activity
20172026
most citedEMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning

2 citations · 5 across the 6 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

MedCalc-R1: Knowledge-Guided Reward Framework for Medical Mathematical Reasoning

Haotian Wang, Lian Yan, Xingzhi Yao +4

In Reinforcement Learning with Verifiable Rewards (RLVR) frameworks for mathematical reasoning tasks, floating-point results are typically evaluated using a tolerance-based reward.…

cs.AI2024

FiVTS: Time Series Forecasting Via Capturing Intra- and Inter-Variable Variations in the Frequency Domain

Rujia Shen, Yang Yang, Yaoxion Lin +4

Time series forecasting (TSF) plays a crucial role in various applications, including medical monitoring and crop growth. Despite the advancements in deep learning methods for TSF,…

cs.AI2022

Causal Coupled Mechanisms: A Control Method with Cooperation and Competition for Complex System

Xuehui Yu, Jingchi Jiang, Xinmiao Yu +2

Complex systems are ubiquitous in the real world and tend to have complicated and poorly understood dynamics. For their control issues, the challenge is to guarantee accuracy, robu…

cs.AI2018

Medical Knowledge Embedding Based on Recursive Neural Network for Multi-Disease Diagnosis

Jingchi Jiang, Huanzheng Wang, Jing Xie +3

The representation of knowledge based on first-order logic captures the richness of natural language and supports multiple probabilistic inference models. Although symbolic represe…

cs.AI20172 cited

EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning

Chao Zhao, Jingchi Jiang, Yi Guan

Objective: Electronic medical records (EMRs) contain an amount of medical knowledge which can be used for clinical decision support (CDS). Our objective is a general system that ca…

cs.AI20171 cited

Learning and inference in knowledge-based probabilistic model for medical diagnosis

Jingchi Jiang, Chao Zhao, Yi Guan +1

Based on a weighted knowledge graph to represent first-order knowledge and combining it with a probabilistic model, we propose a methodology for the creation of a medical knowledge…