报告题目：人工智能用于癌症患者早期临床风险预测：迈向多学科研究的未来

AI for Cancer Patients in the Early Clinical Risk: Towards the Future of Multidisciplinary Research

时间：2024年8月19日上午10点

地点：飞云楼209

报告人：张启昌教授，台湾中山医学大学

School of Medical Informatics, Chung Shan Medical University, Taiwan

Smart Healthcare Committee, Chung Shan Medical University Hospital, Taiwan

Prof. Dr. Chi-Chang Chang

Founding Chairman ICMHI. ICHSM. HBAAI. JEBMI. MDSAT. MDSAM. ISMDS

International Conference on Medical and Health Informatics (ICMHI) in 2017

International Conference on Healthcare Service Management (ICHSM) in 2018

Health Bigdata Analytics Artificial Intelligence Forum (HBAAI) in 2018

Joint Executive Board on Medical Informatics Taiwan (JEBMI) in 2018

Medical Decision Science Association of Taiwan (MEDSAT) in 2022

Medical Decision Science Association of Mongolia (MEDSAM) in 2023

International Symposium on Medical Decision Science (ISMDS) in 2023

Founding Editor-in-Chief Decision Science in Medicine (DsiM) in 2023

http://www.mdsat.org.tw,  president@mdsat.org.tw

 

 

报告摘要：

The growing burden of cancer poses a threat to human development. In this topical speech, we will focus on AI for cancer patients in the early clinical risk. Furthermore, we will focus on the future forms of multidisciplinary research and its extended synergies, which may now come in our era of digitization ([1],[2],[3],[4]). These new and future forms of multidisciplinary research with all its potential opportunities and risks became more and more part of my research during the last two decades, in particular in the field of medicine and health care ([5],[6]). Karl Popper (1963) said, “we are not students of some subject matter, but students of problems. And problems may cross the borders of any subject matter or discipline”([7]). I see them as reflections. To attract attention to solve the grand challenges or scientific curiosity, scientists from all disciplines of knowledge around the world must work together ([8],[9]). There will be a specific focus in this speech on what is realistically possible in the context of extended collaboration, as well as what can be regarded as really appropriate and not just technically feasible.

 

References

[1] C.-J. Tseng, C.-C. Chang*, C.-J. Lu (2017). Integration of ensemble learning and data mining techniques to predict risk factors for recurrent ovarian cancer. Artificial Intelligence in Medicine,78, 47-54.

[2] C.-L. Chan, C.-C. Chang* (2022). Big Data, Decision Models, and Public Health. International Journal of Environmental Research and Public Health, 19, 8543.

[3] Z.-W. Wang, C.-C. Chang*, Q. Zou (2020) COVID-19 Related Research by Data Mining in Single Cell Transcriptome Profiles. Journal of Electronic Science and Technology, 18, 1-5.

[4] Chen CC, Wei CJ, Tseng TY, Chiu MC, Chi-Chang Chang (2024). Applying Object Detection and Large Language Model to Establish a Smart Telemedicine Diagnosis System with Chatbot: A Case Study of Pressure Injuries Diagnosis System. Telemed J E Health. 30(6) e1705-e1712.

[5]. C.-C. Chang, C.-J. Lu, C. Cheewakriangkrai, S.-H. Chang (2018) Preface for the Special Issue on Computational Intelligence Technologies Meet Medical Informatics -From Prediction to Prognosis, Journal of Universal Computer Science, 24, 662-664.

[6] Y-W Chu and C.-C. Chang (2023), Editorial: Using physical & genomics markers for smart therapy via expert systems with computer learning. Front. Genet. 14:1336399.

[7] K.-R. Popper (1963). Conjectures and Refutations: The Growth of Scientific Knowledge. New York: Routledge and Kegan Paul, 1963, p. 88.

[8] C.-C. Chang and C.-J. Lu (2018). Preface for the Special Issue on Computational Intelligence Technologies Meets Medical informatics. Journal of Quality, 25, 141-142.

[9] C.-C. Chang*, C.-J. Lu (2020). Preface for the Special Issue on Measuring Quality Improvement in Healthcare. Journal of Quality, 27, 3-5.