no code implementations • 11 May 2024 • Phoebe Jing, Yijing Gao, Yuanhang Zhang, Xianlong Zeng
In the realm of predictive analytics, the nuanced domain knowledge of investigators often remains underutilized, confined largely to subjective interpretations and ad hoc decision-making.
no code implementations • 23 Apr 2024 • Phoebe Jing, Yijing Gao, Xianlong Zeng
In the field of fraud detection, the availability of comprehensive and privacy-compliant datasets is crucial for advancing machine learning research and developing effective anti-fraud systems.
no code implementations • 3 Apr 2024 • Xianlong Zeng, Fanghao Song, Ang Liu
This study introduces a simple yet effective method for identifying similar data points across non-free text domains, such as tabular and image data, using Large Language Models (LLMs).
no code implementations • 24 Jun 2021 • Xianlong Zeng, Simon Lin, Chang Liu
In addition, our framework showed a great generalizability potential to transfer learned knowledge from one institution to another, paving the way for future healthcare model pre-training across institutions.
1 code implementation • 24 Jun 2021 • Xianlong Zeng, Fanghao Song, Zhongen Li, Krerkkiat Chusap, Chang Liu
Our method can be divided into three stages: 1) a neighborhood generation stage, which generates instances based on the given sample; 2) a classification stage, which yields classifications on the generated instances to carve out the local decision boundary and delineate the model behavior; and 3) a human-in-the-loop stage, which involves human to refine and explore the neighborhood of interest.
BIG-bench Machine Learning Explainable artificial intelligence +1
no code implementations • 23 Jun 2021 • Xianlong Zeng, Simon Lin, Chang Liu
The claims data, containing medical codes, services information, and incurred expenditure, can be a good resource for estimating an individual's health condition and medical risk level.
no code implementations • 13 Sep 2019 • Xianlong Zeng, Soheil Moosavinasab, En-Ju D Lin, Simon Lin, Razvan Bunescu, Chang Liu
Efficient representation of patients is very important in the healthcare domain and can help with many tasks such as medical risk prediction.