PhenoDP:利用深度学习进行基于表型的病例报告、疾病排名和症状推荐。

IF 10.4 1区 生物学 Q1 GENETICS & HEREDITY
Baole Wen, Sheng Shi, Yi Long, Yanan Dang, Weidong Tian
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引用次数: 0

摘要

背景:由于不完整的表型数据和罕见疾病表现的复杂性,当前基于表型的诊断工具往往难以准确地确定疾病的优先级。此外,他们缺乏产生以患者为中心的临床见解或推荐进一步的症状进行鉴别诊断的能力。方法:我们开发了一个基于深度学习的工具包,包含三个模块:Summarizer, Ranker和Recommender。Summarizer对提炼出来的大型语言模型进行了微调,以便根据患者的人类表型本体(Human Phenotype Ontology, HPO)术语创建临床摘要。Ranker通过结合基于信息内容、基于phi和基于语义的相似性度量来对疾病进行排序。推荐者采用对比学习来推荐额外的HPO术语,以提高诊断的准确性。结果:与通用语言模型FlanT5相比,PhenoDP的Summarizer产生了更多临床连贯和以患者为中心的摘要。Ranker实现了最先进的诊断性能,在模拟和现实世界数据集上始终优于现有的基于表型的方法。当推荐的术语被纳入不同的排名管道时,推荐人在提高诊断准确性方面也优于gpt - 40和PhenoTips。结论:PhenoDP通过深度学习增强孟德尔病的诊断,提供精确的总结、排序和症状推荐。其卓越的性能和开源设计使其成为一种有价值的临床工具,具有加速诊断和改善患者预后的潜力。PhenoDP可在https://github.com/TianLab-Bioinfo/PhenoDP免费获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PhenoDP: leveraging deep learning for phenotype-based case reporting, disease ranking, and symptom recommendation.

Background: Current phenotype-based diagnostic tools often struggle with accurate disease prioritization due to incomplete phenotypic data and the complexity of rare disease presentations. Additionally, they lack the ability to generate patient-centered clinical insights or recommend further symptoms for differential diagnosis.

Methods: We developed PhenoDP, a deep learning-based toolkit with three modules: Summarizer, Ranker, and Recommender. The Summarizer fine-tuned a distilled large language model to create clinical summaries from a patient's Human Phenotype Ontology (HPO) terms. The Ranker prioritizes diseases by combining information content-based, phi-based, and semantic-based similarity measures. The Recommender employs contrastive learning to recommend additional HPO terms for enhanced diagnostic accuracy.

Results: PhenoDP's Summarizer produces more clinically coherent and patient-centered summaries than the general-purpose language model FlanT5. The Ranker achieves state-of-the-art diagnostic performance, consistently outperforming existing phenotype-based methods across both simulated and real-world datasets. The Recommender also outperformed GPT-4o and PhenoTips in improving diagnostic accuracy when its suggested terms were incorporated into different ranking pipelines.

Conclusions: PhenoDP enhances Mendelian disease diagnosis through deep learning, offering precise summarization, ranking, and symptom recommendation. Its superior performance and open-source design make it a valuable clinical tool, with potential to accelerate diagnosis and improve patient outcomes. PhenoDP is freely available at https://github.com/TianLab-Bioinfo/PhenoDP .

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来源期刊
Genome Medicine
Genome Medicine GENETICS & HEREDITY-
CiteScore
20.80
自引率
0.80%
发文量
128
审稿时长
6-12 weeks
期刊介绍: Genome Medicine is an open access journal that publishes outstanding research applying genetics, genomics, and multi-omics to understand, diagnose, and treat disease. Bridging basic science and clinical research, it covers areas such as cancer genomics, immuno-oncology, immunogenomics, infectious disease, microbiome, neurogenomics, systems medicine, clinical genomics, gene therapies, precision medicine, and clinical trials. The journal publishes original research, methods, software, and reviews to serve authors and promote broad interest and importance in the field.
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