name---->TBD-57(字串長度:6)---->張詠淳(字串長度:9)```hide---->0(字串長度:1)---->0
(字串長度:2)```name2---->(字串長度:0)---->Yung-Chun Chang(字串長度:15)```talk_title---->(字串長度:0)---->The Future is Here - Unlocking the Potential of Clinical Data with NLP(字串長度:70)```can_playback---->no(字串長度:2)---->yes(字串長度:3)```company---->(字串長度:0)---->Graduate Institute of Data Science, Taipei Medical University(字串長度:61)```position---->(字串長度:0)---->Associate Professor(字串長度:19)```company2---->(字串長度:0)---->Office of Data Science, Taipei Medical University(字串長度:49)```position2---->(字串長度:0)---->Deputy Chief Data Officer(字串長度:25)```desc---->(字串長度:0)---->In this talk, we will delve into the remarkable advancements in Natural Language Processing (NLP) and their transformative potential in the realm of clinical data analysis. The field of medical imaging has seen tremendous growth, and one of the key challenges lies in translating radiomics features into actionable insights. Firstly, we will explore a groundbreaking approach that addresses this challenge by automatically generating Lung-RADS reports from low-dose lung CT scans. By leveraging the power of deep learning to construct a multi-objective framework, we have advanced the extraction of essential information to aid in the accurate diagnosis of lung abnormalities. Moving beyond medical imaging, pathology reports play a crucial role in guiding treatment decisions, particularly in lung cancer cases. We will investigate how Generative Pre-trained Transformers (GPT) can extract vital features from pathology reports, enabling the identification of key indicators for effective lung cancer treatment. By harnessing the capabilities of GPT, we unlock a wealth of information buried within textual data, enhancing the precision of personalized therapies.
 
 Moreover, advanced lung cancer requires comprehensive decision support systems. Deep learning-based techniques offer immense potential in this domain by automating the extraction of key features from pathology reports. By integrating these extracted features into a decision support system, we empower clinicians with valuable insights, aiding in the development of tailored treatment plans and ultimately improving patient outcomes. Throughout this presentation, we will witness how NLP, combined with deep learning methodologies, revolutionizes the analysis of clinical data. By bridging the gap between unstructured text and actionable knowledge, we unlock the full potential of clinical data and propel us into an era where diagnoses are more accurate, treatments are more personalized, and patient care is optimized.
 
 Together, let us embark on a journey that demonstrates how NLP is shaping the future of healthcare, forging a path towards a world where the integration of technology and medicine empowers us to extend the potential of clinical data like never before. The future is indeed here, and it is through the fusion of NLP and clinical data that we shall explore its boundless possibilities.(字串長度:2376)```me_desc---->(字串長度:0)---->Dr. Yung-Chun Chang is a renowned expert in the fields of text mining, natural language processing (NLP), and the associated technologies of machine learning and linguistic recognition. He excels in harnessing these technologies to address challenges across diverse domains, with a primary focus on intelligent healthcare, bioinformatics, business intelligence, and language comprehension. In 2016, Dr. Chang received accolades for his exceptional contributions to research, notably the Outstanding Postdoctoral Research Award from Academia Sinica and the Doctoral Dissertation Award from the Computational Linguistics Society. In 2017, he joined the faculty of Taipei Medical University, where he has since published numerous papers in top-tier international journals and conferences, such as ACL and TKDE.    Passionate about pedagogy, Professor Chang has repeatedly been recognized for his teaching excellence, earning both campus-wide Outstanding Teacher and Distinguished New Faculty awards. Furthermore, he is deeply committed to student mentorship and guidance. His students have achieved commendable results in various international competitions. Noteworthy accolades include a first-place finish in the 2023 NTCIR International Information Retrieval Competition, a second-place finish in the 2022 BioCreative International Biomedical Literature Automated Mining Competition, and a gold award at the Taiwan AI EXPO.   張詠淳博士專精於文字探勘與自然語言處理以及相關的機器學習與語言辨識技術，擅長將這些技術應用於解決不同領域的問題，主要研究領域為智慧醫療、生物資訊、商業智慧以及語言理解。張教授在2016年曾榮獲中研院博士後傑出研究獎以及計算語言學會博士論文獎。於2017年至臺北醫學大學從事教職，近年發表多篇國際頂尖期刊與會議論文。張教授熱愛教學，不僅屢獲校級優良教師、新進優良教師等教學獎項，更致力於學生的培育和指導。在多項國際競賽中獲得優異成績，包括2023年NTCIR國際資料檢索競賽第一名、2022年BioCreative國際生醫文獻自動探勘競賽第二名，以及Taiwan AI EXPO大賽金獎等。(字串長度:2216)```talkfile_link---->簡報57(字串長度:8)---->V(字串長度:1)```position_for_agenda---->
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