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Article(id=1322168033045500133, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322167714660078002, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.02.006, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1709827200000, receivedDateStr=2024-03-08, revisedDate=1767542400000, revisedDateStr=2026-01-05, acceptedDate=null, acceptedDateStr=null, onlineDate=1793159023771, onlineDateStr=2026-10-28, pubDate=1769270400000, pubDateStr=2026-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793159023771, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793159023771, creator=13701087609, updateTime=1793159023771, updator=13701087609, issue=Issue{id=1322167714660078002, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='2', pageStart='177', pageEnd='352', issueExtLink='null', onlineDate='null', pubDate='1769270400000', pubDateStr='2026-01-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793158947863, creator='13701087609', updateTime=1793158947863, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=214, endPage=219, ext={EN=ArticleExt(id=1322168034312179945, articleId=1322168033045500133, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Research on the influencing factors of medical expenses of middle-aged and elderly patients with coronary heart disease complicated with comorbidities based on machine learning, columnId=1220689484526572183, journalTitle=Chinese Nursing Research, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=
Objective

To analyze the influencing factors of medical expenses of middle-aged and elderly patients with coronary heart disease complicated with comorbidities based on machine learning model.

Methods

A total of 3 002 patients with coronary heart disease complicated with complications hospitalized in the 27 public hospitals in a provincial capital city in central China was selected as subjects from January 2022 to January 2023.The patient data were collected.The influencing factors of medical expenses of middle-aged and elderly patients with coronary heart disease complicated with comorbidities were analyzed based on four machine learning models,namely ridge regression,Lasso regression,random forest,and decision tree.

Results

Compared with other models,the random forest model had better fitting effect.The material cost(27.72%) and western medicine cost(19.43%) accounted for relatively high proportion of the patient's medical expenses,while the nursing cost(4.02%) and consultation cost(1.15%) accounted for relatively low proportion.The hospitalization days,the level of medical institution(tertiary hospital),gender(female),age(>50~70 years old), discharge department(cardiology),admission department(cardiology),and common complication(hypertension) were the main influencing factors of medical expenses of middle-aged and elderly patients with coronary heart disease complicated with comorbidities.

Conclusions

Managers should formulate effective measures according to the influencing factors of medical expenses in middle-aged and elderly patients with coronary heart disease with complication to eliminate the waste of medical resources and reduce the burden of middle-aged and elderly patients with coronary heart disease complicated with comorbidities.

, authors=Chengliang WANG, Yaqi YANG, Yu ZHU, Juanjuan YAN, authorsList=Chengliang WANG, Yaqi YANG, Yu ZHU, Juanjuan YAN, authorCompany=null, correspAuthors=Juanjuan YAN, authorNote=null, correspAuthorsNote=
YAN Juanjuan, E-mail:
, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322168039353733359, articleId=1322168033045500133, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=基于机器学习的中老年冠心病共病病人医疗费用影响因素研究, columnId=1220689484681761433, journalTitle=护理研究, columnName=科研论著, runingTitle=null, highlight=null, articleAbstract=
目的

基于机器学习分析中老年冠心病共病病人医疗费用的影响因素。

方法

于2022年1月—2023年1月选取中部某省会城市27所公立医院的3 002例住院冠心病共病病人为研究对象，收集病人数据，基于岭回归、Lasso回归、随机森林、决策树4种机器学习模式分析中老年冠心病共病病人医疗费用的影响因素。

结果

随机森林模型比其他模型拟合效果更优；病人医疗费用构成中，材料费（27.72%）、西药费（19.43%）占比较高，护理费（4.02%）、诊察费（1.15%）占比较低。住院天数、医疗机构等级（三级医院）、性别（女）、年龄（>50~70岁）、出院科室（心内科）、入院科室（心内科）、常见合并症（高血压）是影响中老年冠心病共病病人医疗费用的主要因素。

结论

管理者应根据中老年冠心病共病病人医疗费用影响因素制定有效措施，杜绝医疗资源浪费，减轻中老年冠心病共病病人负担。

, authors=王成亮, 杨雅棋, 朱裕, 闫娟娟*, *, authorsList=王成亮, 杨雅棋, 朱裕, 闫娟娟, authorCompany=null, correspAuthors=闫娟娟, authorNote=

