| 研究生: |
曾瀞瑩 Ching-Ying Tseng |
|---|---|
| 論文名稱: |
以機器學習技術為基礎建構新生兒孕育健康狀態預測模型 Constructing a Prediction Model of Newborns’ Health Status Using Machine Learning Techniques |
| 指導教授: |
曾筱珽
Hsiao-Ting Tseng |
| 口試委員: | |
| 學位類別: |
碩士 Master |
| 系所名稱: |
管理學院 - 資訊管理學系 Department of Information Management |
| 論文出版年: | 2021 |
| 畢業學年度: | 109 |
| 語文別: | 中文 |
| 論文頁數: | 86 |
| 中文關鍵詞: | 機器學習 、新生兒 、精準健康促進 |
| 外文關鍵詞: | Machine Learning, Newborns, Precision Health Promotion |
| 相關次數: | 點閱:12 下載:0 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
隨著物價上漲、薪資縮減、女性意識抬頭,現代人晚婚、不婚已成普遍的現象,養育一名小孩所費不貲,綜合以上原因,婦女生育率逐年下滑,這也導致眾多國家面臨了少子化的社會問題,正因為生育率低下,如何降低新生兒死亡率便是一項重要任務,因此,本研究希望透過個案醫院產科所提供的資料集,結合機器學習的技術來找出對於新生兒健康發展存在重要影響的特徵。
本研究共設計了兩階段的實驗,第一階段是去探討新生兒出生前的特徵對於新生兒出生當下健康狀態的影響,第二階段則是去探討新生兒出生前以及住院期間的特徵對於新生兒能否健康出院的影響,所有實驗皆使用五種分類器來建構預測模型,最後希望實驗結果能供醫護人員在新生兒護理決策上做參考,降低新生兒因健康狀態不佳而導致不良結局的情況,達到精準健康促進的目標。
It has become a common phenomenon for modern people to marry late and not to marry resulting from price hike, wages stagnation, and the awakening female consciousness.
Moreover, it costs to raise a child. Due to many reasons, the fertility rate of women has been declining year by year and this has also caused many social problems. With the low fertility rate, it is more important to control the newborns’ mortality rate. Therefore, we hope to use
machine learning techniques with the maternal and newborn datasets provided by the hospitals to find out the significant variables for newborns’ health development.
We designed a two-stage experiment. The first stage is to explore the impact of the newborns’ characteristics before birth on the health of the newborns. The second stage is to explore the characteristics of the newborn before birth and during the hospital stay for the impact of whether the newborn can be discharged healthily from the hospital. All experiments use five classifiers to construct the predictive models. In the end, we hope that the results of our study can be used as a reference for medical staff to make decisions on newborn’s care and reduce the adverse outcomes of the newborn, achieving the goal of precision health promotion.
內政部戶政司 (2020)。人口統計數據。 取自https://www.ris.gov.tw/app/portal/346。
巫俊郡 (2020)。三種脂肪功能大不同,棕色脂肪讓人吃不胖!取自 https://heho.com.tw/archives/66610。
李淑芬、林宜靜、蔡偉德 (2012)。醫療救治品質存在週末效應? 人文及社會科學集刊,24(2),233-275。
馬偕醫院婦產科醫師 (2014)。《婦產科常見病症和保健百科》,初版。臺北市 : 西北國際。
黃仲毅 (2017)。護理人員願意投入和留任醫院執業之工作條件與彈性制度探討。博士論文,國立臺灣師範大學。
衛生福利部統計處 (2020)。 嬰兒死亡率。 取自https://dep.mohw.gov.tw/dos/cp-1735-3240-113.html。
Akech, S., Rotich, B., Chepkirui, M., Ayieko, P., Irimu, G., English, M., & authors, C. I. N. (2018). The Prevalence and Management of Dehydration amongst Neonatal Admissions to General Paediatric Wards in Kenya—A Clinical Audit. Journal of Tropical Pediatrics, 64(6), 516-522.
Akhtar, F., Li, J., Azeem, M., Chen, S., Pan, H., Wang, Q., & Yang, J.-J. (2020). Effective large for gestational age prediction using machine learning techniques with monitoring biochemical indicators. The Journal of Supercomputing, 76(8), 6219-6237.
Amiri, A., Vehviläinen-Julkunen, K., Solankallio-Vahteri, T., & Tuomi, S. (2020). Impact of nurse staffing on reducing infant, neonatal and perinatal mortality rates: Evidence from panel data analysis in 35 OECD countries. International Journal of Nursing Sciences, 7(2), 161-169.
