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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/46791完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 賴飛羆 | |
| dc.contributor.author | Zi-Jun Wang | en |
| dc.contributor.author | 王子軍 | zh_TW |
| dc.date.accessioned | 2021-06-15T05:41:32Z | - |
| dc.date.available | 2012-02-09 | |
| dc.date.copyright | 2011-02-09 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2011-01-20 | |
| dc.identifier.citation | [1] Safran, C. and H. Goldberg, Electronic patient records and the impact of the
Internet. International Journal of Medical Informatics, 2000. 60(2): pp. 77-83. [2] Adams, W.G., A.M. Mann, and H. Bauchner, Use of an electronic medical record improves the quality of urban pediatric primary care. Pediatrics, 2003. 111(3): pp. 626-32. [3] Nowack, W.J. and M.J. Niccolai. The electronic medical record in clinical care. in Biomedical Engineering Conference, 1996., Proceedings of the 1996 Fifteenth Southern. 1996. [4] Hameed, S.A., et al. Electronic medical record for effective patient monitoring database. in Computer and Communication Engineering, 2008. ICCCE 2008. International Conference on. 2008. [5] Xiangji, H. Machine learning approaches to Information Retrieval and its applications to the web, medical informatics and health care. in Granular Computing, 2008. GrC 2008. IEEE International Conference on. 2008. [6] Chu, S. and B. Cesnik, Knowledge representation and retrieval using conceptual graphs and free text document self-organisation techniques. International Journal of Medical Informatics, 2001. 62(2-3): pp. 121-133. [7] Frisse, M.E., Information retrieval: A health care perspective : WILLIAM R. HERSH. Computers in Medicine series (Helmuth F. Orthner, series ed.), Springer-Verlag, New York (1995). 320 pp., $50.00, ISBN: 0-3879-4494-0. Information Processing & Management, 1997. 33(1): pp. 123-124. [8] Lee, C.-H., C.-H. Wu, and H.-C. Yang, Text Mining of Clinical Records for Cancer Diagnosis, in Proceedings of the Second International Conference on Innovative Computing, Informatio and Control. 2007, IEEE Computer Society. p. 172. [9] Bell, D.S., R.A. Greenes, and P. Doubilet, Form-based clinical input from a structured vocabulary: initial application in ultrasound reporting. Proc Annu Symp Comput Appl Med Care, 1992: pp. 789-90. [10] Kahn C, Wang K, Bell D, Structured entry of radiology reports using world-wide web technology. Radiographics 1996;16:683-691. [11] aterson, G.I., et al. Using the XML-based Clinical Document Architecture for exchange of structured discharge summaries. in System Sciences, 2002. HICSS. Proceedings of the 35th Annual Hawaii International Conference on. 2002. [12] XML: http://www.w3schools.com/xml/default.asp [13] Roman, I., et al. Management of discharge summaries based on XML. in Information Technology Applications in Biomedicine, 2003. 4th International IEEE EMBS Special Topic Conference on. 2003. [14] CDA: Principles of Health Interoperability HL7 and SNOMED Chapter 9 [15] NLP: http://research.microsoft.com/en-us/groups/nlp/ [16] Xu, H., et al., MedEx: a medication information extraction system for clinical narratives. J Am Med Inform Assoc, 2010. 17(1): pp. 19-24. [17] Meystre, S. and P.J. Haug, Natural language processing to extract medical problems from electronic clinical documents: performance evaluation. J Biomed Inform, 2006. 39(6): pp. 589-99 [18] D. Carrell, D. Miglioretti, and R. Smith-Bindman, “Coding free text radiology reports using the Cancer Text Information Extraction System (caTIES),” AMIA Annu Symp Proc, p. 889, 2007. [19] Zhaohui, L., et al. Diagnosis of breast cancer tumor based on manifold learning and Support Vector Machine. in Information and Automation , 2008. ICIA 2008. International Conference on. 2008 [20] Mykowiecka, A., M. Marciniak, and A. Kupsc, Rule-based information extraction from patients' clinical data. Journal of Biomedical Informatics, 2009. 42(5): pp. 923-936. [21] Ontology : http://www-ksl.stanford.edu/kst/what-is-an-ontology.html [22] Mykowiecka, A. and M. Marciniak, Domain Model for Medical Information Extraction—The LightMedOnt Ontology. 2009. pp. 333-357. [23] Annibal, L.P. and J.C. Felipe. An ontology-based framework to support nonintrusive storage and analysis of radiological diagnosis data. In Computer-Based Medical Systems, 2009. CBMS 2009. 22nd IEEE International Symposium on. 2009. [24] Regular Expression: http://www.regular-expressions.info [25] XSL Transformations (XSLT) Version 1.0 W3C Recommendation 16 November 1999. Available from: http://www.w3.org/TR/xslt. Accessed Jan 2007. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/46791 | - |
| dc.description.abstract | 臨床上的醫療資料含有大量的病患資料，如手術記錄、出院病摘以及各種檢查的報告，而這些資料中，可能含有許多寶貴的資訊。如果能進一步分析這些資料，則可以有機會在這大量的醫療資料中得到有用的知識。
一般來說，臨床資料可以被分為兩種類型:經過結構化的資料以及沒有經過結構化的資料.對結構化的資料來說，這些資料是可以直接被電腦來分析，但是非結構化的資料在被分析之前則需要先被經過額外的處理以及抽取。所以我們要解決的最主要的問題就是如何將資料結構化。針對這個議題，我們採取兩種方法來達到資料結構化的目的。 1.針對未來的報告資料: 設計了結構化報告輸入介面讓使用者可以使用此介面去收集肝癌的臨床資料。 2.針對過往的歷史資料: 在這篇論文中我們開發了一個資料萃取輔助系統，其最主要的功能就是可以從結構鬆散(Free-text)的醫學報告中去抽取出有用的資訊。 藉由結合這兩種功能，醫師可以結構化肝癌方面的臨床資料以利於日後相關主題的研究。 | zh_TW |
| dc.description.abstract | Clinical data consist of abundant patients data such as operation notes, diagnosis, and various examination reports. These clinical data may contain a rich source of valuable information. If we can further analyze the clinical data, then the useful knowledge may be obtained from the huge amount of medical data.