王成亮，硕士研究生在读

, correspAuthorsNote=
闫娟娟，E-mail:
YAN Juanjuan, E-mail:
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articleId=1322168033045500133, doi=null, pmid=null, pmcid=null, year=2017, volume=39, issue=2, pageStart=126, pageEnd=128, url=null, language=null, rfNumber=13, rfOrder=12, authorNames=江仕清, 黄鲜桃, 黄志碧, journalName=右江民族医学院学报, refType=null, unstructuredReference=江仕清,黄鲜桃,黄志碧.广西冠心病患者住院费用及影响因素分析[J].右江民族医学院学报,2017,39(2):126-128., articleTitle=广西冠心病患者住院费用及影响因素分析, refAbstract=null), Reference(id=1322530067293520887, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, doi=null, pmid=null, pmcid=null, year=2020, volume=39, issue=9, pageStart=38, pageEnd=40, url=null, language=null, rfNumber=14, rfOrder=13, authorNames=徐阅, 刘黎明, 满晓玮, journalName=中国卫生经济, refType=null, unstructuredReference=徐阅,刘黎明,满晓玮,等.基于SHA 2011的北京市冠心病治疗费用核算结果与分析[J].中国卫生经济,2020,39(9):38-40., articleTitle=基于SHA 2011的北京市冠心病治疗费用核算结果与分析, refAbstract=null), Reference(id=1322530067364824056, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, doi=null, pmid=null, pmcid=null, year=2019, volume=36, issue=4, pageStart=574, pageEnd=577, url=null, language=null, rfNumber=15, rfOrder=14, authorNames=王子, 徐沛瑶, 朱敏, journalName=中国卫生统计, refType=null, unstructuredReference=王子,徐沛瑶,朱敏.高血压患者住院费用及其影响因素研究[J].中国卫生统计,2019,36(4):574-577., articleTitle=高血压患者住院费用及其影响因素研究, refAbstract=null), Reference(id=1322530067440321529, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, doi=null, pmid=null, pmcid=null, year=2019, volume=38, issue=10, pageStart=69, pageEnd=71, url=null, language=null, rfNumber=16, rfOrder=15, authorNames=李洋, 毛静馥, 黄卫东, journalName=中国卫生经济, refType=null, unstructuredReference=李洋,毛静馥,黄卫东,等.全民医保视角下糖尿病患者住院费用结构及影响因素分析[J].中国卫生经济,2019,38(10):69-71., articleTitle=全民医保视角下糖尿病患者住院费用结构及影响因素分析, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322530059190125491, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, xref=1, ext=[AuthorCompanyExt(id=1322530059198514100, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, companyId=1322530059190125491, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Shanxi University of Chinese Medicine, Shanxi 030619 China), AuthorCompanyExt(id=1322530059206902709, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, companyId=1322530059190125491, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1山西中医药大学，山西 030619)])], figs=[ArticleFig(id=1322530063564784603, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, language=EN, label=Figure 1, caption=Ranking of impact characteristics importance of medical expenses based on random forest, figureFileSmall=j0ndmXKHS+PNTq/eH2Gcig==, figureFileBig=b+h9dcQt9OfZDT9/FKHNUw==, tableContent=null), ArticleFig(id=1322530063648670684, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, language=CN, label=图1, caption=基于随机森林的病人医疗费用影响特征重要度排序, figureFileSmall=j0ndmXKHS+PNTq/eH2Gcig==, figureFileBig=b+h9dcQt9OfZDT9/FKHNUw==, tableContent=null), ArticleFig(id=1322530063732556765, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, language=EN, label=Table 1, caption=

Method of assigning patient characteristic variables

, figureFileSmall=null, figureFileBig=null, tableContent=
特征变量赋值方式
性别男=1；女=2
年龄30~50岁=1；>50~70岁=2；>70~85岁=3；>85岁=4
医疗机构等级一级医院=1；二级医院=2；三级医院=3
入院科室其他=0；心内科=1；老年科=2；肾内科=3；神经内科=4；中医科=5
出院科室其他=0；心内科=1；老年科=2；肾内科=3；神经内科=4；中医科=5
离院方式医嘱离院=1；其他=2；非医嘱离院=3；医嘱转院=4；死亡=5
住院天数原值代入
常见合并症未合并=0；合并=1
), ArticleFig(id=1322530063795471326, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, language=CN, label=表1, caption=

病人特征变量赋值方式

, figureFileSmall=null, figureFileBig=null, tableContent=
特征变量赋值方式
性别男=1；女=2
年龄30~50岁=1；>50~70岁=2；>70~85岁=3；>85岁=4
医疗机构等级一级医院=1；二级医院=2；三级医院=3
入院科室其他=0；心内科=1；老年科=2；肾内科=3；神经内科=4；中医科=5
出院科室其他=0；心内科=1；老年科=2；肾内科=3；神经内科=4；中医科=5
离院方式医嘱离院=1；其他=2；非医嘱离院=3；医嘱转院=4；死亡=5
住院天数原值代入
常见合并症未合并=0；合并=1
), ArticleFig(id=1322530065452221407, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, language=EN, label=Table 2, caption=

Common comorbidities of patients with coronary heart disease with complication in different genders

, figureFileSmall=null, figureFileBig=null, tableContent=
合并症总体（n=3 002）男性（n=1 538）女性（n=1 464）
心房颤动132（4.4）72（4.7）61（4.2）
心脏瓣膜病21（0.7）8（0.5）15（1.0）
高脂血症279（9.3）115（7.5）165（11.3）
糖尿病495（16.5）258（16.8）237（16.2）
中枢神经系统疾病99（3.3）52（3.4）45（3.1）
陈旧性心肌梗死117（3.9）86（5.6）29（2.0）
慢性阻塞性肺疾病48（1.6）34（2.2）13（0.9）
慢性肾功能不全75（2.5）34（2.2）40（2.7）
高血压1 135（37.8）564（36.7）571（39.0）
癌症27（0.9）15（1.0）12（0.8）
睡眠呼吸障碍症54（1.8）26（1.7）28（1.9）
贫血114（3.8）40（2.6）75（5.1）
), ArticleFig(id=1322530065527718880, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, language=CN, label=表2, caption=

