CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis

no code implementations • 17 Jul 2020 • Liantao Ma, Xinyu Ma, Junyi Gao, Chaohe Zhang, Zhihao Yu, Xianfeng Jiao, Wenjie Ruan, Yasha Wang, Wen Tang, Jiangtao Wang

Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide.

Knowledge Distillation Length-of-Stay prediction +1

Mortality Prediction with Adaptive Feature Importance Recalibration for Peritoneal Dialysis Patients: a deep-learning-based study on a real-world longitudinal follow-up dataset

1 code implementation • 17 Jan 2023 • Liantao Ma, Chaohe Zhang, Junyi Gao, Xianfeng Jiao, Zhihao Yu, Xinyu Ma, Yasha Wang, Wen Tang, Xinju Zhao, Wenjie Ruan, Tao Wang

Here, our objective is to develop a deep learning model for a real-time, individualized, and interpretable mortality prediction model - AICare.

Feature Importance Mortality Prediction

Microstructure-Empowered Stock Factor Extraction and Utilization

no code implementations • 16 Aug 2023 • Xianfeng Jiao, Zizhong Li, Chang Xu, Yang Liu, Weiqing Liu, Jiang Bian

To address these challenges, we propose a novel framework that aims to effectively extract essential factors from order flow data for diverse downstream tasks across different granularities and scenarios.

Stock Trend Prediction

Truth Forest: Toward Multi-Scale Truthfulness in Large Language Models through Intervention without Tuning

1 code implementation • 29 Dec 2023 • Zhongzhi Chen, Xingwu Sun, Xianfeng Jiao, Fengzong Lian, Zhanhui Kang, Di Wang, Cheng-Zhong Xu

We introduce Truth Forest, a method that enhances truthfulness in LLMs by uncovering hidden truth representations using multi-dimensional orthogonal probes.