Knowledge discovery on in vitro fertilization clinical data using particle swarm optimization

Chih Chuan Chen, Yi Chung Cheng, Chao Chin Hsu, Sheng-Tun Li

研究成果: Conference contribution

7 引文 (Scopus)

摘要

In vitro fertilization (IVF) is a medically assisted reproduction technique (ART) for treating infertility. During IVF procedures, a female patient requires hormone treatment to control ovulation, oocytes are taken from the patient and fertilized in vitro, and after fertilization, one or usually more resulting embryos are transferred into the uterus. Although IVF is considered as a method of last resort for infertile couples, the success rate is still low, which can be only as high as 40% for women under age of 30. In this study, we build a predictive model which takes into account a patient's physiology and the results of the stages of an IVF cycle, to assist obstetricians and gynecologists in increasing success rate of IVF. The predictive model is based on a knowledge discovering technique incorporated with particle swarm optimization (PSO), which is a competitive heuristic technique for solving optimization task. This study uses the database of IVF cycles developed by a women and infants clinic in Taiwan as the foundation. A repertory grid is developed to help selecting attributes for the data mining technique. The results show that the proposed technique can exploit rules approved by the obstetrician/gynecologist and the assistant on both comprehensibility and justifiability.

原文English
主出版物標題Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009
頁面278-283
頁數6
DOIs
出版狀態Published - 2009 十一月 18
事件2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009 - Taichung, Taiwan
持續時間: 2009 六月 222009 六月 24

出版系列

名字Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009

Other

Other2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009
國家Taiwan
城市Taichung
期間09-06-2209-06-24

指紋

Fertilization in Vitro
Particle swarm optimization (PSO)
Data mining
Hormones
Physiology
Reproductive Techniques
Data Mining
Ovulation
Taiwan
Infertility
Uterus
Oocytes
Embryonic Structures
Databases

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Biomedical Engineering
  • Health Informatics

引用此文

Chen, C. C., Cheng, Y. C., Hsu, C. C., & Li, S-T. (2009). Knowledge discovery on in vitro fertilization clinical data using particle swarm optimization. 於 Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009 (頁 278-283). [5211262] (Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009). https://doi.org/10.1109/BIBE.2009.36
Chen, Chih Chuan ; Cheng, Yi Chung ; Hsu, Chao Chin ; Li, Sheng-Tun. / Knowledge discovery on in vitro fertilization clinical data using particle swarm optimization. Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009. 2009. 頁 278-283 (Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009).
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abstract = "In vitro fertilization (IVF) is a medically assisted reproduction technique (ART) for treating infertility. During IVF procedures, a female patient requires hormone treatment to control ovulation, oocytes are taken from the patient and fertilized in vitro, and after fertilization, one or usually more resulting embryos are transferred into the uterus. Although IVF is considered as a method of last resort for infertile couples, the success rate is still low, which can be only as high as 40{\%} for women under age of 30. In this study, we build a predictive model which takes into account a patient's physiology and the results of the stages of an IVF cycle, to assist obstetricians and gynecologists in increasing success rate of IVF. The predictive model is based on a knowledge discovering technique incorporated with particle swarm optimization (PSO), which is a competitive heuristic technique for solving optimization task. This study uses the database of IVF cycles developed by a women and infants clinic in Taiwan as the foundation. A repertory grid is developed to help selecting attributes for the data mining technique. The results show that the proposed technique can exploit rules approved by the obstetrician/gynecologist and the assistant on both comprehensibility and justifiability.",
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Chen, CC, Cheng, YC, Hsu, CC & Li, S-T 2009, Knowledge discovery on in vitro fertilization clinical data using particle swarm optimization. 於 Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009., 5211262, Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009, 頁 278-283, 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009, Taichung, Taiwan, 09-06-22. https://doi.org/10.1109/BIBE.2009.36

Knowledge discovery on in vitro fertilization clinical data using particle swarm optimization. / Chen, Chih Chuan; Cheng, Yi Chung; Hsu, Chao Chin; Li, Sheng-Tun.

Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009. 2009. p. 278-283 5211262 (Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009).

研究成果: Conference contribution

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Chen CC, Cheng YC, Hsu CC, Li S-T. Knowledge discovery on in vitro fertilization clinical data using particle swarm optimization. 於 Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009. 2009. p. 278-283. 5211262. (Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009). https://doi.org/10.1109/BIBE.2009.36