In general, the clinical data can be divided into two types: the structuralized data and the non-structuralized data. For the structuralized data, the data can be analyzed by computer directly, however, the non-structuralized data need to be additionally processed and extracted before the further studying. Therefore the major problem we want to solve is how to structuralize the clinical data. We adopt two ways to fulfill the target for structuralizing data. 1. Reports in the future: A Structure Report Interface is designed for collecting liver cancer clinical data by this interface. 2. Clinical documents in the past: The Data Extraction Assistant System is designed for extracting useful information from the medical free-text report. Through the combination of these two functions, physicians can structuralize liver cancer clinical data and we hope the system can facilitate the study relevant to liver cancer | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T05:41:32Z (GMT). No. of bitstreams: 1 ntu-99-R97945042-1.pdf: 1599820 bytes, checksum: 8b35557a60ce4ffac35fc6f52b537231 (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | Chapter 1 Introduction 1
1.1 Electronic patient record (EPR)………………………………………………1 1.2 Structuralized data and Non-structuralized data………………………………2 1.3 Motivation and Objective…………………………………………………….3 1.4 Thesis Organization……………………………………………………………5 Chapter 2 Background and Related Work……………………………………………6 2.1 Clinical Report System…………………………………………………………6 2.1.1 CDA-Based Structure Discharge Summary System……………………7 2.2 Information extraction…………………………………………11 2.2.1 Rule-based information extraction system 11 Chapter 3 Methodology……………………………………………………………….15 3.1 System Architecture…………………………………………………………15 3.1.1 Structure report interface………………………16 3.1.2 Data Extraction on textual clinical document………………………18 3.2 Viewing the XML-Based Structure Report……………………………………21 3.3 Regular Expression on search rules……………………………………………23 3.4 Extraction Modules-Extraction Items generation……………………………27 Chapter 4 Results and Discussion……………………………………………………29 4.1 System Implementation………………………………………………………29 4.1.1 Structure Report……………………………………………………...29 4.1.2 Data Extraction Assistant System……………………………………33 4.2 Preliminary Result……………………………………………………………40 4.2.1 Structure Report Interface……………………………………………40 4.2.2 Data Extraction Assistant System……………………………………41 4.3 Discussion and Future Work…………………………………………………42 4.3.1 Structure Report Interface……………………………………………42 4.3.2 Data Extraction Assistant System……………………………………43 Chapter 5 Conclusion………………………………………………………………45 References………………………………………………………………46 | |
| dc.language.iso | en | |
| dc.subject | 資訊萃取 | zh_TW |
| dc.subject | 臨床醫療資料 | zh_TW |
| dc.subject | 結構化資料 | zh_TW |
| dc.subject | 鬆散文件報告 | zh_TW |
| dc.subject | 結構化報告 | zh_TW |
| dc.subject | structuralized data | en |
| dc.subject | data extraction | en |
| dc.subject | structure report | en |
| dc.subject | free-text report | en |
| dc.subject | clinical data | en |
| dc.title | 促進醫學資訊之資料分析方法-文字型式報告之資訊萃取以及於結構化報告介面收集結構化資料(以醫院肝癌資料為例) | zh_TW |
| dc.title | The methodology of facilitating data analysis in medical informatics -information extraction from free-text data and structural data collection through the structure report interface | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳澤雄,鐘玉芳,譚慶鼎,賴美淑 | |
| dc.subject.keyword | 臨床醫療資料,結構化資料,鬆散文件報告,結構化報告,資訊萃取, | zh_TW |
| dc.subject.keyword | clinical data,structuralized data,free-text report,structure report,data extraction, | en |
| dc.relation.page | 49 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2011-01-20 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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