Berrouiguet, S., Billot, R., Larsen, M. E., Lopez-Castroman, J., Jaussent, I., Walter, M., Lenca, P., Baca-García, E., Courtet, P. (2019). An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support. JMIR mental health, 6(5), e9766-e9766.
Burstein, R., Henry, N. J., Collison, M. L., Marczak, L. B., Sligar, A., Watson, S., . . . Meles, G. G. (2019). Mapping 123 million neonatal, infant and child deaths between 2000 and 2017. Nature, 574(7778), 353-358.
Chen, C. W., Tsai, C. Y., Sung, F. C., Lee, Y. Y., Lu, T. H., Li, C. Y., & Ko, M. C. (2010). Adverse birth outcomes among pregnancies of teen mothers: age-specific analysis of national data in Taiwan. Child Care Health Dev, 36(2), 232-240.
Chen, H. Y., & Chauhan, S. P. (2019). Risk of Neonatal and Infant Mortality in Twins and Singletons by Gestational Age. Am J Perinatol, 36(8), 798-805.
Chou, D., Daelmans, B., Jolivet, R. R., Kinney, M., & Say, L. (2015). Ending preventable maternal and newborn mortality and stillbirths. BMJ : British Medical Journal, 351, h4255.
Crump, C., Sundquist, J., & Sundquist, K. (2020). Preterm delivery and long term mortality in women: national cohort and co-sibling study. BMJ, 370, m2533.
Das, J. C. (2015). Hypernatremic Dehydration in Newborn Infants. The Ulutas Medical Journal, 1(2), 22-25.
Don Sharkey, C. H., Carole Ward, Michel Valstar and Mercedes Torres-Torres. (2021). Improving neonatal outcomes through machine learning. Retrieved from http://www.healthcaretechnologies.ac.uk/case-studies/paediatrics-and-neonatal-care/improving-neonatal-outcomes-through-machine-learning.aspx
Elie Mbombo, R. M., Phelgona Otieno and Isaac Ogwayo. (2018). Determinants of Modes of Delivery: A Hospital based Retrospective Study in Kenya. J Women's Health Care,, 7(1), 1-6.
Ferlie, E., Crilly, T., Jashapara, A., Trenholm, S., Peckham, A., & Currie, G. (2015). Knowledge mobilization in healthcare organizations: a view from the resource-based view of the firm. International journal of health policy and management, 4(3), 127-130.
Fu, Y., Gou, W., Hu, W., Mao, Y., Tian, Y., Liang, X., Guan, Y., Huang, T., Li, K., Guo, X., Liu, H., Li, D., Zheng, J.-S. (2020). Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort. BMC Medicine, 18(1), 184.
Goudjil, M., Koudil, M., Bedda, M., & Ghoggali, N. (2018). A Novel Active Learning Method Using SVM for Text Classification. International Journal of Automation and Computing, 15(3), 290-298.
Guarga Montori, M., Álvarez Martínez, A., Luna Álvarez, C., Abadía Cuchí, N., Mateo Alcalá, P., & Ruiz-Martínez, S. (2021). Advanced maternal age and adverse pregnancy outcomes: A cohort study. Taiwanese Journal of Obstetrics and Gynecology, 60(1), 119-124.
Guy-Evans, O. (2020). Bronfenbrenner's Ecological Systems Theory. Retrieved from https://www.simplypsychology.org/Bronfenbrenner.html
Haksari, E. L., Lafeber, H. N., Hakimi, M., Pawirohartono, E. P., & Nyström, L. (2016). Reference curves of birth weight, length, and head circumference for gestational ages in Yogyakarta, Indonesia. BMC Pediatrics, 16(1), 188.
Hidalgo-Lopezosa, P., Hidalgo-Maestre, M., & Rodríguez-Borrego, M. A. (2016). Labor stimulation with oxytocin: effects on obstetrical and neonatal outcomes. Revista latino-americana de enfermagem, 24, e2744-e2744.
Islam, T., Hussain, N., Islam, S., & Chakrabarty, A. (2018). Detecting Adverse Drug Reaction with Data Mining And Predicting its Severity With Machine Learning. Paper presented at the 2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC).
