| 基于多层感知器神经网络的住院病案内涵质量预测模型研究 |
| 修订日期：2023-05-31 点此下载全文 |
| 引用本文：袁筱祺,赵英英.基于多层感知器神经网络的住院病案内涵质量预测模型研究[J].医学信息学杂志,2023,44(11):35-40 |
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| 基金项目:上海申康医院发展中心医疗质量安全与医疗服务模式创新项目(项目编号:SHDC12022622)；上海市第一人民医院管理创新研究项目(项目编号:YNGL-2023-21)。 |
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| 中文摘要:目的/意义 探讨影响住院病案内涵质量的重要因素,为病案内涵质控提供预测模型,提高住院病案内涵质量。方法/过程 收集2022年6—11月上海市第一人民医院经质控督查的住院病案590份,单因素分析初筛影响因素,构建住院病案内涵质量的多层感知器神经网络预测模型。结果/结论 预测模型的AUC为0.940,95%CI为0.928~0.951,灵敏度为93.73%,特异度为78.22%。影响病案评为甲级的独立因素集中在手术安全核查表、首次主任查房分析、手术护理记录单等方面。多层感知器神经网络内涵质量预测模型的预测效能较好,能够为住院病案内涵质量管理提供理论参考。 |
| 中文关键词:神经网络 住院病案 内涵质量 预测模型 人工智能 |
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| Study on the Connotation Quality Prediction Model of Inpatient Medical Record Based on Multi-layer Perceptron Neural Network |
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| Abstract:Purpose/Significance To explore the important factors affecting the connotation quality of inpatient medical records, and to provide model prediction and improve the connotation quality of inpatient medical records. Method/Process A total of 590 inpatient medical records monitored by quality control in Shanghai First People’s Hospital from June to November 2022 are collected. The influencing factors are initially screened by single factor analysis, and a multi-layer perceptron neural network prediction model for the connotation quality of inpatient medical records is constructed. Result/Conclusion The area under the curve (AUC) of the prediction model is 0.940,95% CI is 0.928~0.951, the sensitivity is 93.73%, and the specificity is 78.22%. The top three independent factors affecting the rating of a case as grade A are concentrated in the surgical safety checklist, the analysis of the first director’s ward round, and the surgical nursing record. The multi-layer perceptron neural network connotation quality prediction model has good prediction efficiency, which provides theoretical references for the connotation quality management of inpatient medical records. |
| keywords:neural network inpatient medical record connotation quality prediction model artificial intelligence |
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