J.Y.)
| | - Donghyun Kim
- Department of Advanced General Dentistry, Yonsei University College of Dentistry, Seoul 03722, Republic of Korea
- InVisionLab Inc., Seoul 05854, Republic of Korea
| | - Sook Hyun Park
- Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.J.Y.)
| | - Jung Ho Han
- Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.J.Y.)
| | - Joohee Lim
- Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.J.Y.)
| | - Jeong Eun Shin
- Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.J.Y.)
| | - Ho Seon Eun
- Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.J.Y.)
| | - Soon Min Lee
- Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.J.Y.)
| | - Min Soo Park
- Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (S.J.Y.)
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Ushida T, Kotani T, Baba J, Imai K, Moriyama Y, Nakano-Kobayashi T, Iitani Y, Nakamura N, Hayakawa M, Kajiyama H. Antenatal prediction models for outcomes of extremely and very preterm infants based on machine learning. Arch Gynecol Obstet 2023; 308:1755-1763. [PMID: 36502513 DOI: 10.1007/s00404-022-06865-x] [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: 04/23/2022] [Accepted: 11/17/2022] [Indexed: 12/14/2022]
Abstract
PURPOSE Predicting individual risks for adverse outcomes in preterm infants is necessary for perinatal management and antenatal counseling for their parents. To evaluate whether a machine learning approach can improve the prediction of severe infant outcomes beyond the performance of conventional logistic models, and to identify maternal and fetal factors that largely contribute to these outcomes. METHODS A population-based retrospective study was performed using clinical data of 31,157 infants born at < 32 weeks of gestation and weighing ≤ 1500 g, registered in the Neonatal Research Network of Japan between 2006 and 2015. We developed a conventional logistic model and 6 types of machine learning models based on 12 maternal and fetal factors. Discriminative ability was evaluated using the area under the receiver operating characteristic curves (AUROCs), and the importance of each factor in terms of its contribution to outcomes was evaluated using the SHAP (SHapley Additive exPlanations) value. RESULTS The AUROCs of the most discriminative machine learning models were better than those of the conventional models for all outcomes. The AUROCs for in-hospital death and short-term adverse outcomes in the gradient boosting decision tree were significantly higher than those in the conventional model (p = 0.015 and p = 0.002, respectively). The SHAP value analyses showed that gestational age, birth weight, and antenatal corticosteroid treatment were the three most important factors associated with severe infant outcomes. CONCLUSION Machine learning models improve the prediction of severe infant outcomes. Moreover, the machine learning approach provides insight into the potential risk factors for severe infant outcomes.
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Affiliation(s)
- Takafumi Ushida
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan.
- Division of Perinatology, Center for Maternal-Neonatal Care, Nagoya University Hospital, Nagoya, Japan.
| | - Tomomi Kotani
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
- Division of Perinatology, Center for Maternal-Neonatal Care, Nagoya University Hospital, Nagoya, Japan
| | - Joji Baba
- Education Software Co., Ltd, Tokyo, Japan
| | - Kenji Imai
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
| | - Yoshinori Moriyama
- Department of Obstetrics and Gynecology, Fujita Health University School of Medicine, Toyoake, Japan
| | | | - Yukako Iitani
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
| | - Noriyuki Nakamura
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
| | - Masahiro Hayakawa
- Division of Neonatology, Center for Maternal-Neonatal Care, Nagoya University Hospital, Nagoya, Japan
| | - Hiroaki Kajiyama
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
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Keles E, Bagci U. The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review. NPJ Digit Med 2023; 6:220. [PMID: 38012349 PMCID: PMC10682088 DOI: 10.1038/s41746-023-00941-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 10/05/2023] [Indexed: 11/29/2023] Open
Abstract
Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.
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Affiliation(s)
- Elif Keles
- Northwestern University, Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA.
| | - Ulas Bagci
- Northwestern University, Feinberg School of Medicine, Department of Radiology, Chicago, IL, USA
- Northwestern University, Department of Biomedical Engineering, Chicago, IL, USA
- Department of Electrical and Computer Engineering, Chicago, IL, USA
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Iqbal F, Satti MI, Irshad A, Shah MA. Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach. Open Life Sci 2023; 18:20220609. [PMID: 37465102 PMCID: PMC10350886 DOI: 10.1515/biol-2022-0609] [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: 02/15/2023] [Revised: 06/12/2023] [Accepted: 06/12/2023] [Indexed: 07/20/2023] Open
Abstract
In developing countries, child health and restraining under-five child mortality are one of the fundamental concerns. UNICEF adopted sustainable development goal 3 (SDG3) to reduce the under-five child mortality rate globally to 25 deaths per 1,000 live births. The under-five mortality rate is 69 deaths per 1,000 live child-births in Pakistan as reported by the Demographic and Health Survey (2018). Predictive analytics has the power to transform the healthcare industry, personalizing care for every individual. Pakistan Demographic Health Survey (2017-2018), the publicly available dataset, is used in this study and multiple imputation methods are adopted for the treatment of missing values. The information gain, a feature selection method, ranked the information-rich features and examine their impact on child mortality prediction. The synthetic minority over-sampling method (SMOTE) balanced the training dataset, and four supervised machine learning classifiers have been used, namely the decision tree classifier, random forest classifier, naive Bayes classifier, and extreme gradient boosting classifier. For comparative analysis, accuracy, precision, recall, and F1-score have been used. Eventually, a predictive analytics framework is built that predicts whether the child is alive or dead. The number under-five children in a household, preceding birth interval, family members, mother age, age of mother at first birth, antenatal care visits, breastfeeding, child size at birth, and place of delivery were found to be critical risk factors for child mortality. The random forest classifier performed efficiently and predicted under-five child mortality with accuracy (93.8%), precision (0.964), recall (0.971), and F1-score (0.967). The findings could greatly assist child health intervention programs in decision-making.
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Affiliation(s)
- Farrukh Iqbal
- Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Karachi, Pakistan
| | - Muhammad Islam Satti
- Department of Computer Science, Millennium Institute of Technology & Entrepreneurship (MiTE), Karachi, Pakistan
- Department of Computing and IT (DOCIT), The Millennium Universal College (TMUC), Islamabad 44000, Pakistan
| | - Azeem Irshad
- Faculty of Computer Science, Asghar Mall College Rawalpindi, HED, Govt. of Punjab, Pakistan
| | - Mohd Asif Shah
- Department of Economics, Kabridahar University, Po Box 250, Somali, Ethiopia
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Kopanitsa G, Metsker O, Kovalchuk S. Machine Learning Methods for Pregnancy and Childbirth Risk Management. J Pers Med 2023; 13:975. [PMID: 37373964 DOI: 10.3390/jpm13060975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 06/04/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
Machine learning methods enable medical systems to automatically generate data-driven decision support models using real-world data inputs, eliminating the need for explicit rule design. In this research, we investigated the application of machine learning methods in healthcare, specifically focusing on pregnancy and childbirth risks. The timely identification of risk factors during early pregnancy, along with risk management, mitigation, prevention, and adherence management, can significantly reduce adverse perinatal outcomes and complications for both mother and child. Given the existing burden on medical professionals, clinical decision support systems (CDSSs) can play a role in risk management. However, these systems require high-quality decision support models based on validated medical data that are also clinically interpretable. To develop models for predicting childbirth risks and due dates, we conducted a retrospective analysis of electronic health records from the perinatal Center of the Almazov Specialized Medical Center in Saint-Petersburg, Russia. The dataset, which was exported from the medical information system, consisted of structured and semi-structured data, encompassing a total of 73,115 lines for 12,989 female patients. Our proposed approach, which includes a detailed analysis of predictive model performance and interpretability, offers numerous opportunities for decision support in perinatal care provision. The high predictive performance achieved by our models ensures precise support for both individual patient care and overall health organization management.
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Affiliation(s)
- Georgy Kopanitsa
- Faculty of Digital Transformations, ITMO University, 4 Birzhevaya Liniya, 199034 Saint-Petersburg, Russia
- Almazov National Medical Research Centre, Ulitsa Akkuratova, 2, 197341 Saint-Petersburg, Russia
| | - Oleg Metsker
- Almazov National Medical Research Centre, Ulitsa Akkuratova, 2, 197341 Saint-Petersburg, Russia
| | - Sergey Kovalchuk
- Faculty of Digital Transformations, ITMO University, 4 Birzhevaya Liniya, 199034 Saint-Petersburg, Russia
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11
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Natarajan A, Lam G, Liu J, Beam AL, Beam KS, Levin JC. Prediction of extubation failure among low birthweight neonates using machine learning. J Perinatol 2023; 43:209-214. [PMID: 36611107 PMCID: PMC10348822 DOI: 10.1038/s41372-022-01591-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 12/09/2022] [Accepted: 12/14/2022] [Indexed: 01/09/2023]
Abstract
OBJECTIVE To develop machine learning models predicting extubation failure in low birthweight neonates using large amounts of clinical data. STUDY DESIGN Retrospective cohort study using MIMIC-III, a large single-center, open-source clinical dataset. Logistic regression and boosted-tree (XGBoost) models using demographics, medications, and vital sign and ventilatory data were developed to predict extubation failure, defined as reintubation within 7 days. RESULTS 1348 low birthweight (≤2500 g) neonates who received mechanical ventilation within the first 7 days were included, of which 350 (26%) failed a trial of extubation. The best-performing model was a boosted-tree model incorporating demographics, vital signs, ventilator parameters, and medications (AUROC 0.82). The most important features were birthweight, last FiO2, average mean airway pressure, caffeine use, and gestational age. CONCLUSIONS Machine learning models identified low birthweight ventilated neonates at risk for extubation failure. These models will need to be validated across multiple centers to determine generalizability of this tool.
