Human phenotype ontology entity resolver

This advanced pipeline extracts human phenotype entities from clinical texts and utilizes the sbiobert_base_cased_mli Sentence Bert Embeddings to map these entities to their corresponding Human Phenotype Ontology (HPO) codes. It also returns associated codes from the following vocabularies for each HPO code: - MeSH (Medical Subject Headings)- SNOMED- UMLS (Unified Medical Language System ) - ORPHA (international reference resource for information on rare diseases and orphan drugs) - OMIM (Online Mendelian Inheritance in Man). <p><br/></p> Use the provided Streamlit playground application to test this service. <p><br/></p> **Covered entities:** HP <p><br/></p> Using this model can accelerate clinical research and time-to-market for new drugs. It can also improve efficacy of treatments through personalized medicine approaches.本先进流水线可从临床文本中提取人类表型实体，并借助sbiobert_base_cased_mli 句BERT嵌入（Sentence Bert Embeddings）将这些实体映射至对应的人类表型本体（Human Phenotype Ontology, HPO）编码。 此外，其还将为每个HPO编码关联以下词汇表中的对应编码：医学主题词表（Medical Subject Headings, MeSH）、SNOMED、统一医学语言系统（Unified Medical Language System, UMLS）、罕见病与孤儿药信息国际参考资源（ORPHA）、在线人类孟德尔遗传（Online Mendelian Inheritance in Man, OMIM）。 可通过提供的Streamlit交互式演示应用测试本服务。 **覆盖实体：** HP 本模型可加速临床研究与新药上市进程，还可通过个性化医疗方案提升治疗有效性。

搜集汇总

背景与挑战

背景概述

该数据集是一个高级流程，能够从临床文本中提取人类表型实体，并利用sbiobert_base_cased_mli嵌入将其映射到人类表型本体（HPO）代码，同时返回MeSH、SNOMED等相关医学术语系统的关联代码。它覆盖HP实体，可加速临床研究、药物开发，并提升个性化医疗的疗效。