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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74334Full metadata record
| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 鄭卜壬(Pu-Jen Cheng) | |
| dc.contributor.author | Hsin-Chieh Ma | en |
| dc.contributor.author | 馬欣婕 | zh_TW |
| dc.date.accessioned | 2021-06-17T08:30:22Z | - |
| dc.date.available | 2019-08-18 | |
| dc.date.copyright | 2019-08-18 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-12 | |
| dc.identifier.citation | [1] Y. Zhang, R. Chen, J. Tang, W. F. Stewart, and J. Sun, “LEAP: Learning to prescribe effective and safe treatment combinations for multimorbidity,” Proc. 23rd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. - KDD ’17, pp. 1315–1324, 2017.
[2] J. Shang, S. Hong, Y. Zhou, M. Wu, and H. Li, “Knowledge Guided Multi-instance Multi-label Learning via Neural Networks in Medicines Prediction,” Proc. Mach. Learn. Res., vol. 95, no. 2016, pp. 831–846, 2018. [3] J. M. Bajor and T. A. Lasko, “Predicting Medications from Diagnostic Codes with Recurrent Neural Networks,” Proceeding 5th Int. Conf. Learn. Represent. - (ICLR ’17), pp. 1–19, 2017. [4] W. Chiang and X. Ning, “Computational Drug Recommendation Approaches toward Safe Polypharmacy,” bioRxiv, no. August, p. 518415, 2019. [5] S. Syed-Abdul et al., “A smart medication recommendation model for the electronic prescription,” Comput. Methods Programs Biomed., vol. 117, no. 2, pp. 218–224, 2014. [6] E. Choi, M. T. Bahadori, E. Searles, C. Coffey, and J. Sun, “Multi-layer Representation Learning for Medical Concepts,” Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. - KDD ’16 1495-1504, 2016. [7] E. Choi, M. T. Bahadori, L. Song, W. F. Stewart, and J. Sun, “GRAM: Graph-based Attention Model for Healthcare Representation Learning,” arXiv:1611.07012v3, 2016. [8] D. Kartchner, T. Christensen, J. Humpherys, and S. Wade, “Code2Vec: Embedding and Clustering Medical Diagnosis Data,” in Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017, 2017, pp. 386–390. [9] A. L. Beam et al., “Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data,” arXiv:1804.01486, 2018. [10] E. Choi, M. T. Bahadori, J. A. Kulas, A. Schuetz, W. F. Stewart, and J. Sun, “RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism,” arXiv:1608.05745v4, no. Nips, 2016. [11] K. Fernandes, D. Chicco, J. S. Cardoso, and J. Fernandes, “Supervised deep learning embeddings for the prediction of cervical cancer diagnosis,” PeerJ Comput. Sci., vol. 4, no. Cdc, p. e154, 2018. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74334 | - |
| dc.description.abstract | 門診藥物推薦系統可作為門診醫師開立處方的參考，亦有助於藥物整合的進行。我們使用台北市立聯合醫院的門診就診資料，並提出兩種模型，一種是以相互加強原理拆解藥物對於診斷的機率分佈，一種是以自注意力機制為基礎的神經網路模型建構診斷與藥物組合的嵌入向量，進行以就診為單位的藥物推薦。 | zh_TW |
| dc.description.abstract | Medication recommendation system can assist doctors making prescription, and it is also useful for medication reconciliation. We use outpatient data from Taipei City Hospital, and propose two approaches. One use mutual reinforcement to decompose distribution of medications, and the other one is building embeddings based on self-attention mechanism. We use these two methods to make encounter-based outpatient medication recommendation. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T08:30:22Z (GMT). No. of bitstreams: 1 ntu-108-R06946010-1.pdf: 1966865 bytes, checksum: bed2446e1bf19b1a8b9e81a74514e54b (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES vii Chapter 1 Introduction 1 1.1 Outpatient Setting 1 1.2 ICD-10 and ATC Codes 2 1.2.1 ICD-10 Codes 2 1.2.2 ATC Codes 3 Chapter 2 Related Works 4 2.1 Medication Prediction and Recommendation 4 2.2 Medical Concept Representation 5 Chapter 3 Problem Formulation 6 3.1 Problem Definition 6 3.2 Notations 6 Chapter 4 Methodology 7 4.1 Overview 7 4.2 Decomposing Approach 8 4.3 Single-ENcounter-Double-embedding (SEND) Model 10 4.3.1 Hierarchical Coding 10 4.3.2 Building Encounter Embedding 11 4.3.3 Generating Recommendation 12 Chapter 5 Experimental Setups 14 5.1 Dataset 14 5.1.1 Dataset Splitting 15 5.1.2 Merging Medication Set 16 5.2 Baseline Methods 16 5.3 Evaluation Method 17 Chapter 6 Results and Discussion 19 6.1 Baseline Comparison 19 6.2 Parameter Analysis 20 6.3 Subgroup Analysis 22 6.4 Embedding Analysis 25 6.5 Example 28 Chapter 7 Conclusion and Future Works 29 7.1 Conclusion 29 7.2 Future Works 29 REFERENCE 30 | |
| dc.language.iso | en | |
| dc.subject | 自注意力機制 | zh_TW |
| dc.subject | 門診 | zh_TW |
| dc.subject | 藥物推薦 | zh_TW |
| dc.subject | 藥物預測 | zh_TW |
| dc.subject | 嵌入式向量 | zh_TW |
| dc.subject | Medication recommendation | en |
| dc.subject | Outpatient | en |
| dc.subject | Embedding | en |
| dc.subject | Self-Attention Mechanism | en |
| dc.subject | Medication Prediction | en |
| dc.title | 以就診為單位之門診藥物推薦 | zh_TW |
| dc.title | Encounter-Based Outpatient Medication Recommendation | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 王大為(Da-Wei Wang) | |
| dc.contributor.oralexamcommittee | 盧文祥(Wen-Hsiang Lu),王正豪(Jenq-Haur Wang),黃乾綱(Chien-Kang Huang) | |
| dc.subject.keyword | 門診,藥物推薦,藥物預測,自注意力機制,嵌入式向量, | zh_TW |
| dc.subject.keyword | Outpatient,Medication recommendation,Medication Prediction,Self-Attention Mechanism,Embedding, | en |
| dc.relation.page | 31 | |
| dc.identifier.doi | 10.6342/NTU201903242 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-08-12 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資料科學學位學程 | zh_TW |
| Appears in Collections: | 資料科學學位學程 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-108-1.pdf Restricted Access | 1.92 MB | Adobe PDF |
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