基于临床笔记和 DRGs 的个人医疗费用预测方法

IF 2.3 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Chai Yang;Xiaoxuan Hu;Qingli Zhu;Qiang Tu;Hongyang Geng;Jing Xu;Zhenfeng Liu;Yanjun Wang;Jing Wang
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引用次数: 0

摘要

个人医疗成本预测是指估算患者医疗相关费用的过程。有效的医疗成本预测有助于医疗机构的预算编制、资源分配和财务规划,是医疗机构和患者的重要工具。本研究介绍了一种利用临床笔记和诊断相关组(DRGs)预测医用耗材成本的先进方法。该方法采用变压器双向编码器表示法(BERT)进行文本矢量化,利用光梯度提升机(LightGBM)增强 DRGs 内的疾病诊断和手术预测,并采用随机森林回归法进行准确的医疗费用预测。它在预测疾病诊断和手术程序方面的准确率超过 91%,平均绝对误差 (MAE) 为 2281.20,R 平方值为 0.8557。这些指标表明,该模型具有很高的准确性和可靠性,展示了其在医疗保健环境中预测医疗成本的功效。该方法通过将语义信息与机器学习算法相结合,改善了医院资源管理和成本估算。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Individual Medical Costs Prediction Methods Based on Clinical Notes and DRGs
Individual medical costs prediction refers to the process of estimating the expenses associated with a patient’s medical care. Effective medical costs prediction helps in budgeting, resource allocation, and financial planning in healthcare settings, making it a crucial tool for both healthcare providers and patients. This study introduces an advanced method for predicting medical consumables costs, leveraging clinical notes and diagnosis related groups (DRGs). The approach employs Bidirectional Encoder Representations from Transformers (BERT) for text vectorization to enhance disease diagnosis and surgical procedure prediction within DRGs using Light Gradient Boosting Machine (LightGBM), and Random Forest Regression for accurate medical costs prediction. It achieves over 91% accuracy in predicting disease diagnosis and surgical procedures, and a Mean Absolute Error (MAE) of 2281.20 and an R-squared value of 0.8557. These metrics indicate a high level of accuracy and reliability, showcasing the model’s efficacy in predicting medical costs in a healthcare setting. This method improves hospital resource management and costs estimation by integrating semantic information with machine learning algorithms.
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CiteScore
5.70
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