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Affiliation(s)
| | - Grace Lam
- Department of Computer Science, Stanford University, Palo Alto, CA, USA
| | - Jingyi Liu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Andrew L Beam
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Kristyn S Beam
- Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, USA
| | - Jonathan C Levin
- Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, USA.
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12
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Pammi M, Aghaeepour N, Neu J. Multiomics, artificial intelligence, and precision medicine in perinatology. Pediatr Res 2023; 93:308-315. [PMID: 35804156 PMCID: PMC9825681 DOI: 10.1038/s41390-022-02181-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/12/2022] [Accepted: 05/30/2022] [Indexed: 01/11/2023]
Abstract
Technological advances in omics evaluation, bioinformatics, and artificial intelligence have made us rethink ways to improve patient outcomes. Collective quantification and characterization of biological data including genomics, epigenomics, metabolomics, and proteomics is now feasible at low cost with rapid turnover. Significant advances in the integration methods of these multiomics data sets by machine learning promise us a holistic view of disease pathogenesis and yield biomarkers for disease diagnosis and prognosis. Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. In this review, we intend to update the readers on the recent developments related to the use of artificial intelligence in integrating multiomic and clinical data sets in the field of perinatology, focusing on neonatal intensive care and the opportunities for precision medicine. We intend to briefly discuss the potential negative societal and ethical consequences of using artificial intelligence in healthcare. We are poised for a new era in medicine where computational analysis of biological and clinical data sets will make precision medicine a reality. IMPACT: Biotechnological advances have made multiomic evaluations feasible and integration of multiomics data may provide a holistic view of disease pathophysiology. Artificial Intelligence and machine learning tools are being increasingly used in healthcare for diagnosis, prognostication, and outcome predictions. Leveraging artificial intelligence and machine learning tools for integration of multiomics and clinical data will pave the way for precision medicine in perinatology.
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Affiliation(s)
- Mohan Pammi
- Section of Neonatology, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, TX, USA.
| | - Nima Aghaeepour
- Departments of Anesthesiology, Pediatrics, and Biomedical Data Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Josef Neu
- Section of Neonatology, Department of Pediatrics, University of Florida, Gainesville, FL, USA
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13
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Moreira A, Tovar M, Smith AM, Lee GC, Meunier JA, Cheema Z, Moreira A, Winter C, Mustafa SB, Seidner S, Findley T, Garcia JGN, Thébaud B, Kwinta P, Ahuja SK. Development of a peripheral blood transcriptomic gene signature to predict bronchopulmonary dysplasia. Am J Physiol Lung Cell Mol Physiol 2023; 324:L76-L87. [PMID: 36472344 PMCID: PMC9829478 DOI: 10.1152/ajplung.00250.2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 09/27/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
Bronchopulmonary dysplasia (BPD) is the most common lung disease of extreme prematurity, yet mechanisms that associate with or identify neonates with increased susceptibility for BPD are largely unknown. Combining artificial intelligence with gene expression data is a novel approach that may assist in better understanding mechanisms underpinning chronic lung disease and in stratifying patients at greater risk for BPD. The objective of this study is to develop an early peripheral blood transcriptomic signature that can predict preterm neonates at risk for developing BPD. Secondary analysis of whole blood microarray data from 97 very low birth weight neonates on day of life 5 was performed. BPD was defined as positive pressure ventilation or oxygen requirement at 28 days of age. Participants were randomly assigned to a training (70%) and testing cohort (30%). Four gene-centric machine learning models were built, and their discriminatory abilities were compared with gestational age or birth weight. This study adheres to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement. Neonates with BPD (n = 62 subjects) exhibited a lower median gestational age (26.0 wk vs. 30.0 wk, P < 0.01) and birth weight (800 g vs. 1,280 g, P < 0.01) compared with non-BPD neonates. From an initial pool (33,252 genes/patient), 4,523 genes exhibited a false discovery rate (FDR) <1%. The area under the receiver operating characteristic curve (AUC) for predicting BPD utilizing gestational age or birth weight was 87.8% and 87.2%, respectively. The machine learning models, using a combination of five genes, revealed AUCs ranging between 85.8% and 96.1%. Pathways integral to T cell development and differentiation were associated with BPD. A derived five-gene whole blood signature can accurately predict BPD in the first week of life.
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Affiliation(s)
- Alvaro Moreira
- Department of Pediatrics, Neonatology Regenerative and Precision Medicine Laboratory, University of Texas Health Science Center at San Antonio, San Antonio, Texas
- Veterans Administration Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
| | - Miriam Tovar
- Department of Pediatrics, Neonatology Regenerative and Precision Medicine Laboratory, University of Texas Health Science Center at San Antonio, San Antonio, Texas
- Veterans Administration Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
| | - Alisha M Smith
- Veterans Administration Research Center for AIDS and HIV-1 Infection and Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
- The Foundation for Advancing Veterans' Health Research, South Texas Veterans Health Care System, San Antonio, Texas
- Department of Microbiology, Immunology & Molecular Genetics, University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - Grace C Lee
- Veterans Administration Research Center for AIDS and HIV-1 Infection and Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
- Pharmacotherapy Education and Research Center, School of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas
- College of Pharmacy, The University of Texas at Austin, Austin, Texas
| | - Justin A Meunier
- Veterans Administration Research Center for AIDS and HIV-1 Infection and Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
- Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - Zoya Cheema
- Department of Pediatrics, Neonatology Regenerative and Precision Medicine Laboratory, University of Texas Health Science Center at San Antonio, San Antonio, Texas
- Veterans Administration Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
| | - Axel Moreira
- Division of Critical Care, Department of Pediatrics, Baylor College of Medicine, Texas Children's Hospital, Houston, Texas
| | - Caitlyn Winter
- Department of Pediatrics, Neonatology Regenerative and Precision Medicine Laboratory, University of Texas Health Science Center at San Antonio, San Antonio, Texas
- Veterans Administration Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
| | - Shamimunisa B Mustafa
- Department of Pediatrics, Neonatology Regenerative and Precision Medicine Laboratory, University of Texas Health Science Center at San Antonio, San Antonio, Texas
- Veterans Administration Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
| | - Steven Seidner
- Department of Pediatrics, Neonatology Regenerative and Precision Medicine Laboratory, University of Texas Health Science Center at San Antonio, San Antonio, Texas
- Veterans Administration Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
| | - Tina Findley
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center at Houston and Children's Memorial Hermann Hospital, Houston, Texas
| | - Joe G N Garcia
- Department of Medicine, University of Arizona Health Sciences, Tucson, Arizona
| | - Bernard Thébaud
- Sinclair Centre for Regenerative Medicine, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Department of Pediatrics, Children's Hospital of Eastern Ontario (CHEO) and CHEO Research Institute, University of Ottawa, Ottawa, Ontario, Canada
| | - Przemko Kwinta
- Neonatal Intensive Care Unit, Department of Pediatrics, Jagiellonian University Medical College, Krakow, Poland
| | - Sunil K Ahuja
- Veterans Administration Center for Personalized Medicine, South Texas Veterans Health Care System, San Antonio, Texas
- The Foundation for Advancing Veterans' Health Research, South Texas Veterans Health Care System, San Antonio, Texas
- Department of Microbiology, Immunology & Molecular Genetics, University of Texas Health Science Center at San Antonio, San Antonio, Texas
- Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas
- Department of Biochemistry and Structural Biology, University of Texas Health Science Center at San Antonio, San Antonio, Texas
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Silva Rocha ED, de Morais Melo FL, de Mello MEF, Figueiroa B, Sampaio V, Endo PT. On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature. BMC Med Inform Decis Mak 2022; 22:334. [PMID: 36536413 PMCID: PMC9764498 DOI: 10.1186/s12911-022-02082-3] [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: 08/13/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND 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. METHODS 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. RESULTS 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's age. Having predictive models for preventing mortality during and post-pregnancy not only improve the mother's quality of life, as well as it can be a powerful and low-cost tool to decrease mortality ratios. CONCLUSION 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.
