Medical knowledge embedding based on recursive neural network for multi-disease diagnosis.

The representation of knowledge based on first-order logic captures the richness of natural language and supports multiple probabilistic inference models. Although symbolic representation enables quantitative reasoning with statistical probability, it is difficult to utilize with machine learning models as they perform numerical operations. In contrast, knowledge embedding (i.

Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN.

Specific entity terms such as disease, test, symptom, and genes in Electronic Medical Record (EMR) can be extracted by Named Entity Recognition (NER). However, limited resources of labeled EMR pose a great challenge for mining medical entity terms. In this study, a novel multitask bi-directional RNN model combined with deep transfer learning is proposed as a potential solution of transferring knowledge and data augmentation to enhance NER performance with limited data.

A multitask bi-directional RNN model for named entity recognition on Chinese electronic medical records.

Background: Electronic Medical Record (EMR) comprises patients' medical information gathered by medical stuff for providing better health care. Named Entity Recognition (NER) is a sub-field of information extraction aimed at identifying specific entity terms such as disease, test, symptom, genes etc. NER can be a relief for healthcare providers and medical specialists to extract useful information automatically and avoid unnecessary and unrelated information in EMR.

Max-margin weight learning for medical knowledge network.

Background And Objective: The application of medical knowledge strongly affects the performance of intelligent diagnosis, and method of learning the weights of medical knowledge plays a substantial role in probabilistic graphical models (PGMs). The purpose of this study is to investigate a discriminative weight-learning method based on a medical knowledge network (MKN).

Methods: We propose a training model called the maximum margin medical knowledge network (MKN), which is strictly derived for calculating the weight of medical knowledge.

Building a comprehensive syntactic and semantic corpus of Chinese clinical texts.

Objective: To build a comprehensive corpus covering syntactic and semantic annotations of Chinese clinical texts with corresponding annotation guidelines and methods as well as to develop tools trained on the annotated corpus, which supplies baselines for research on Chinese texts in the clinical domain.

Materials And Methods: An iterative annotation method was proposed to train annotators and to develop annotation guidelines. Then, by using annotation quality assurance measures, a comprehensive corpus was built, containing annotations of part-of-speech (POS) tags, syntactic tags, entities, assertions, and relations.

Increasing incidence of type 1 diabetes in children aged 0-14 years in Harbin, China (1990-2000).

Aims: (1) To determine the incidence of type 1 diabetes mellitus in children aged<15 years in Harbin, China and (2) to examine the trend in incidence over the period from 1990 to 2000.

Methods: Newly diagnosed cases of type 1 diabetes from 1990 to 2000 were identified among 1,286,154 Chinese children aged 0-14 years in Harbin. The primary source of case ascertainment was from hospital records and the secondary source from the health records of school clinics.