征询专家意见,利用患者数据辅助基于深度学习的莱姆病分类器

IF 3.7 2区 医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Medical Informatics Pub Date : 2024-10-31 DOI:10.1016/j.ijmedinf.2024.105682
{"title":"征询专家意见,利用患者数据辅助基于深度学习的莱姆病分类器","authors":"","doi":"10.1016/j.ijmedinf.2024.105682","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Diagnosing erythema migrans (EM) skin lesion, the most common early symptom of Lyme disease, using deep learning techniques can be effective to prevent long-term complications. Existing works on deep learning based EM recognition only utilizes lesion image due to the lack of a dataset of Lyme disease related images with associated patient data. Doctors rely on patient information about the background of the skin lesion to confirm their diagnosis. To assist deep learning model with a probability score calculated from patient data, this study elicited opinions from fifteen expert doctors. To the best of our knowledge, this is the first expert elicitation work to calculate Lyme disease probability from patient data.</div></div><div><h3>Methods</h3><div>For the elicitation process, a questionnaire with questions and possible answers related to EM was prepared. Doctors provided relative weights to different answers to the questions. We converted doctors' evaluations to probability scores using Gaussian mixture based density estimation. We exploited formal concept analysis and decision tree for elicited model validation and explanation. We also proposed an algorithm for combining independent probability estimates from multiple modalities, such as merging the EM probability score from a deep learning image classifier with the elicited score from patient data.</div></div><div><h3>Results</h3><div>We successfully elicited opinions from fifteen expert doctors to create a model for obtaining EM probability scores from patient data.</div></div><div><h3>Conclusions</h3><div>The elicited probability score and the proposed algorithm can be utilized to make image based deep learning Lyme disease pre-scanners robust. The proposed elicitation and validation process is easy for doctors to follow and can help address related medical diagnosis problems where it is challenging to collect patient data.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Expert opinion elicitation for assisting deep learning based Lyme disease classifier with patient data\",\"authors\":\"\",\"doi\":\"10.1016/j.ijmedinf.2024.105682\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Diagnosing erythema migrans (EM) skin lesion, the most common early symptom of Lyme disease, using deep learning techniques can be effective to prevent long-term complications. Existing works on deep learning based EM recognition only utilizes lesion image due to the lack of a dataset of Lyme disease related images with associated patient data. Doctors rely on patient information about the background of the skin lesion to confirm their diagnosis. To assist deep learning model with a probability score calculated from patient data, this study elicited opinions from fifteen expert doctors. To the best of our knowledge, this is the first expert elicitation work to calculate Lyme disease probability from patient data.</div></div><div><h3>Methods</h3><div>For the elicitation process, a questionnaire with questions and possible answers related to EM was prepared. Doctors provided relative weights to different answers to the questions. We converted doctors' evaluations to probability scores using Gaussian mixture based density estimation. We exploited formal concept analysis and decision tree for elicited model validation and explanation. We also proposed an algorithm for combining independent probability estimates from multiple modalities, such as merging the EM probability score from a deep learning image classifier with the elicited score from patient data.</div></div><div><h3>Results</h3><div>We successfully elicited opinions from fifteen expert doctors to create a model for obtaining EM probability scores from patient data.</div></div><div><h3>Conclusions</h3><div>The elicited probability score and the proposed algorithm can be utilized to make image based deep learning Lyme disease pre-scanners robust. The proposed elicitation and validation process is easy for doctors to follow and can help address related medical diagnosis problems where it is challenging to collect patient data.</div></div>\",\"PeriodicalId\":54950,\"journal\":{\"name\":\"International Journal of Medical Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Medical Informatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1386505624003459\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Medical Informatics","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1386505624003459","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

背景利用深度学习技术诊断莱姆病最常见的早期症状--迁延性红斑(EM)皮损,可以有效预防长期并发症。由于缺乏与莱姆病相关的图像数据集和相关患者数据,现有基于深度学习的 EM 识别工作只能利用皮损图像。医生只能依靠患者提供的皮损背景信息来确诊。为了帮助深度学习模型从患者数据中计算出概率分数,本研究征求了 15 位专家医生的意见。据我们所知,这是首次从患者数据中计算莱姆病概率的专家征询工作。方法在征询过程中,我们准备了一份问卷,其中包含与EM相关的问题和可能的答案。医生对问题的不同答案给出了相对权重。我们使用基于高斯混合物的密度估计法将医生的评价转换为概率分数。我们利用正式概念分析和决策树来验证和解释模型。我们还提出了一种算法,用于合并来自多种模式的独立概率估计值,例如将来自深度学习图像分类器的EM概率分数与来自患者数据的诱导分数合并。结果我们成功地从15位专家医生那里获得了意见,从而创建了一个从患者数据中获得EM概率分数的模型。所提出的诱导和验证过程对医生来说很容易操作,有助于解决收集患者数据具有挑战性的相关医疗诊断问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Expert opinion elicitation for assisting deep learning based Lyme disease classifier with patient data

Background

Diagnosing erythema migrans (EM) skin lesion, the most common early symptom of Lyme disease, using deep learning techniques can be effective to prevent long-term complications. Existing works on deep learning based EM recognition only utilizes lesion image due to the lack of a dataset of Lyme disease related images with associated patient data. Doctors rely on patient information about the background of the skin lesion to confirm their diagnosis. To assist deep learning model with a probability score calculated from patient data, this study elicited opinions from fifteen expert doctors. To the best of our knowledge, this is the first expert elicitation work to calculate Lyme disease probability from patient data.

Methods

For the elicitation process, a questionnaire with questions and possible answers related to EM was prepared. Doctors provided relative weights to different answers to the questions. We converted doctors' evaluations to probability scores using Gaussian mixture based density estimation. We exploited formal concept analysis and decision tree for elicited model validation and explanation. We also proposed an algorithm for combining independent probability estimates from multiple modalities, such as merging the EM probability score from a deep learning image classifier with the elicited score from patient data.

Results

We successfully elicited opinions from fifteen expert doctors to create a model for obtaining EM probability scores from patient data.

Conclusions

The elicited probability score and the proposed algorithm can be utilized to make image based deep learning Lyme disease pre-scanners robust. The proposed elicitation and validation process is easy for doctors to follow and can help address related medical diagnosis problems where it is challenging to collect patient data.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
International Journal of Medical Informatics
International Journal of Medical Informatics 医学-计算机:信息系统
CiteScore
8.90
自引率
4.10%
发文量
217
审稿时长
42 days
期刊介绍: International Journal of Medical Informatics provides an international medium for dissemination of original results and interpretative reviews concerning the field of medical informatics. The Journal emphasizes the evaluation of systems in healthcare settings. The scope of journal covers: Information systems, including national or international registration systems, hospital information systems, departmental and/or physician''s office systems, document handling systems, electronic medical record systems, standardization, systems integration etc.; Computer-aided medical decision support systems using heuristic, algorithmic and/or statistical methods as exemplified in decision theory, protocol development, artificial intelligence, etc. Educational computer based programs pertaining to medical informatics or medicine in general; Organizational, economic, social, clinical impact, ethical and cost-benefit aspects of IT applications in health care.
期刊最新文献
Application of the openEHR reference model for PGHD: A case study on the DH-Convener initiative Tracking provenance in clinical data warehouses for quality management Acute myocardial infarction risk prediction in emergency chest pain patients: An external validation study Healthcare professionals’ cross-organizational access to electronic health records: A scoping review Cross-modal similar clinical case retrieval using a modular model based on contrastive learning and k-nearest neighbor search
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1