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Affiliation(s)
- Elisson da Silva Rocha
- grid.26141.300000 0000 9011 5442Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, Brazil
| | - Flavio Leandro de Morais Melo
- grid.26141.300000 0000 9011 5442Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, Brazil
| | | | - Barbara Figueiroa
- Programa Mãe Coruja Pernambucana, Secretaria de Saúde do Estado de Pernambuco, Recife, Brazil
| | | | - Patricia Takako Endo
- grid.26141.300000 0000 9011 5442Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, Brazil
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15
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McAdams RM, Kaur R, Sun Y, Bindra H, Cho SJ, Singh H. Predicting clinical outcomes using artificial intelligence and machine learning in neonatal intensive care units: a systematic review. J Perinatol 2022; 42:1561-1575. [PMID: 35562414 DOI: 10.1038/s41372-022-01392-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 03/30/2022] [Accepted: 04/01/2022] [Indexed: 01/19/2023]
Abstract
BACKGROUND Advances in technology, data availability, and analytics have helped improve quality of care in the neonatal intensive care unit. OBJECTIVE To provide an in-depth review of artificial intelligence (AI) and machine learning techniques being utilized to predict neonatal outcomes. METHODS The PRISMA protocol was followed that considered articles from established digital repositories. Included articles were categorized based on predictions of: (a) major neonatal morbidities such as sepsis, bronchopulmonary dysplasia, intraventricular hemorrhage, necrotizing enterocolitis, and retinopathy of prematurity; (b) mortality; and (c) length of stay. RESULTS A total of 366 studies were considered; 68 studies were eligible for inclusion in the review. The current set of predictor models are primarily built on supervised learning and mostly used regression models built on retrospective data. CONCLUSION With the availability of EMR data and data-sharing of NICU outcomes across neonatal research networks, machine learning algorithms have shown breakthrough performance in predicting neonatal disease.
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Affiliation(s)
- Ryan M McAdams
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Ravneet Kaur
- Child Health Imprints (CHIL) USA Inc, Madison, WI, USA
| | - Yao Sun
- Division of Neonatology, University of California San Francisco, San Francisco, CA, USA
| | | | - Su Jin Cho
- College of Medicine, Ewha Womans University Seoul, Seoul, Korea
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Teji JS, Jain S, Gupta SK, Suri JS. NeoAI 1.0: Machine learning-based paradigm for prediction of neonatal and infant risk of death. Comput Biol Med 2022; 147:105639. [DOI: 10.1016/j.compbiomed.2022.105639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 05/01/2022] [Accepted: 05/01/2022] [Indexed: 11/29/2022]
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Artificial Intelligence in NICU and PICU: A Need for Ecological Validity, Accountability, and Human Factors. Healthcare (Basel) 2022; 10:healthcare10050952. [PMID: 35628089 PMCID: PMC9140402 DOI: 10.3390/healthcare10050952] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 05/17/2022] [Accepted: 05/19/2022] [Indexed: 02/04/2023] Open
Abstract
Pediatric patients, particularly in neonatal and pediatric intensive care units (NICUs and PICUs), are typically at an increased risk of fatal decompensation. That being said, any delay in treatment or minor errors in medication dosage can overcomplicate patient health. Under such an environment, clinicians are expected to quickly and effectively comprehend large volumes of medical information to diagnose and develop a treatment plan for any baby. The integration of Artificial Intelligence (AI) into the clinical workflow can be a potential solution to safeguard pediatric patients and augment the quality of care. However, before making AI an integral part of pediatric care, it is essential to evaluate the technology from a human factors perspective, ensuring its readiness (technology readiness level) and ecological validity. Addressing AI accountability is also critical to safeguarding clinicians and improving AI acceptance in the clinical workflow. This article summarizes the application of AI in NICU/PICU and consecutively identifies the existing flaws in AI (from clinicians’ standpoint), and proposes related recommendations, which, if addressed, can improve AIs’ readiness for a real clinical environment.
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18
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Mfateneza E, Rutayisire PC, Biracyaza E, Musafiri S, Mpabuka WG. Application of machine learning methods for predicting infant mortality in Rwanda: analysis of Rwanda demographic health survey 2014-15 dataset. BMC Pregnancy Childbirth 2022; 22:388. [PMID: 35509018 PMCID: PMC9066935 DOI: 10.1186/s12884-022-04699-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 04/18/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Extensive research on infant mortality (IM) exists in developing countries; however, most of the methods applied thus far relied on conventional regression analyses with limited prediction capability. Advanced of Machine Learning (AML) methods provide accurate prediction of IM; however, there is no study conducted using ML methods in Rwanda. This study, therefore, applied Machine Learning Methods for predicting infant mortality in Rwanda. METHODS: A cross-sectional study design was conducted using the 2014-15 Rwanda Demographic and Health Survey. Python software version 3.8 was employed to test and apply ML methods through Random Forest (RF), Decision Tree, Support Vector Machine and Logistic regression. STATA version 13 was used for analysing conventional methods. Evaluation metrics methods specifically confusion matrix, accuracy, precision, recall, F1 score, and Area under the Receiver Operating Characteristics (AUROC) were used to evaluate the performance of predictive models. RESULTS Ability of prediction was between 68.6% and 61.5% for AML. We preferred with the RF model (61.5%) presenting the best performance. The RF model was the best predictive model of IM with accuracy (84.3%), recall (91.3%), precision (80.3%), F1 score (85.5%), and AUROC (84.2%); followed by decision tree model with model accuracy (83%), recall (91%), precision (79%), F1 score (84.67%) and AUROC(82.9%), followed by support vector machine with model accuracy (68.6%), recall (74.9%), precision(67%), F1 score (70.73%) and AUROC (68.6%) and last was a logistic regression with the low accuracy of prediction (61.5%), recall (61.1%), precision (62.2%), F1 score (61.6%) and AUROC (61.5%) compared to other predictive models. Our predictive models showed that marital status, children ever born, birth order and wealth index are the 4 top predictors of IM. CONCLUSIONS In developing a predictive model, ML methods are used to classify certain hidden information that could not be detected by traditional statistical methods. Random Forest was classified as the best classifier to be used for the predictive models of IM.
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Affiliation(s)
- Emmanuel Mfateneza
- African Centre of Excellence in Data Science, University of Rwanda, Kigali, Rwanda
| | | | | | - Sanctus Musafiri
- Clinical Department of Internal Medicine, University of Rwanda, Kigali, Rwanda
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Umamaheswaran S., John R, Nagarajan S., Karthick Raghunath K. M., Arvind K. S.. Predictive Assessment of Fetus Features Using Scanned Image Segmentation Techniques and Deep Learning Strategy. INTERNATIONAL JOURNAL OF E-COLLABORATION 2022. [DOI: 10.4018/ijec.307130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Fetus weight at various stages of pregnancy is a critical component in determining the health of the baby. Abnormalities arising early in the pregnancy may be prevented by preventive measures. A variety of techniques suggested to predict foetus weight. Computer vision is a capability that can estimate the weight of a baby based on ultra-sonograms taken at various stages of pregnancy. Using the scanned data, one may train an advanced convolutional neural network that helps in accurately forecasting the fetus's size, weight, and overall health. The research utilizes computer vision techniques with image clustering methods for preprocessing, to predict the foetus's health, training datasets defective foetus datasets and healthy foetus datasets. Developing an integrated computer vision and a deep neural network is the hour which decrease the cost of operations and manual processes This study estimate the fetus's weight with optimal accuracy range at varying gestation age.
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Machine Learning Models for Predicting Mortality in 7472 Very Low Birth Weight Infants Using Data from a Nationwide Neonatal Network. Diagnostics (Basel) 2022; 12:diagnostics12030625. [PMID: 35328178 PMCID: PMC8947011 DOI: 10.3390/diagnostics12030625] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 02/26/2022] [Accepted: 03/01/2022] [Indexed: 11/30/2022] Open
Abstract
Statistical and analytical methods using artificial intelligence approaches such as machine learning (ML) are increasingly being applied to the field of pediatrics, particularly to neonatology. This study compared the representative ML analysis and the logistic regression (LR), which is a traditional statistical analysis method, using them to predict mortality of very low birth weight infants (VLBWI). We included 7472 VLBWI data from a nationwide Korean neonatal network. Eleven predictor variables (neonatal factors: male sex, gestational age, 5 min Apgar scores, body temperature, and resuscitation at birth; maternal factors: diabetes mellitus, hypertension, chorioamnionitis, premature rupture of membranes, antenatal steroid, and cesarean delivery) were selected based on clinical impact and statistical analysis. We compared the predicted mortality between ML methods—such as artificial neural network (ANN), random forest (RF), and support vector machine (SVM)—and LR with a randomly selected training set (80%) and a test set (20%). The model performances of area under the receiver operating curve (95% confidence interval) equaled LR 0.841 (0.811−0.872), ANN 0.845 (0.815−0.875), and RF 0.826 (0.795−0.858). The exception was SVM 0.631 (0.578−0.683). No statistically significant differences were observed between the performance of LR, ANN, and RF (i.e., p > 0.05). However, the SVM model was lower (p < 0.01). We suggest that VLBWI mortality prediction using ML methods would yield the same prediction rate as the traditional statistical LR method and may be suitable for predicting mortality. However, low prediction rates are observed in certain ML methods; hence, further research is needed on these limitations and selecting an appropriate method.
