Selected Publications

Breast cancer metastasis cannot be detected solely on the basis of surface markers of tumor cells or analytical measurements of single serum biomarkers. A more comprehensive overview of breast cancer may be achieved through the evaluation of a combination of various clinicopathological features. The random-forest-based model presented in this study may be helpful as part of a follow-up intervention decision support system.
In Int J Med Inform.
hVISA cannot be detected in most clinical microbiology laboratories because the required diagnostic tools are either expensive, time consuming, or labor intensive. To analyze complex MALDI-TOF mass spectra, machine learning (ML) algorithms can be used to generate robust hVISA detection models. We demonstrated that hVISA/VISA could be detected by analyzing MALDI-TOF mass spectra using ML.
In Front Microbiol.
Despite published guidelines, second-line therapy is often initiated without evidence of recommended use of first-line therapy. Apparent treatment failures, which may in fact be attributable to non-adherence to guidelines are common. Point of care and population-level processes are needed to monitor and improve guideline adherence.
In Diabetes Care.
The total payments insurance companies pay for knee and hip implants were twice as high as the average selling prices at which hospitals purchased the implants from manufacturers, resulting in hundreds of millions of dollars of additional insurance claims.
In JAMA.

Recent Publications

5 most recent publications

More Publications

  • Predicting breast cancer metastasis by using serum biomarkers and clinicopathological data with machine learning technologies

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  • Rapid Detection of Heterogeneous Vancomycin-Intermediate Staphylococcus aureus Based on Matrix-Assisted Laser Desorption Ionization Time-of-Flight: Using a Machine Learning Approach and Unbiased Validation

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  • Predicting post-stroke activities of daily living through a machine learning- based approach on initiating rehabilitation

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  • Antihyperglycemic Medications: A Claims-Based Estimate of First-Line Therapy Use Before Initialization of Second-Line Medications

    Details PDF Covered in Vector

  • Difference Between Estimated Purchase Price and Insurance Payments for Knee and Hip Implants in Privately Insured Patients Younger Than 65 Years

    Details PDF JAMA Covered in STAT

Projects

探索病歷表現型– 發展醫療大數據中的疾病發展歷程探勘方法與模組

建立醫療大數據標準化整合模組與開源套件、發展與驗證病程演進與病歷表現型探勘演算法,以及建立病歷表現型探勘與視覺化模組、開源套件與知識共享平台。

以機器學習演算法分析檢驗數值以偵測尿液中微生物-以陰道滴蟲為例

使用尿液檢驗與電子病歷資料,搭配三種機器學習演算法,邏輯迴歸、支持向量機,以及隨機森林法,找出最適合做陰道滴蟲檢體篩檢的機器學習模型。

使用電子病歷預測傳染病流行趨勢-以流行性感冒與流感併發重症為例

應用機器學習演算法建立預測模型,使醫療機構能更早一步發現可能的流行趨勢,即時啟動防疫措施與調整投入之醫療資源,改善醫療機構資源分配以及提升醫療機構的緊急應變量能。

應用機器學習方法進行多臨床參數分析以建立癌症預後預測之模型

建立乳癌預後預測模型。乳癌預後預測標的為一年內再復發情形,未來可幫助乳癌案例的介入追蹤決策,進一步達到早期診斷,早期治療以的目的。

以整合型健康照護資料評估傳染病盛行情形

藉由此整合型健康照護資料分析模式輔助傳染病監測,使傳染病的監測更為主動、及時且穩定,期盼最終能進一步改善現今傳染病監測模式與介入流程,提升傳染病監測品質與效率。

Teaching

  • Undergraduated Level

  • Graduated Level

    • 醫學資訊 Medical Informatics
  • EMBA

    • 巨量資料產業實務個案 Big Data Practices in Industries

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Recent & Upcoming Talks

Contact

  • yjtseng [at] mail.cgu.edu.tw
  • +886-3-2118800 #5815
  • No.259, Wenhua 1st Rd., Guishan Dist., Taoyuan City 333, Taiwan
  • email for appointment