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燈號說明

審定:無
翻譯:趙志峰(簡介並寄信)
編輯:陳玉侖(簡介並寄信)


授課對象

本課程為醫學資訊碩士班學生之必修課程,亦開放給其他研究所學生及大學部高年級的學生。

專題

以下為本課程一些專題製作之範例,以領域區分:
  1. 資料庫之匿名性
    • 利用Boolean推論來匿名化資料庫

  2. 診斷模型
    • 利用病患可報告式之臨床病歷因子來預測冠狀動脈栓塞之病症。
    • 以基因演算法來選擇在邏輯迴歸之變數:冠狀動脈栓塞領域之範例。

  3. 預測模型
    • 建構並評估此模型,用以預測在冠狀動脈修復術與動脈支撐之後的死亡率與冠狀動脈栓塞情況。
    • 在病患血管整形後伴隨慢性腎衰竭之主要併發症:預測模型間之比較。


閱讀書目

本課程沒有指定教科書,然而,以下為推薦使用的參考教科書:

Hastie, T., R. Tibshirani, 與 J. H. Friedman. 《統計學習之要素:資料採礦、推論與預測》(The Elements of Statistical Learning: Data Mining, Inference, and Prediction) (Springer Series in Statistics.) Springer Verlag, Oct 2001.

Duda, Richard O., Peter E. Hart, 與 David G. Stork《樣型分類》(Pattern Classification) 2nd ed. John Wiley & Sons, Nov 2000.



選修條件

熟悉SAS與MATLEB軟體將會有幫助,可能需要參閱各自的使用者說明書。

評分

30% 作業

作業之繳交期限為每個單元的結束時,這些作業需要撰寫程式,且必須包含其程式碼。作業為個人單獨完成,在繳交期限後收到,將受到大量分數扣減之懲罰。在解答已經討論過的情況下,不再接受任何作業。

30% 期中考

期中考包含決策分析與機器學習兩主題,時間限制1.5小時。學生可帶課堂筆記,作業解答以及閱讀書目。

40% 期末專題

期末專題必須專為此課程所製作,並且必須個別獨立完成。即使此專題可包含部份先前用於其他用途的成果,重要的是,在製作為此課程設計的專題上,必須展現出實際努力成果。期末報告的型式期望能夠有5頁以上,而且須以15分鐘的上台報告型式說明如何執行。建議學生提前與講師討論他們的專題。期末報告的範例在課堂期間將會被提及。

MATLAB®為MathWorks公司之註冊商標。




Who Should Take this Course?

The course is required for students in the Master's Program in Medical Informatics, but is open to other graduate students and advanced undergraduates.

Projects

Below are some examples of projects developed in this course, by domain:

  1. Anonymization of Databases

    • Using Boolean Reasoning to Anonymize Databases


  2. Diagnostic Models

    • Using Patient-Reportable Clinical History Factors to Predict Myocardial Infarction

    • A Genetic Algorithm to Select Variables in Logistic Regression: Example in the Domain of Myocardial Infarction


  3. Prognostic Models

    • Development and Evaluation of Models to Predict Death and Myocardial Infarction Following Coronary Angioplasty and Stenting

    • Major Complications after Angioplasty in Patients with Chronic Renal Failure: A Comparison of Predictive Models


Readings

There is no required textbook for this course, however, the following textbooks are recommended for reference:

Hastie, T., R. Tibshirani, and J. H. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. (Springer Series in Statistics.) Springer Verlag, Oct 2001.

Duda, Richard O., Peter E. Hart, and David G. Stork. Pattern Classification. 2nd ed. John Wiley & Sons, Nov 2000.



Prerequisites

Familiarity with SAS and MATLAB® will be helpful, and consultation with respective user manuals may be necessary.

Grading

30% Homework

Homework's are due at the end of each module. They may require programming, and all code must be included. They are to be solved individually. Homework's received after the deadline may be subject to substantial grade penalty. No homework's will be accepted after the solutions have been handled.

30% Midterm

The midterm will contain topics from decision analysis and machine learning. There will be a strict time limit of 1.5 h. Students should bring class notes, homework solutions, and readings.

40% Final Project

The final project has to be developed for this course and should be done individually. Although the project may contain parts developed previously for another purpose, it is essential that substantial effort be demonstrated into developing a project specifically for this class. A final report in the form of a 5+ page paper is expected, as well as a demonstration of the implementation in the form of a 15 minute presentation. Students are advised to discuss their projects with the instructors ahead of time. Examples of final reports will be handled during the course.

MATLAB® is a trademark of The MathWorks, Inc.




 
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