The AI-assisted Innovation In Patient Flow of Emergency Department
►國立臺灣大學 陳銘憲特聘教授 / Distinguished Prof. Ming-Syan Chen, National Taiwan University
自2020 年三月起，臺大AI中心結合臺灣大學AI技術應用與臺大醫院急診醫學部，聯手企業夥伴，執行科技部 AI 拔尖整合計畫。運用人工智慧技術，在臺大醫院急診室打造智慧急診全新流程。 藉由導入精準電子檢傷、病史即時分析、胸部 X 光即時危險辨識、安全離部的決策推薦、院內心跳停止事件的高危風險預測、 打造物聯網場域建立高危病患預警，與醫護過勞預測等各種 AI 模型，提供醫護即時預測與診療輔助建議，部分流程更有效縮短檢驗診療時間從數小時到數十分鐘，大幅縮短病患停留急診期間，有效降低誤診率與優化醫療品質，最終解決急診壅塞問題。
Since March 2020, Most Joint Research Center for AI Technology and All Vista Healthcare combined AI technology applications of NTU, the emergency department of NTUH, and industrial partners to execute Capstone project. With AI technology, the Capstone project team is establishing the brand-new intelligent patient flow of emergency departments by deploying various categories of AI models.It includes precise triage, real-time medical history analysis, real-time X-Ray recognition for chest inappropriate intubation, appropriate discharge recommendation, high-risk prediction for In-Hospital Cardiac Arrest, proactive warning for patient’s critical illness, and medical personnel overwork prediction.
We expect to provide real-time prediction and treatment recommendations for assisting the doctor’s diagnosis process. Furthermore, some diagnosis processes could be shortened substantially from hours to minutes. Our goal is not only to reduce the erroneous diagnosis effectively but to optimize medical quality. Finally, it could resolve the ED crowding phenomenon significantly.
▲影像辨識疼痛指數 | Image recognition application on pain rating index
▲AI輔助的創新急診流程 | An AI assisted Innovated Emergency Medicine Patient Flow
此研究歸屬科技部 AI 專案計畫執行成果,詳細資訊請參考附錄之計畫總表第 1 項
For the name of the project which output this research, please refer to project serial no. 1 on the List of MOST AI projects on Appendix
Fine-Grained Financial Opinion Mining
► 國立臺灣大學 陳信希特聘教授 / Distinguished Prof. Hsin-Hsi Chen, National Taiwan University
As one of the dominant teams in financial opinion mining, we have proposed several novel research issues in financial opinion mining and lead the development of this field by offering benchmark datasets. We made up the shortcomings of previous embedding methods in numeral representation, and designed a multi-task architecture for solving the numeral attachment task. The first exploration of argument mining in financial narratives was also present. We also pointed out the important issue in the financial false information detection task.
The demonstration systems based on our results were developed, including visualizing the textual data on financial social media platforms to indicators and an opinion-based personalized recommendation system. Additionally, the proposed system won the first prize in the FinTech Competition.
- CIKM-2020: https://reurl.cc/bXQDRr
- ACL-2019: https://reurl.cc/ZGK9QV
- SIGIR-2019: https://reurl.cc/3ag7NV
- IJCAI-2019: https://reurl.cc/O0ZG0A
- 獲獎新聞: https://reurl.cc/vqY0qA
▲ System Flowchart
此研究歸屬科技部 AI 專案計畫執行成果,詳細資訊請參考附錄之計畫總表第 3 項
For the name of the project which output this research, please refer to project serial no. 3 on the List of MOST AI projects on Appendix
NLP-based Deep Learning for Fast Screening of Alzheimer’s Disease
► 國立臺灣大學 傅立成講座教授 / Chair Prof. Li-Chen Fu, National Taiwan University
The team has designed and implemented an effective screening system, which could analyze whether an elder has Mild Cognitive Impairment (MCI) or Alzheimer’s disease (AD) on the basis of neuropsychology.
Empowered by a tailored Neural Network that can process natural languages, our system can efficiently analyze and classify spoken text or audio of an elder to detect dementia in an early phase. The accuracy of detection on MCI and AD with the test cases from the National Taiwan University Hospital is as high as 82% and 91%, which shows better results than other contemporary works. Hence, as Taiwan has become an aged society, it is necessary to have such system to safeguard the cognitive health of the elders.
The elder will undergo the neuropsychology tests. Then, a deep-learning-based system will analyze the spoken text of the elder and show whether the elder might have Alzheimer’s disease.
此研究歸屬科技部 AI 專案計畫執行成果,詳細資訊請參考附錄之計畫總表第 23 項
For the name of the project which output this research, please refer to project serial no. 23 on the List of MOST AI projects on Appendix
▎YOLOv4 and Scaled-YOLOv4
► 中央研究院 廖鴻源博士 / Dr. Mark Liao Hong-yuan, Academia Sinica
YOLOv4 是中研院 AI 團隊與義隆電子一個合作案的產出。它在2020年9月28日到2020年12月15日雄踞在以 MS COCO資料集為基礎的物件偵測競賽排行榜中名列世界第一達到兩個半月之久。它的原始碼開放給全世界使用。臺灣包括竹科、南科等高科技廠商都使用此技術為基礎開發它們的產品。中研院團隊也將技術報告放在archive網站上面成為技術報告,截至2021年9月7日止,該技術報告已被引用 1498 次之多(Google Scholar)。
YOLO4 is the output of a cooperation project between Academia Sinica and ELAN electronics Inc. From September 28, 2020 to December 15, 2020, it ranked first in the world in the object detection competition based on the MS COCO dataset. It defeated all world-class companies, including Google, Facebook, Microsoft, Qualcomm, Amazon, etc. We open the source codes of YOLOv4 to the whole world.
Many high-tech manufacturers in Taiwan, including Hsinchu and Southern Science Park, use this technology as a basis to develop their products. The Academia Sinica research team also turned the report into an archive technical report. As of September 7, 2021, this technical report has been cited 1498 times (Google Scholar).
Using YOLOv4 to perform vehicle detection during the night.
Using YOLOv4 to perform vehicle detection during the day time.
此研究歸屬科技部 AI 專案計畫執行成果,詳細資訊請參考附錄之計畫總表第 8 項
For the name of the project which output this research, please refer to project serial no. 8 on the List of MOST AI projects on Appendix