Jamshed, S., Khan, F., Chohan, S. K., Bano, Z., Shahnawaz, S., Anwar, A., & Hashmi, A. A. (2020). Frequency of Normal Birth Length and Its Determinants: A Cross-Sectional Study in Newborns. Cureus, 12(9), e10556-e10556.
Janice Edwards, D. K., Jeremy Short. (2014). Mastering Strategic Management (1st Canadian Edition ed.): BCcampus.
Juretschke, L. J. (2000). Apgar scoring: its use and meaning for today's newborn. Neonatal Netw, 19(1), 17-19.
Karmaus, W., Soto-Ramírez, N., & Zhang, H. (2017). Infant feeding pattern in the first six months of age in USA: a follow-up study. International Breastfeeding Journal, 12(1), 48.
Kemfang Ngowa, J. D., Domkam, I., Ngassam, A., Nguefack-Tsague, G., Dobgima Pisoh, W., Noa, C., & Kasia, J. M. (2014). References of Birth Weights for Gestational Age and Sex from a Large Cohort of Singleton Births in Cameroon. Obstetrics and Gynecology International, 2014, 361451.
Kent, J. (2020). Machine Learning Predicts Life-Threatening Disease in Infants. Retrieved from https://healthitanalytics.com/news/machine-learning-predicts-life-threatening-disease-in-infants
Khosla, A., Cao, Y., Lin, C. C.-Y., Chiu, H.-K., Hu, J., & Lee, H. (2010). An integrated machine learning approach to stroke prediction. Paper presented at the Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, Washington, DC, USA.
Kuhle, S., Maguire, B., Zhang, H., Hamilton, D., Allen, A. C., Joseph, K. S., & Allen, V. M. (2018). Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: a retrospective cohort study. BMC Pregnancy and Childbirth, 18(1), 333.
Londero, A. P., Rossetti, E., Pittini, C., Cagnacci, A., & Driul, L. (2019). Maternal age and the risk of adverse pregnancy outcomes: a retrospective cohort study. BMC Pregnancy and Childbirth, 19(1), 261.
Lubkowska, A., Szymański, S., & Chudecka, M. (2019). Surface Body Temperature of Full-Term Healthy Newborns Immediately after Birth-Pilot Study. International journal of environmental research and public health, 16(8), 1312.
Mehari, M.-a., Maeruf, H., Robles, C. C., Woldemariam, S., Adhena, T., Mulugeta, M., Haftu, A., Hagose, H., Kumsa, H. (2020). Advanced maternal age pregnancy and its adverse obstetrical and perinatal outcomes in Ayder comprehensive specialized hospital, Northern Ethiopia, 2017: a comparative cross-sectional study. BMC Pregnancy and Childbirth, 20(1), 60.
Merazzi, D., Bresesti, I., Tagliabue, P., Valsecchi, M. G., De Lorenzo, P., Lista, G., . . . Collaboration, G. (2020). Body temperature at nursery admission in a cohort of healthy newborn infants: results from an observational cross-sectional study. Italian Journal of Pediatrics, 46(1), 46.
Morais, A., Peixoto, H., Coimbra, C., Abelha, A., & Machado, J. (2017). Predicting the need of Neonatal Resuscitation using Data Mining. Procedia Computer Science, 113, 571-576.
Networks, New Zealand Child and Youth Clinical Networks (2019). Practice recommendations for weight loss, dehydration and hypernatraemic dehydration in the neonate. Retrieved from https://www.starship.org.nz/guidelines/practice-recommendations-for-weight-loss-dehydration-and-hypernatraemic/
Nguyên, X.-L., Chaskalovic, J., Rakotonanahary, D., & Fleury, B. (2010). Insomnia symptoms and CPAP compliance in OSAS patients: A descriptive study using Data Mining methods. Sleep Medicine, 11(8), 777-784.
Olson, D. L., & Lauhoff, G. (2019). Descriptive Data Mining. In Descriptive Data Mining (pp. 129-130). Singapore: Springer Singapore.
Palmer, W. L., Bottle, A., & Aylin, P. (2015). Association between day of delivery and obstetric outcomes: observational study. BMJ, 351, h5774.
Pam, V. C., Yilgwan, C. S., Shwe, D. D., Abok, I., Shehu, N., Gomerep, S. S., Ejiji, I. S., Ocheke, A., Ajang, F. M., Mutihir, J. T., Gurumdimma, N., Egah, D., Oguche, S. (2019). Head Circumference of Babies at Birth in Nigeria. Journal of Tropical Pediatrics, 65(6), 626-633.