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Adegboro CO, Choudhury A, Asan O, Kelly MM. Artificial Intelligence to Improve Health Outcomes in the NICU and PICU: A Systematic Review. Hosp Pediatr 2022; 12:93-110. [PMID: 34890453 DOI: 10.1542/hpeds.2021-006094] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
CONTEXT Artificial intelligence (AI) technologies are increasingly used in pediatrics and have the potential to help inpatient physicians provide high-quality care for critically ill children. OBJECTIVE We aimed to describe the use of AI to improve any health outcome(s) in neonatal and pediatric intensive care. DATA SOURCE PubMed, IEEE Xplore, Cochrane, and Web of Science databases. STUDY SELECTION We used peer-reviewed studies published between June 1, 2010, and May 31, 2020, in which researchers described (1) AI, (2) pediatrics, and (3) intensive care. Studies were included if researchers assessed AI use to improve at least 1 health outcome (eg, mortality). DATA EXTRACTION Data extraction was conducted independently by 2 researchers. Articles were categorized by direct or indirect impact of AI, defined by the European Institute of Innovation and Technology Health joint report. RESULTS Of the 287 publications screened, 32 met inclusion criteria. Approximately 22% (n = 7) of studies revealed a direct impact and improvement in health outcomes after AI implementation. Majority were in prototype testing, and few were deployed into an ICU setting. Among the remaining 78% (n = 25) AI models outperformed standard clinical modalities and may have indirectly influenced patient outcomes. Quantitative assessment of health outcomes using statistical measures, such as area under the receiver operating curve (56%; n = 18) and specificity (38%; n = 12), revealed marked heterogeneity in metrics and standardization. CONCLUSIONS Few studies have revealed that AI has directly improved health outcomes for pediatric critical care patients. Further prospective, experimental studies are needed to assess AI's impact by using established implementation frameworks, standardized metrics, and validated outcome measures.
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Affiliation(s)
- Claudette O Adegboro
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
| | - Avishek Choudhury
- Division of Engineering Management, School of Systems and Enterprise, Stevens Institute of Technology, Hoboken, New Jersey
| | - Onur Asan
- Division of Engineering Management, School of Systems and Enterprise, Stevens Institute of Technology, Hoboken, New Jersey
| | - Michelle M Kelly
- Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin
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Amodeo I, De Nunzio G, Raffaeli G, Borzani I, Griggio A, Conte L, Macchini F, Condò V, Persico N, Fabietti I, Ghirardello S, Pierro M, Tafuri B, Como G, Cascio D, Colnaghi M, Mosca F, Cavallaro G. A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study. PLoS One 2021; 16:e0259724. [PMID: 34752491 PMCID: PMC8577746 DOI: 10.1371/journal.pone.0259724] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 10/25/2021] [Indexed: 02/04/2023] Open
Abstract
INTRODUCTION Outcome predictions of patients with congenital diaphragmatic hernia (CDH) still have some limitations in the prenatal estimate of postnatal pulmonary hypertension (PH). We propose applying Machine Learning (ML), and Deep Learning (DL) approaches to fetuses and newborns with CDH to develop forecasting models in prenatal epoch, based on the integrated analysis of clinical data, to provide neonatal PH as the first outcome and, possibly: favorable response to fetal endoscopic tracheal occlusion (FETO), need for Extracorporeal Membrane Oxygenation (ECMO), survival to ECMO, and death. Moreover, we plan to produce a (semi)automatic fetus lung segmentation system in Magnetic Resonance Imaging (MRI), which will be useful during project implementation but will also be an important tool itself to standardize lung volume measures for CDH fetuses. METHODS AND ANALYTICS Patients with isolated CDH from singleton pregnancies will be enrolled, whose prenatal checks were performed at the Fetal Surgery Unit of the Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (Milan, Italy) from the 30th week of gestation. A retrospective data collection of clinical and radiological variables from newborns' and mothers' clinical records will be performed for eligible patients born between 01/01/2012 and 31/12/2020. The native sequences from fetal magnetic resonance imaging (MRI) will be collected. Data from different sources will be integrated and analyzed using ML and DL, and forecasting algorithms will be developed for each outcome. Methods of data augmentation and dimensionality reduction (feature selection and extraction) will be employed to increase sample size and avoid overfitting. A software system for automatic fetal lung volume segmentation in MRI based on the DL 3D U-NET approach will also be developed. ETHICS AND DISSEMINATION This retrospective study received approval from the local ethics committee (Milan Area 2, Italy). The development of predictive models in CDH outcomes will provide a key contribution in disease prediction, early targeted interventions, and personalized management, with an overall improvement in care quality, resource allocation, healthcare, and family savings. Our findings will be validated in a future prospective multicenter cohort study. REGISTRATION The study was registered at ClinicalTrials.gov with the identifier NCT04609163.
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Affiliation(s)
- Ilaria Amodeo
- NICU, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Giorgio De Nunzio
- Department of Mathematics and Physics “E. De Giorgi”, Laboratory of Biomedical Physics and Environment, Università del Salento, Lecce, Italy
- Advanced Data Analysis in Medicine (ADAM), Laboratory of Interdisciplinary Research Applied to Medicine (DReAM), Università del Salento, Lecce, Italy
- Azienda Sanitaria Locale (ASL), Lecce, Italy
| | - Genny Raffaeli
- NICU, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy
| | - Irene Borzani
- Pediatric Radiology Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Alice Griggio
- Monza and Brianza Mother and Child Foundation, San Gerardo Hospital, Università degli Studi di Milano-Bicocca, Monza, Italy
| | - Luana Conte
- Department of Mathematics and Physics “E. De Giorgi”, Laboratory of Biomedical Physics and Environment, Università del Salento, Lecce, Italy
- Advanced Data Analysis in Medicine (ADAM), Laboratory of Interdisciplinary Research Applied to Medicine (DReAM), Università del Salento, Lecce, Italy
- Azienda Sanitaria Locale (ASL), Lecce, Italy
| | - Francesco Macchini
- Department of Pediatric Surgery, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Valentina Condò
- NICU, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Nicola Persico
- Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy
- Department of Obstetrics and Gynecology, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Isabella Fabietti
- Department of Obstetrics and Gynecology, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Stefano Ghirardello
- NICU, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Maria Pierro
- NICU, Bufalini Hospital, Azienda Unità Sanitaria Locale della Romagna, Cesena, Italy
| | - Benedetta Tafuri
- Department of Mathematics and Physics “E. De Giorgi”, Laboratory of Biomedical Physics and Environment, Università del Salento, Lecce, Italy
- Advanced Data Analysis in Medicine (ADAM), Laboratory of Interdisciplinary Research Applied to Medicine (DReAM), Università del Salento, Lecce, Italy
- Azienda Sanitaria Locale (ASL), Lecce, Italy
| | - Giuseppe Como
- NICU, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Donato Cascio
- Department of Physics and Chemistry, Università degli Studi di Palermo, Palermo, Italy
| | - Mariarosa Colnaghi
- NICU, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Fabio Mosca
- NICU, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Clinical Sciences and Community Health, Università degli Studi di Milano, Milan, Italy
| | - Giacomo Cavallaro
- NICU, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
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Machine Learning Approaches to Predict In-Hospital Mortality among Neonates with Clinically Suspected Sepsis in the Neonatal Intensive Care Unit. J Pers Med 2021; 11:jpm11080695. [PMID: 34442338 PMCID: PMC8400295 DOI: 10.3390/jpm11080695] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 07/12/2021] [Accepted: 07/21/2021] [Indexed: 01/21/2023] Open
Abstract
Background: preterm and critically ill neonates often experience clinically suspected sepsis during their prolonged hospitalization in the neonatal intensive care unit (NICU), which can be the initial sign of final adverse outcomes. Therefore, we aimed to utilize machine learning approaches to predict neonatal in-hospital mortality through data-driven learning. Methods: a total of 1095 neonates who experienced clinically suspected sepsis in a tertiary-level NICU in Taiwan between August 2017 and July 2020 were enrolled. Clinically suspected sepsis was defined based on clinical features and laboratory criteria and the administration of empiric antibiotics by clinicians. The variables used for analysis included patient demographics, clinical features, laboratory data, and medications. The machine learning methods used included deep neural network (DNN), k-nearest neighbors, support vector machine, random forest, and extreme gradient boost. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC). Results: the final in-hospital mortality of this cohort was 8.2% (90 neonates died). A total of 765 (69.8%) and 330 (30.2%) patients were randomly assigned to the training and test sets, respectively. Regarding the efficacy of the single model that most accurately predicted the outcome, DNN exhibited the greatest AUC (0.923, 95% confidence interval [CI] 0.953–0.893) and the best accuracy (95.64%, 95% CI 96.76–94.52%), Cohen’s kappa coefficient value (0.74, 95% CI 0.79–0.69) and Matthews correlation coefficient value (0.75, 95% CI 0.80–0.70). The top three most influential variables in the DNN importance matrix plot were the requirement of ventilator support at the onset of suspected sepsis, the feeding conditions, and intravascular volume expansion. The model performance was indistinguishable between the training and test sets. Conclusions: the DNN model was successfully established to predict in-hospital mortality in neonates with clinically suspected sepsis, and the machine learning algorithm is applicable for clinicians to gain insights and have better communication with families in advance.
