||Named Entity Recognition in Chinese Medical Literature Using Pretraining Models
||... about the medicine [ 2 , 3 ]. Named Entity Recognition (NER) is the fundamental task in Natural Language Processing (NLP). It is also the initial step in extracting valuable knowledge from unstructured text and building a medical Knowledge Graph (KG). As shown in Figure 1 , NER aims to recognize entities from unstructured text, and the results of NER may affect subsequent knowledge extraction tasks, such as the Relation Extraction (RE). In the ...
||Medical Knowledge Graph to Enhance Fraud, Waste, and Abuse Detection on Claim Data: Model Development and Performance Evaluation
||... insurance inspectors tend to build an intelligent system to detect suspicious claims with inappropriate diagnoses/medications automatically. Objective: The aim of this study was to develop an automated method for making use of a medical knowledge graph to identify clinically suspected claims for FWA detection. Methods: First, we identified the medical knowledge that is required to assess the clinical rationality of the claims. We then searched for data sources that ...
||Relation Extraction Based on Fusion Dependency Parsing from Chinese EMRs
||The Electronic Medical Record (EMR) contains a great deal of medical knowledge related to patients, which has been widely used in the construction of medical knowledge graph s. Previous studies mainly focus on the features based on surface semantics of EMRs for relation extraction, such as contextual feature, but the features of sentence structure in Chinese EMRs have been neglected. In this paper, a fusion dependency parsing-based relation extraction method ...
||A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development
||... based algorithms, including TransE, TransH, TransR, TransD, and TranSparse, have been proposed to learn effective embedding vectors from typical knowledge graphs in which the relations between head and tail entities are deterministic. However, in medical knowledge graphs, the relations between head and tail entities are inherently probabilistic. This difference introduces a challenge in embedding medical knowledge graphs. Objective: We aimed to address the challenge of how to learn the probability ...
||If you did not already know
||... on data-driven learning and cannot be able to encode extra expert knowledge graph. In this work, we propose an End-to-End Knowledge-routed Relational Dialogue System (KR-DS) that seamlessly incorporates rich medical knowledge graph into the topic transition in dialogue management, and makes it cooperative with natural language understanding and natural language generation. A novel Knowledge-routed Deep Q-network (KR-DQN) is introduced to manage topic ...
||Ping An's COVID-19 Smart Audio Screening System Identified More Than 1,600 Suspected Cases to Date
||... health administrations, health care institutions, and medical services providers to enhance public medical services. Ping An Smart Healthcare has clinical decision support models for thousands of diseases, and has developed an excellent and comprehensive medical knowledge graph , covering drugs, diseases, prescriptions, risk factors and experts library. Ping An Smart Healthcare is available to nearly 800 million people in more than 70 cities in China and Southeast Asia . SOURCE Ping An ...
||Require Clinical Ontologist at Symbolic AI
||... assurance of automated mapping using Health Language terminology application of data to standardized medical terminologies. Editing/updating medical taxonomy/ontology for hierarchical and semantic relationship. Training Artificial General Intelligence Software tools for learning on Medical Knowledge Graph s Manage medical records interoperability by mapping clinical content to national terminology standards like RxNorm, SNOMED and UMLS Coordinate with internal and external organizations to enhance the quality of mapping and interoperability Build databases ...