Sk Imran Hossain , Jocelyn de Goër de Herve , David Abrial , Richard Emilion , Isabelle Lebert , Yann Frendo , Delphine Martineau , Olivier Lesens , Engelbert Mephu Nguifo
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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. 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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。
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.
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.