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24
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Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data. NPJ Digit Med 2021; 4:108. [PMID: 34262112 PMCID: PMC8280207 DOI: 10.1038/s41746-021-00479-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 06/21/2021] [Indexed: 11/17/2022] Open
Abstract
Mortality remains an exceptional burden of extremely preterm birth. Current clinical mortality prediction scores are calculated using a few static variable measurements, such as gestational age, birth weight, temperature, and blood pressure at admission. While these models do provide some insight, numerical and time-series vital sign data are also available for preterm babies admitted to the NICU and may provide greater insight into outcomes. Computational models that predict the mortality risk of preterm birth in the NICU by integrating vital sign data and static clinical variables in real time may be clinically helpful and potentially superior to static prediction models. However, there is a lack of established computational models for this specific task. In this study, we developed a novel deep learning model, DeepPBSMonitor (Deep Preterm Birth Survival Risk Monitor), to predict the mortality risk of preterm infants during initial NICU hospitalization. The proposed deep learning model can effectively integrate time-series vital sign data and fixed variables while resolving the influence of noise and imbalanced data. The proposed model was evaluated and compared with other approaches using data from 285 infants. Results showed that the DeepPBSMonitor model outperforms other approaches, with an accuracy, recall, and AUC score of 0.888, 0.780, and 0.897, respectively. In conclusion, the proposed model has demonstrated efficacy in predicting the real-time mortality risk of preterm infants in initial NICU hospitalization.
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25
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Baker S, Xiang W, Atkinson I. Hybridized neural networks for non-invasive and continuous mortality risk assessment in neonates. Comput Biol Med 2021; 134:104521. [PMID: 34111664 DOI: 10.1016/j.compbiomed.2021.104521] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 05/06/2021] [Accepted: 05/25/2021] [Indexed: 11/19/2022]
Abstract
Premature birth is the primary risk factor in neonatal deaths, with the majority of extremely premature babies cared for in neonatal intensive care units (NICUs). Mortality risk prediction in this setting can greatly improve patient outcomes and resource utilization. However, existing schemes often require laborious medical testing and calculation, and are typically only calculated once at admission. In this work, we propose a shallow hybrid neural network for the prediction of mortality risk in 3-day, 7-day, and 14-day risk windows using only birthweight, gestational age, sex, and heart rate (HR) and respiratory rate (RR) information from a 12-h window. As such, this scheme is capable of continuously updating mortality risk assessment, enabling analysis of health trends and responses to treatment. The highest performing scheme was the network that considered mortality risk within 3 days, with this scheme outperforming state-of-the-art works in the literature and achieving an area under the receiver-operator curve (AUROC) of 0.9336 with standard deviation of 0.0337 across 5 folds of cross-validation. As such, we conclude that our proposed scheme could readily be used for continuously-updating mortality risk prediction in NICU environments.
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Affiliation(s)
- Stephanie Baker
- College of Science & Engineering, James Cook University, Cairns, Queensland, 4878, Australia.
| | - Wei Xiang
- School of Engineering and Mathematical Sciences, La Trobe University, Melbourne, Victoria, 3086, Australia
| | - Ian Atkinson
- eResearch Centre, James Cook University, Townsville, Queensland, 4811, Australia
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26
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Tang BH, Guan Z, Allegaert K, Wu YE, Manolis E, Leroux S, Yao BF, Shi HY, Li X, Huang X, Wang WQ, Shen AD, Wang XL, Wang TY, Kou C, Xu HY, Zhou Y, Zheng Y, Hao GX, Xu BP, Thomson AH, Capparelli EV, Biran V, Simon N, Meibohm B, Lo YL, Marques R, Peris JE, Lutsar I, Saito J, Burggraaf J, Jacqz-Aigrain E, van den Anker J, Zhao W. Drug Clearance in Neonates: A Combination of Population Pharmacokinetic Modelling and Machine Learning Approaches to Improve Individual Prediction. Clin Pharmacokinet 2021; 60:1435-1448. [PMID: 34041714 DOI: 10.1007/s40262-021-01033-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex problems in the current era of big data. OBJECTIVE The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates. METHODS Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods. RESULTS The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods. CONCLUSION A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.
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Affiliation(s)
- Bo-Hao Tang
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Zheng Guan
- Centre for Human Drug Research, Leiden, The Netherlands.,Leiden University Medical Center, Leiden, The Netherlands
| | - Karel Allegaert
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium.,Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
| | - Yue-E Wu
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Efthymios Manolis
- Modelling and Simulation Working Party, European Medicines Agency, Amsterdam, The Netherlands
| | | | - Bu-Fan Yao
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Hai-Yan Shi
- Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China
| | - Xiao Li
- Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China
| | - Xin Huang
- Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China.,Clinical Research Center, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China
| | - Wen-Qi Wang
- Clinical Research Center, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China
| | - A-Dong Shen
- Key Laboratory of Major Diseases in Children and National Key Discipline of Pediatrics (Capital Medical University), Ministry of Education, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Xiao-Ling Wang
- Clinical Research Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, People's Republic of China
| | - Tian-You Wang
- Clinical Research Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, People's Republic of China
| | - Chen Kou
- Department of Neonatology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Hai-Yan Xu
- Department of Pediatrics, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China
| | - Yue Zhou
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Yi Zheng
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Guo-Xiang Hao
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Bao-Ping Xu
- Department of Respiratory Diseases, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, People's Republic of China
| | - Alison H Thomson
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Edmund V Capparelli
- Pediatric Pharmacology and Drug Discovery, University of California, San Diego, CA, USA
| | - Valerie Biran
- Neonatal Intensive Care Unit, Hospital Robert Debre, Paris, France
| | - Nicolas Simon
- Aix Marseille Univ, APHM, INSERM, IRD, SESSTIM, Hop Sainte Marguerite, Service de Pharmacologie Clinique, CAP-TV, Marseille, France
| | - Bernd Meibohm
- Department of Pharmaceutical Sciences, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Yoke-Lin Lo
- Department of Pharmacy, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.,School of Pharmacy, International Medical University, Kuala Lumpur, Malaysia
| | - Remedios Marques
- Department of Pharmacy Services, La Fe Hospital, Valencia, Spain
| | - Jose-Esteban Peris
- Department of Pharmacy and Pharmaceutical Technology, University of Valencia, Valencia, Spain
| | - Irja Lutsar
- Institute of Medical Microbiology, University of Tartu, Tartu, Estonia
| | - Jumpei Saito
- Department of Pharmacy, National Children's Hospital National Center for Child Health and Development, Tokyo, Japan
| | - Jacobus Burggraaf
- Centre for Human Drug Research, Leiden, The Netherlands.,Leiden University Medical Center, Leiden, The Netherlands
| | - Evelyne Jacqz-Aigrain
- Department of Pediatric Pharmacology and Pharmacogenetics, Hospital Robert Debre, APHP, Paris, France.,Clinical Investigation Center CIC1426, Hoŝpital Robert Debre, Paris, France.,University Paris Diderot, Sorbonne Paris Cite, Paris, France
| | - John van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA.,Departments of Pediatrics, Pharmacology and Physiology, Genomics and Precision Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC, USA.,Department of Paediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital, Basel, Switzerland
| | - Wei Zhao
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China. .,Modelling and Simulation Working Party, European Medicines Agency, Amsterdam, The Netherlands. .,Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China. .,Clinical Research Center, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China.
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27
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van Beek PE, Andriessen P, Onland W, Schuit E. Prognostic Models Predicting Mortality in Preterm Infants: Systematic Review and Meta-analysis. Pediatrics 2021; 147:peds.2020-020461. [PMID: 33879518 DOI: 10.1542/peds.2020-020461] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/27/2021] [Indexed: 11/24/2022] Open
Abstract
CONTEXT Prediction models can be a valuable tool in performing risk assessment of mortality in preterm infants. OBJECTIVE Summarizing prognostic models for predicting mortality in very preterm infants and assessing their quality. DATA SOURCES Medline was searched for all articles (up to June 2020). STUDY SELECTION All developed or externally validated prognostic models for mortality prediction in liveborn infants born <32 weeks' gestation and/or <1500 g birth weight were included. DATA EXTRACTION Data were extracted by 2 independent authors. Risk of bias (ROB) and applicability assessment was performed by 2 independent authors using Prediction model Risk of Bias Assessment Tool. RESULTS One hundred forty-two models from 35 studies reporting on model development and 112 models from 33 studies reporting on external validation were included. ROB assessment revealed high ROB in the majority of the models, most often because of inadequate (reporting of) analysis. Internal and external validation was lacking in 41% and 96% of these models. Meta-analyses revealed an average C-statistic of 0.88 (95% confidence interval [CI]: 0.83-0.91) for the Clinical Risk Index for Babies score, 0.87 (95% CI: 0.81-0.92) for the Clinical Risk Index for Babies II score, and 0.86 (95% CI: 0.78-0.92) for the Score for Neonatal Acute Physiology Perinatal Extension II score. LIMITATIONS Occasionally, an external validation study was included, but not the development study, because studies developed in the presurfactant era or general NICU population were excluded. CONCLUSIONS Instead of developing additional mortality prediction models for preterm infants, the emphasis should be shifted toward external validation and consecutive adaption of the existing prediction models.
