Two algorithms for improving model-based diagnosis using multiple observations and deep learning.

Model-based diagnosis (MBD) is a critical problem in artificial intelligence. Recent advancements have made it possible to address this challenge using methods like deep learning. However, current approaches that use deep learning for MBD often struggle with accuracy and computation time due to the limited diagnostic information provided by a single observation.

Analysis of the effect of glutamyltransferase on hyperlipidemia based on decision tree.

Objectives: This study is designed to analyze the potential influencing factors of hyperlipidemia, and to explore the relationship between liver function indicators such as gamma-glutamyltransferase (GGT) and hyperlipidemia.

Methods: Data were derived from 7599 outpatients who visited the Department of Endocrinology of the First Hospital of Jilin University (2017-2019). A multinomial regression model is used to identify related factors of hyperlipidemia and the decision tree method is used to explore the general rules in hyperlipidemia patients and non-hyperlipidemia patients on these factors.

Distant supervision for neural relation extraction integrated with word attention and property features.

Distant supervision for neural relation extraction is an efficient approach to extracting massive relations with reference to plain texts. However, the existing neural methods fail to capture the critical words in sentence encoding and meanwhile lack useful sentence information for some positive training instances. To address the above issues, we propose a novel neural relation extraction model.