Pereira, S., Portela, F., Santos, M. F., Machado, J., & Abelha, A. (2015). Predicting Type of Delivery by Identification of Obstetric Risk Factors through Data Mining. Procedia Computer Science, 64, 601-609.
Pimenta, J. R. R., Grandi, C., Aragon, D. C., & Cardoso, V. C. (2020). Comparison of birth weight, length, and head circumference between the BRISA-RP and Intergrowth-21st cohorts. Jornal de Pediatria, 96(4), 511-519.
PRACTICE, AMERICAN ACADEMY OF PEDIATRICS COMMITTEE ON FETUS AND NEWBORN, & AMERICAN COLLEGE OF OBSTETRICIANS AND GYNECOLOGISTS COMMITTEE ON OBSTETRIC PRACTICE (2015). The Apgar Score. Pediatrics, 136(4), 819.
Prospects, Department of Economic and Social Affairs Population Dynamics (2019). World Population Prospects. Retrieved from https://population.un.org/wpp/
Rawashdeh, H., Awawdeh, S., Shannag, F., Henawi, E., Faris, H., Obeid, N., & Hyett, J. (2020). Intelligent system based on data mining techniques for prediction of preterm birth for women with cervical cerclage. Comput Biol Chem, 85, 107233.
Razavi, R., Gharipour, A., & Gharipour, M. (2020). Depression screening using mobile phone usage metadata: a machine learning approach. Journal of the American Medical Informatics Association, 27(4), 522-530.
Reuter, S., Moser, C., & Baack, M. (2014). Respiratory distress in the newborn. Pediatrics in review, 35(10), 417-429.
Santos, R. S., Malheiros, S. M. F., Cavalheiro, S., & de Oliveira, J. M. P. (2013). A data mining system for providing analytical information on brain tumors to public health decision makers. Computer Methods and Programs in Biomedicine, 109(3), 269-282.
Smyser, C. D., Dosenbach, N. U. F., Smyser, T. A., Snyder, A. Z., Rogers, C. E., Inder, T. E., Schlaggar, B. L., Neil, J. J. (2016). Prediction of brain maturity in infants using machine-learning algorithms. Neuroimage, 136, 1-9.
Snowden, J. M., & Caughey, A. B. (2015). Is there a weekend effect in obstetrics? BMJ, 351, h6192.
Stock, S. J., & Norman, J. E. (2019). Medicines in pregnancy. F1000Research, 8, F1000 Faculty Rev-1911.
Sweet, L. R., Keech, C., Klein, N. P., Marshall, H. S., Tagbo, B. N., Quine, D., Kaur, P., Tikhonov, I., Nisar, M. I., Kochhar, S., Muñoz, F. M., Brighton Collaboration Respiratory Distress in the Neonate Working, G. (2017). Respiratory distress in the neonate: Case definition & guidelines for data collection, analysis, and presentation of maternal immunization safety data. Vaccine, 35(48 Pt A), 6506-6517.
Tovar, D., Cornejo, E., Xanthopoulos, P., Guarracino, M. R., & Pardalos, P. M. (2012). Data mining in psychiatric research. Methods Mol Biol, 829, 593-603.
Tsakiridis, I., Giouleka, S., Mamopoulos, A., Athanasiadis, A., Daniilidis, A., & Dagklis, T. (2020). Operative vaginal delivery: a review of four national guidelines. J Perinat Med, 48(3), 189-198.
United Nations International Children's Emergency Fund. (2020). Neonatal mortality. Retrieved from https://data.unicef.org/topic/child-survival/neonatal-mortality/
Vilar, S., Friedman, C., & Hripcsak, G. (2018). Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media. Briefings in bioinformatics, 19(5), 863-877.
Villar, J., Cheikh Ismail, L., Victora, C. G., Ohuma, E. O., Bertino, E., Altman, D. G., Lambert, A., Papageorghiou, A. T., Carvalho, M., Jaffer, Y. A., Gravett, M. G., Purwar, M., Frederick, I. O., Noble, A. J., Pang, R., Barros, F. C., Chumlea, C., Bhutta, Z. A., Kennedy, S. H. (2014). International standards for newborn weight, length, and head circumference by gestational age and sex: the Newborn Cross-Sectional Study of the INTERGROWTH-21st Project. Lancet, 384(9946), 857-868.