Individual Medical Costs Prediction Methods Based on Clinical Notes and DRGs

IF 2.3 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE journal of radio frequency identification Pub Date : 2024-04-23 DOI:10.1109/JRFID.2024.3392682
Chai Yang;Xiaoxuan Hu;Qingli Zhu;Qiang Tu;Hongyang Geng;Jing Xu;Zhenfeng Liu;Yanjun Wang;Jing Wang
{"title":"Individual Medical Costs Prediction Methods Based on Clinical Notes and DRGs","authors":"Chai Yang;Xiaoxuan Hu;Qingli Zhu;Qiang Tu;Hongyang Geng;Jing Xu;Zhenfeng Liu;Yanjun Wang;Jing Wang","doi":"10.1109/JRFID.2024.3392682","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE journal of radio frequency identification","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10507203/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

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.
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
基于临床笔记和 DRGs 的个人医疗费用预测方法
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。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
5.70
自引率
0.00%
发文量
0
期刊最新文献
Robust Low-Cost Drone Detection and Classification Using Convolutional Neural Networks in Low SNR Environments Overview of RFID Applications Utilizing Neural Networks A 920-MHz, 160-μW, 25-dB Gain Negative Resistance Reflection Amplifier for BPSK Modulation RFID Tag A Fully-Passive Frequency Diverse Lens-Enabled mmID for Precise Ranging and 2-Axis Orientation Detection in Next-Generation IoT and Cyberphysical Systems A Compact Slot-Based Bi-Directional UHF RFID Reader Antenna for Far-Field Applications
×
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