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Affiliation(s)
- Pauline E van Beek
- Department of Neonatology, Máxima Medical Centre, Veldhoven, Netherlands;
| | - Peter Andriessen
- Department of Neonatology, Máxima Medical Centre, Veldhoven, Netherlands.,Department of Applied Physics, School of Medical Physics and Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Wes Onland
- Department of Neonatology, Amsterdam University Medical Centers and University of Amsterdam, Amsterdam, Netherlands
| | - Ewoud Schuit
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, Netherlands; and.,Cochrane Netherlands, University Medical Center Utrecht and Utrecht University, Utrecht, Netherlands
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28
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Sun Y, Kaur R, Gupta S, Paul R, Das R, Cho SJ, Anand S, Boutilier JJ, Saria S, Palma J, Saluja S, McAdams RM, Kaur A, Yadav G, Singh H. Development and validation of high definition phenotype-based mortality prediction in critical care units. JAMIA Open 2021; 4:ooab004. [PMID: 33796821 PMCID: PMC7991779 DOI: 10.1093/jamiaopen/ooab004] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 01/12/2021] [Accepted: 01/24/2021] [Indexed: 12/02/2022] Open
Abstract
Objectives The objectives of this study are to construct the high definition phenotype (HDP), a novel time-series data structure composed of both primary and derived parameters, using heterogeneous clinical sources and to determine whether different predictive models can utilize the HDP in the neonatal intensive care unit (NICU) to improve neonatal mortality prediction in clinical settings. Materials and Methods A total of 49 primary data parameters were collected from July 2018 to May 2020 from eight level-III NICUs. From a total of 1546 patients, 757 patients were found to contain sufficient fixed, intermittent, and continuous data to create HDPs. Two different predictive models utilizing the HDP, one a logistic regression model (LRM) and the other a deep learning long–short-term memory (LSTM) model, were constructed to predict neonatal mortality at multiple time points during the patient hospitalization. The results were compared with previous illness severity scores, including SNAPPE, SNAPPE-II, CRIB, and CRIB-II. Results A HDP matrix, including 12 221 536 minutes of patient stay in NICU, was constructed. The LRM model and the LSTM model performed better than existing neonatal illness severity scores in predicting mortality using the area under the receiver operating characteristic curve (AUC) metric. An ablation study showed that utilizing continuous parameters alone results in an AUC score of >80% for both LRM and LSTM, but combining fixed, intermittent, and continuous parameters in the HDP results in scores >85%. The probability of mortality predictive score has recall and precision of 0.88 and 0.77 for the LRM and 0.97 and 0.85 for the LSTM. Conclusions and Relevance The HDP data structure supports multiple analytic techniques, including the statistical LRM approach and the machine learning LSTM approach used in this study. LRM and LSTM predictive models of neonatal mortality utilizing the HDP performed better than existing neonatal illness severity scores. Further research is necessary to create HDP–based clinical decision tools to detect the early onset of neonatal morbidities.
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Affiliation(s)
- Yao Sun
- Division of Neonatology, Department of Pediatrics, University of California San Francisco, San Francisco, California, USA
| | - Ravneet Kaur
- Research and Development, Child Health Imprints (CHIL) Pte. Ltd., Singapore
| | - Shubham Gupta
- Research and Development, Child Health Imprints (CHIL) Pte. Ltd., Singapore
| | - Rahul Paul
- Research and Development, Child Health Imprints (CHIL) Pte. Ltd., Singapore
| | - Ritu Das
- Research and Development, Child Health Imprints (CHIL) Pte. Ltd., Singapore
| | - Su Jin Cho
- Department of Pediatrics, College of Medicine, Ewha Womans University Seoul, Seoul, Korea
| | - Saket Anand
- Department of Computer Science, Indraprastha Institute of Information Technology, New Delhi, India
| | - Justin J Boutilier
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Wisconsin, USA
| | - Suchi Saria
- Machine Learning and Healthcare Lab, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Applied Math & Statistics, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Health Policy & Management, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jonathan Palma
- Department of Pediatrics, Stanford University, Stanford, California, USA
| | - Satish Saluja
- Department of Neonatology, Sir Ganga Ram Hospital, New Delhi, India
| | - Ryan M McAdams
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Avneet Kaur
- Department of Neonatology, Apollo Cradle Hospitals, New Delhi, India
| | - Gautam Yadav
- Department of Pediatrics, Kalawati Hospital, Rewari, India
| | - Harpreet Singh
- Research and Development, Child Health Imprints (CHIL) Pte. Ltd., Singapore
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29
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Ushida T, Moriyama Y, Nakatochi M, Kobayashi Y, Imai K, Nakano-Kobayashi T, Nakamura N, Hayakawa M, Kajiyama H, Kotani T. Antenatal prediction models for short- and medium-term outcomes in preterm infants. Acta Obstet Gynecol Scand 2021; 100:1089-1096. [PMID: 33656762 DOI: 10.1111/aogs.14136] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 02/20/2021] [Accepted: 02/26/2021] [Indexed: 01/08/2023]
Abstract
INTRODUCTION In extremely and very preterm infants, predicting individual risks for adverse outcomes antenatally is challenging but necessary for risk-stratified perinatal management and parents' participation in decision-making about treatment. Our aim was to develop and validate prediction models for short-term (neonatal period) and medium-term (3 years of age) outcomes based on antenatal maternal and fetal factors alone. MATERIAL AND METHODS A population-based study was conducted on 31 157 neonates weighing ≤1500 g and born between 22 and 31 weeks of gestation registered in the Neonatal Research Network of Japan during 2006-2015. Short-term outcomes were assessed in 31 157 infants and medium-term outcomes were assessed in 13 751 infants among the 31 157 infants. The clinical data were randomly divided into training and validation data sets in a ratio of 2:1. The prediction models were developed by factors selected using stepwise logistic regression from 12 antenatal maternal and fetal factors with the training data set. The number of factors incorporated into the model varied from 3 to 10, on the basis of each outcome. To evaluate predictive performance, the area under the receiver operating characteristics curve (AUROC) was calculated for each outcome with the validation data set. RESULTS Among short-term outcomes, AUROCs for in-hospital death, chronic lung disease, intraventricular hemorrhage (grade III or IV) and periventricular leukomalacia were 0.85 (95% CI 0.83-0.86), 0.80 (95% CI 0.79-0.81), 0.78 (95% CI 0.75-0.80), and 0.58 (95% CI 0.55-0.61), respectively. Among medium-term outcomes, AUROCs for cerebral palsy and developmental quotient of <70 at 3 years of age were 0.66 (95% CI 0.63-0.69) and 0.72 (95% CI 0.70-0.74), respectively. CONCLUSIONS Although the predictive performance of these models varied for each outcome, their discriminative ability for in-hospital death, chronic lung disease, and intraventricular hemorrhage (grade III or IV) was relatively good. We provided a bedside prediction tool for calculating the likelihood of various infant complications for clinical use. To develop these prediction models would be valuable in each country, and these risk assessment tools could facilitate risk-stratified perinatal management and parents' shared understanding of their infants' subsequent risks.
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Affiliation(s)
- Takafumi Ushida
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Japan.,Division of Perinatology, Center for Maternal-Neonatal Care, Nagoya University Hospital, Nagoya, Japan
| | - Yoshinori Moriyama
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Japan.,Department of Obstetrics and Gynecology, Fujita Health University School of Medicine, Toyoake, Japan
| | - Masahiro Nakatochi
- Division of Public Health Informatics, Department of Integrative Health Science, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yumiko Kobayashi
- Data Science Division, Data Coordinating Center, Department of Advanced Medicine, Nagoya University Hospital, Nagoya, Japan
| | - Kenji Imai
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Tomoko Nakano-Kobayashi
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Noriyuki Nakamura
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Masahiro Hayakawa
- Division of Neonatology, Center for Maternal-Neonatal Care, Nagoya University Hospital, Nagoya, Japan
| | - Hiroaki Kajiyama
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Tomomi Kotani
- Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Japan.,Division of Perinatology, Center for Maternal-Neonatal Care, Nagoya University Hospital, Nagoya, Japan
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Mangold C, Zoretic S, Thallapureddy K, Moreira A, Chorath K, Moreira A. Machine Learning Models for Predicting Neonatal Mortality: A Systematic Review. Neonatology 2021; 118:394-405. [PMID: 34261070 PMCID: PMC8887024 DOI: 10.1159/000516891] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 04/27/2021] [Indexed: 11/19/2022]
Abstract
INTRODUCTION Approximately 7,000 newborns die every day, accounting for almost half of child deaths under 5 years of age. Deciphering which neonates are at increased risk for mortality can have an important global impact. As such, integrating high computational technology (e.g., artificial intelligence [AI]) may help identify the early and potentially modifiable predictors of neonatal mortality. Therefore, the objective of this study was to collate, critically appraise, and analyze neonatal prediction studies that included AI. METHODS A literature search was performed in PubMed, Cochrane, OVID, and Google Scholar. We included studies that used AI (e.g., machine learning (ML) and deep learning) to formulate prediction models for neonatal death. We excluded small studies (n < 500 individuals) and studies using only antenatal factors to predict mortality. Two independent investigators screened all articles for inclusion. The data collection consisted of study design, number of models, features used per model, feature importance, internal and/or external validation, and calibration analysis. Our primary outcome was the average area under the receiving characteristic curve (AUC) or sensitivity and specificity for all models included in each study. RESULTS Of 434 articles, 11 studies were included. The total number of participants was 1.26 M with gestational ages ranging from 22 weeks to term. Number of features ranged from 3 to 66 with timing of prediction as early as 5 min of life to a maximum of 7 days of age. The average number of models per study was 4, with neural network, random forest, and logistic regression comprising the most used models (58.3%). Five studies (45.5%) reported calibration plots and 2 (18.2%) conducted external validation. Eight studies reported results by AUC and 5 studies reported the sensitivity and specificity. The AUC varied from 58.3% to 97.0%. The mean sensitivities ranged from 63% to 80% and specificities from 78% to 99%. The best overall model was linear discriminant analysis, but it also had a high number of features (n = 17). DISCUSSION/CONCLUSION ML models can accurately predict death in neonates. This analysis demonstrates the most commonly used predictors and metrics for AI prediction models for neonatal mortality. Future studies should focus on external validation, calibration, as well as deployment of applications that can be readily accessible to health-care providers.
