| 基于多模态数据挖掘的网络医生答复采纳预测研究 |
| 修订日期：2023-09-11 点此下载全文 |
| 引用本文：邓伟伟,余天炜,陈寒,等.基于多模态数据挖掘的网络医生答复采纳预测研究[J].医学信息学杂志,2024,45(2):44-51 |
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| 中文摘要:目的/意义 通过多模态数据分析方法对网络问诊平台中的医患问答相关数据进行挖掘,并基于挖掘结果预测患者是否采纳网络医生对其问题的答复。方法/过程 基于网络问诊平台爬取的医患问答相关数值和类别数据、文本和图像数据,根据疾病类型构建急性疾病、慢性疾病和混合疾病3个数据集；分别采用归一化、独热编码、Med-BERT和卷积神经网络处理数值、类别、文本和图像数据；利用集成学习梯度提升树自动预测患者是否采纳网络医生答复。结果/结论 医生头像信息可改善网络医生答复采纳预测效果,且通过多模态数据挖掘可有效预测网络医生答复采纳情况。 |
| 中文关键词:互联网医疗 答复采纳预测 多模态数据挖掘 机器学习 |
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| Study on Online Doctor Response Adoption Prediction Based on Multimodal Data Mining |
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| Abstract:Significance To use multimodal data analysis method to mine medical Q&A data in online healthcare platforms and predict whether patients will adopt online doctors’ responses. Method/Process First, numerical, categorical, textual, and visual data related to doctor-patient Q&A are obtained from online healthcare platforms, and three datasets of acute disease, chronic disease and mixed disease are constructed according to disease types. Then, normalization, one-hot encoding, Med-BERT, and convolutional neural network are used respectively to process numerical, categorical, textual, and visual data. Finally, a gradient boosting decision tree is used to predict whether patients will adopt online doctors’ responses. Result/Conclusion Doctors’ profile pictures can improve the prediction effect of online doctor response adoption, and multimodal data mining can effectively predict the response adoption. |
| keywords:online healthcare response adoption prediction multimodal data mining machine learning |
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