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Affiliation(s)
- Cheyenne Mangold
- Department of Pediatrics, University of Texas Health San Antonio, San Antonio, Texas, USA
| | - Sarah Zoretic
- Department of Pediatrics, University of Texas Health San Antonio, San Antonio, Texas, USA,
| | - Keerthi Thallapureddy
- Department of Pediatrics, University of Texas Health San Antonio, San Antonio, Texas, USA
| | - Axel Moreira
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA
| | - Kevin Chorath
- Department of Otolaryngology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Alvaro Moreira
- Department of Pediatrics, University of Texas Health San Antonio, San Antonio, Texas, USA
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Iriondo M, Thio M, del Río R, Baucells BJ, Bosio M, Figueras-Aloy J. Prediction of mortality in very low birth weight neonates in Spain. PLoS One 2020; 15:e0235794. [PMID: 32645708 PMCID: PMC7347394 DOI: 10.1371/journal.pone.0235794] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 06/22/2020] [Indexed: 11/23/2022] Open
Abstract
Objective Predictive models for preterm infant mortality have been developed internationally, albeit not valid for all populations. This study aimed to develop and validate different mortality predictive models, using Spanish data, to be applicable to centers with similar morbidity and mortality. Methods Infants born alive, admitted to NICU (BW<1500 g or GA<30 w), and registered in the SEN1500 database, were included. There were two time periods; development of the predictive models (2009–2012) and validation (2013–2015). Three models were produced; prenatal (1), first 24 hours of life (2), and whilst admitted (3). For the statistical analysis, hospital mortality was the dependent variable. Significant variables were used in multivariable regression models. Specificity, sensitivity, accuracy, and area under the curve (AUC), for all models, were calculated. Results Out of 14953 included newborns, 2015 died; 373 (18.5%) in their first 24 hours, 1315 (65.3%) during the first month, and 327 (16.2%) thereafter, before discharge. In the development stage, mortality prediction AUC was 0.834 (95% CI: 0.822–0.846) (p<0.001) in model 1 and 0.872 (95% CI: 0.860–0.884) (p<0.001) in model 2. Model 3’s AUC was 0.989 (95% CI: 0.983–0.996) (p<0.001) and 0.942 (95% CI: 0.929–0.956) (p<0.001) during the 0–30 and >30 days of life, respectively. During validation, models 1 and 2 showed moderate concordance, whilst that of model 3 was good. Conclusion Using dynamic models to predict individual mortality can improve outcome estimations. Development of models in the prenatal period, first 24 hours, and during hospital admission, cover key stages of mortality prediction in preterm infants.
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Affiliation(s)
- Martín Iriondo
- Neonatology Department, Hospital Sant Joan de Déu, BCNatal, Hospital Sant Joan de Déu-Hospital, Barcelona University, Barcelona, Spain
- * E-mail:
| | - Marta Thio
- Newborn Research Centre, The Royal Women's Hospital, Melbourne & University of Melbourne, Melbourne, Australia
- Murdoch Childrens Research Institute, Melbourne, Australia
- University of Melbourne, Melbourne, Australia
| | - Ruth del Río
- Neonatology Department, Hospital Sant Joan de Déu, BCNatal, Hospital Sant Joan de Déu-Hospital, Barcelona University, Barcelona, Spain
| | - Benjamin J. Baucells
- Neonatology Department, Hospital Sant Joan de Déu, BCNatal, Hospital Sant Joan de Déu-Hospital, Barcelona University, Barcelona, Spain
| | - Mattia Bosio
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Josep Figueras-Aloy
- Neonatology Department, Hospital Clínic, BCNatal, Hospital Clínic- Hospital Sant Joan de Déu, Barcelona University, Barcelona, Spain
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Prediction of mortality in premature neonates. An updated systematic review. ANALES DE PEDIATRÍA (ENGLISH EDITION) 2020. [DOI: 10.1016/j.anpede.2019.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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Turova V, Sidorenko I, Eckardt L, Rieger-Fackeldey E, Felderhoff-Müser U, Alves-Pinto A, Lampe R. Machine learning models for identifying preterm infants at risk of cerebral hemorrhage. PLoS One 2020; 15:e0227419. [PMID: 31940391 PMCID: PMC6961932 DOI: 10.1371/journal.pone.0227419] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 12/18/2019] [Indexed: 11/18/2022] Open
Abstract
Intracerebral hemorrhage in preterm infants is a major cause of brain damage and cerebral palsy. The pathogenesis of cerebral hemorrhage is multifactorial. Among the risk factors are impaired cerebral autoregulation, infections, and coagulation disorders. Machine learning methods allow the identification of combinations of clinical factors to best differentiate preterm infants with intra-cerebral bleeding and the development of models for patients at risk of cerebral hemorrhage. In the current study, a Random Forest approach is applied to develop such models for extremely and very preterm infants (23-30 weeks gestation) based on data collected from a cohort of 229 individuals. The constructed models exhibit good prediction accuracy and might be used in clinical practice to reduce the risk of cerebral bleeding in prematurity.
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Affiliation(s)
- Varvara Turova
- Research Unit for Pediatric Neuroorthopedics and Cerebral Palsy of the Buhl-Strohmaier Foundation, Orthopedic Department, Klinikum Rechts der Isar, Technical University of Munich, München, Germany
- * E-mail:
| | - Irina Sidorenko
- Chair of Mathematical Modelling, Mathematical Faculty, Technical University of Munich, Garching bei München, Germany
| | - Laura Eckardt
- Departments of Pediatrics and Neonatology, University Hospital Essen, University of Duisburg‐Essen, Essen, Germany
| | - Esther Rieger-Fackeldey
- Department of Pediatrics, Neonatology, Klinikum Rechts der Isar, Technical University of Munich, München, Germany
| | - Ursula Felderhoff-Müser
- Departments of Pediatrics and Neonatology, University Hospital Essen, University of Duisburg‐Essen, Essen, Germany
| | - Ana Alves-Pinto
- Research Unit for Pediatric Neuroorthopedics and Cerebral Palsy of the Buhl-Strohmaier Foundation, Orthopedic Department, Klinikum Rechts der Isar, Technical University of Munich, München, Germany
| | - Renée Lampe
- Research Unit for Pediatric Neuroorthopedics and Cerebral Palsy of the Buhl-Strohmaier Foundation, Orthopedic Department, Klinikum Rechts der Isar, Technical University of Munich, München, Germany
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Del Río R, Thió M, Bosio M, Figueras J, Iriondo M. [Prediction of mortality in premature neonates. An updated systematic review]. An Pediatr (Barc) 2020; 93:24-33. [PMID: 31926888 DOI: 10.1016/j.anpedi.2019.11.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 11/13/2019] [Indexed: 12/29/2022] Open
Abstract
INTRODUCTION Extreme prematurity is associated with high mortality rates. The probability of death at different points in time is a priority for professionals and parents, and needs to be established on an individual basis. The aim of this study is to carry out a systematic review of predictive models of mortality in premature infants that have been published recently. METHODS A double search was performed for article published in PubMed on models predicting mortality in premature neonates. The population studied were premature neonates with a gestational age of ≤30 weeks and / or a weight at birth of ≤1500g. Works published with new models from June 2010 to July 2019 after a systematic review by Medlock (2011) were included. An assessment was made of the population, characteristics of the model, variables used, measurements of functioning, and validation. RESULTS Of the 7744 references (1st search) and 1435 (2nd search) found, 31 works were selected, with 8 new models finally being included. Five models (62.5%) were developed in North America and 2 (25%) in Europe. A sequential model (Ambalavanan) enables predictions of mortality to be made at birth, 7, 28 days of life, and 36 weeks post-menstrual. A multiple logistic regression analysis was performed on 87.5% of the models. The population discrimination was measured using Odds Ratio (75%) and the area under the curve (50%). "Internal Validation" had been carried out on 5 models. Three models can be accessed on-line. There are no predictive models validated in Spain. CONCLUSIONS The making of decisions based on predictive models can lead to the care given to the premature infant being more individualised and with a better use of resources. Predictive models of mortality in premature neonates in Spain need to be developed.
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Affiliation(s)
- Ruth Del Río
- Departamento de Neonatología, Hospital Sant Joan de Déu, BCNatal-Hospital Clínic-Hospital Sant Joan de Déu, Universitat de Barcelona, Barcelona, España.
| | - Marta Thió
- Newborn Research Centre, The Royal Women's Hospital, Melbourne, University of Melbourne, Melbourne, Australia
| | - Mattia Bosio
- Barcelona Supercomputing Center (BSC), Barcelona, España
| | - Josep Figueras
- Departamento de Neonatología, Hospital Clínic, BCNatal-Hospital Clínic-Hospital Sant Joan de Déu, Universitat de Barcelona, España
| | - Martín Iriondo
- Departamento de Neonatología, Hospital Sant Joan de Déu, BCNatal-Hospital Clínic-Hospital Sant Joan de Déu, Universitat de Barcelona, Barcelona, España
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Beluzo CE, Silva E, Alves LC, Bresan RC, Arruda NM, Sovat R, Carvalho T. Towards neonatal mortality risk classification: A data-driven approach using neonatal, maternal, and social factors. INFORMATICS IN MEDICINE UNLOCKED 2020; 20:100398. [PMID: 33102685 PMCID: PMC7568208 DOI: 10.1016/j.imu.2020.100398] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 07/13/2020] [Accepted: 07/14/2020] [Indexed: 11/16/2022] Open
Abstract
Infant mortality is an important health measure in a population as a crude indicator of the poverty and socioeconomic level. It also shows the availability and quality of health services and medical technology in a specific region. Although improvements have been observed in the last decades, the implementation of actions to reduce infant mortality is still a concern in many countries. To address such an important problem, this paper proposes a new support decision approach to classify newborns according to their neonatal mortality risk. Using features related to mother, newborn, and socio-demographic, we model the problem using a data-driven classification model able to provide the probability of a newborn dying until 28 t h days of life. More than a theoretical study, decision support tools as the one proposed here is relevant in countries in development as Brazil, because it aims at identifying risky neonates that may die to raise the attention of medical practitioners so that they can work harder to reduce the overall neonatal mortality. Overcoming an AUC of 96%, the proposed method is able to provide not just the probability of death risk but also an explicable interpretation of most important features for model decision, which is paramount in public health applications. Furthermore, we provide an extensive analysis across different rounds of experiments, including an analysis of pre and post partum features influence over data-driven model. Finally, different from previously conducted studies which rely on databases with less than 100,000 samples, our model takes advantage from a new proposed database, constructed using more than 1,400,000 samples comprising births and deaths extracted from public records in São Paulo-Brazil from 2012 to 2018.
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Affiliation(s)
| | - Everton Silva
- Federal Institute of São Paulo, Campinas, SP, Brazil
| | | | | | | | - Ricardo Sovat
- Federal Institute of São Paulo, Campinas, SP, Brazil
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Helguera-Repetto AC, Soto-Ramírez MD, Villavicencio-Carrisoza O, Yong-Mendoza S, Yong-Mendoza A, León-Juárez M, González-Y-Merchand JA, Zaga-Clavellina V, Irles C. Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks. Front Pediatr 2020; 8:525. [PMID: 33042902 PMCID: PMC7518045 DOI: 10.3389/fped.2020.00525] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 07/24/2020] [Indexed: 12/21/2022] Open
Abstract
Neonatal sepsis remains difficult to diagnose due to its non-specific signs and symptoms. Traditional scoring systems help to discriminate between septic or not patients, but they do not consider every single patient particularity. Thus, the purpose of this study was to develop an early- and late-onset neonatal sepsis diagnosis model, based on clinical maternal and neonatal data from electronic records, at the time of clinical suspicion. A predictive model was obtained by training and validating an artificial Neural Networks (ANN) algorithm with a balanced dataset consisting of preterm and term non-septic or septic neonates (early- and late-onset), with negative and positive culture results, respectively, using 25 maternal and neonatal features. The outcome of the model was sepsis or not. The performance measures of the model, evaluated with an independent dataset, outperformed physician's diagnosis using the same features based on traditional scoring systems, with a 93.3% sensitivity, an 80.0% specificity, a 94.4% AUROC, and a regression coefficient of 0.974 between actual and simulated results. The model also performed well-relative to the state-of-the-art methods using similar maternal/neonatal variables. The top 10 factors estimating sepsis were maternal age, cervicovaginitis and neonatal: fever, apneas, platelet counts, gender, bradypnea, band cells, catheter use, and birth weight.
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Affiliation(s)
| | - María Dolores Soto-Ramírez
- Department of Immunobiochemistry, Instituto Nacional de Perinatología, Mexico City, Mexico.,Department of Microbiology, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Mexico City, Mexico
| | - Oscar Villavicencio-Carrisoza
- Department of Immunobiochemistry, Instituto Nacional de Perinatología, Mexico City, Mexico.,Department of Microbiology, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Mexico City, Mexico
| | - Samantha Yong-Mendoza
- Department of Immunobiochemistry, Instituto Nacional de Perinatología, Mexico City, Mexico.,Department of Microbiology, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Mexico City, Mexico
| | - Angélica Yong-Mendoza
- Department of Immunobiochemistry, Instituto Nacional de Perinatología, Mexico City, Mexico
| | - Moisés León-Juárez
- Department of Immunobiochemistry, Instituto Nacional de Perinatología, Mexico City, Mexico
| | - Jorge A González-Y-Merchand
- Department of Microbiology, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Mexico City, Mexico
| | - Verónica Zaga-Clavellina
- Department of Physiology and Cellular Development, Instituto Nacional de Perinatología, Mexico City, Mexico
| | - Claudine Irles
- Department of Physiology and Cellular Development, Instituto Nacional de Perinatología, Mexico City, Mexico
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Bourke J, Wong K, Srinivasjois R, Pereira G, Shepherd CCJ, White SW, Stanley F, Leonard H. Predicting Long-Term Survival Without Major Disability for Infants Born Preterm. J Pediatr 2019; 215:90-97.e1. [PMID: 31493909 DOI: 10.1016/j.jpeds.2019.07.056] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 06/07/2019] [Accepted: 07/23/2019] [Indexed: 12/20/2022]
Abstract
OBJECTIVE To describe the long-term neurodevelopmental and cognitive outcomes for children born preterm. STUDY DESIGN In this retrospective cohort study, information on children born in Western Australia between 1983 and 2010 was obtained through linkage to population databases on births, deaths, and disabilities. For the purpose of this study, disability was defined as a diagnosis of intellectual disability, autism, or cerebral palsy. The Kaplan-Meier method was used to estimate the probability of disability-free survival up to age 25 years by gestational age. The effect of covariates and predicted survival was examined using parametric survival models. RESULTS Of the 720 901 recorded live births, 12 083 children were diagnosed with disability, and 5662 died without any disability diagnosis. The estimated probability of disability-free survival to 25 years was 4.1% for those born at gestational age 22 weeks, 19.7% for those born at 23 weeks, 42.4% for those born at 24 weeks, 53.0% for those born at 25 weeks, 78.3% for those born at 28 weeks, and 97.2% for those born full term (39-41 weeks). There was substantial disparity in the predicted probability of disability-free survival for children born at all gestational ages by birth profile, with 5-year estimates of 4.9% and 10.4% among Aboriginal and Caucasian populations, respectively, born at 24-27 weeks and considered at high risk (based on low Apgar score, male sex, low sociodemographic status, and remote region of residence) and 91.2% and 93.3%, respectively, for those at low risk (ie, high Apgar score, female sex, high sociodemographic status, residence in a major city). CONCLUSIONS Apgar score, birth weight, sex, socioeconomic status, and maternal ethnicity, in addition to gestational age, have pronounced impacts on disability-free survival.
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Affiliation(s)
- Jenny Bourke
- Telethon Kids Institute, The University of Western Australia, Perth, Australia
| | - Kingsley Wong
- Telethon Kids Institute, The University of Western Australia, Perth, Australia
| | - Ravisha Srinivasjois
- Department of Neonatology and Paediatrics, Joondalup Health Campus, Perth, Australia; School of Paediatrics and Child Health, University of Western Australia, Perth, Australia; School of Public Health, Curtin University, Perth, Australia; School of Medical and Health Sciences, Edith Cowan University, Perth, Australia
| | - Gavin Pereira
- Telethon Kids Institute, The University of Western Australia, Perth, Australia; School of Public Health, Curtin University, Perth, Australia
| | - Carrington C J Shepherd
- Telethon Kids Institute, The University of Western Australia, Perth, Australia; Ngangk Yira Research Centre for Aboriginal Health and Social Equity, Murdoch University, Murdoch, Australia
| | - Scott W White
- Maternal Fetal Medicine Service, King Edward Memorial Hospital, Subiaco, Australia; Division of Obstetrics and Gynaecology, The University of Western Australia, Perth, Australia
| | - Fiona Stanley
- Telethon Kids Institute, The University of Western Australia, Perth, Australia
| | - Helen Leonard
- Telethon Kids Institute, The University of Western Australia, Perth, Australia.
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