不同性别冠心病共病病人常见合并症情况

, figureFileSmall=null, figureFileBig=null, tableContent=
合并症总体（n=3 002）男性（n=1 538）女性（n=1 464）
心房颤动132（4.4）72（4.7）61（4.2）
心脏瓣膜病21（0.7）8（0.5）15（1.0）
高脂血症279（9.3）115（7.5）165（11.3）
糖尿病495（16.5）258（16.8）237（16.2）
中枢神经系统疾病99（3.3）52（3.4）45（3.1）
陈旧性心肌梗死117（3.9）86（5.6）29（2.0）
慢性阻塞性肺疾病48（1.6）34（2.2）13（0.9）
慢性肾功能不全75（2.5）34（2.2）40（2.7）
高血压1 135（37.8）564（36.7）571（39.0）
癌症27（0.9）15（1.0）12（0.8）
睡眠呼吸障碍症54（1.8）26（1.7）28（1.9）
贫血114（3.8）40（2.6）75（5.1）
), ArticleFig(id=1322530065590633441, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, language=EN, label=Table 3, caption=

Average medical expenses of patients with different characteristics

, figureFileSmall=null, figureFileBig=null, tableContent=
项目平均费用合计
[元（%）]		性别（元）年龄（元）
男女30~50岁>50~70岁>70~85岁>85岁
医疗总费用16 818.6（100.00）17 009.5116 627.7114 560.9714 967.2816 537.7821 208.41
中成药费687.5（4.09）640.47734.50458.25666.07734.12891.49
西药费3 268.1（19.43）3 057.163 479.002 147.292 363.353 444.755 116.92
治疗费2 778.3（16.52）3 085.772 470.912 863.652 729.802 498.063 021.85
化验费1 886.4（11.22）1 867.491 905.351 604.871 690.241 929.322 321.24
材料费4 661.8（27.72）4 953.264 370.304 468.524 420.044 427.365 331.20
诊察费192.7（1.15）181.72203.65162.89178.73193.69235.42
检查费2 257.0（13.42）2 207.862 306.102 116.812 158.312 210.222 542.57
床位费410.2（2.44）388.67431.78313.56350.28432.69544.37
护理费676.6（4.02）627.11726.15425.13410.46667.571 203.35
), ArticleFig(id=1322530065716462562, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, language=CN, label=表3, caption=

不同特征病人平均医疗费用情况

, figureFileSmall=null, figureFileBig=null, tableContent=
项目平均费用合计
[元（%）]		性别（元）年龄（元）
男女30~50岁>50~70岁>70~85岁>85岁
医疗总费用16 818.6（100.00）17 009.5116 627.7114 560.9714 967.2816 537.7821 208.41
中成药费687.5（4.09）640.47734.50458.25666.07734.12891.49
西药费3 268.1（19.43）3 057.163 479.002 147.292 363.353 444.755 116.92
治疗费2 778.3（16.52）3 085.772 470.912 863.652 729.802 498.063 021.85
化验费1 886.4（11.22）1 867.491 905.351 604.871 690.241 929.322 321.24
材料费4 661.8（27.72）4 953.264 370.304 468.524 420.044 427.365 331.20
诊察费192.7（1.15）181.72203.65162.89178.73193.69235.42
检查费2 257.0（13.42）2 207.862 306.102 116.812 158.312 210.222 542.57
床位费410.2（2.44）388.67431.78313.56350.28432.69544.37
护理费676.6（4.02）627.11726.15425.13410.46667.571 203.35
), ArticleFig(id=1322530065829708771, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, language=EN, label=Table 4, caption=

Performance comparison of 4 machine learning models

, figureFileSmall=null, figureFileBig=null, tableContent=
模型MSERMSRR2
岭回归0.424 6320.651 6380.575 37
Lasso回归0.425 0330.651 9450.574 96
随机森林0.075 4320.205 9670.957 57
决策树0.077 8710.279 0530.922 12
), ArticleFig(id=1322530065930372068, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, language=CN, label=表4, caption=

4种机器学习模型效能比较

, figureFileSmall=null, figureFileBig=null, tableContent=
模型MSERMSRR2
岭回归0.424 6320.651 6380.575 37
Lasso回归0.425 0330.651 9450.574 96
随机森林0.075 4320.205 9670.957 57
决策树0.077 8710.279 0530.922 12
), ArticleFig(id=1322530066005869541, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, language=EN, label=Table 5, caption=

Prediction efficiency of each project expenses of machine learning models(R2 value)

, figureFileSmall=null, figureFileBig=null, tableContent=
费用岭回归Lasso回归随机森林决策树
中成药费0.249 80.249 70.908 20.732 4
西药费0.651 10.650 90.958 30.912 0
治疗费0.999 90.999 90.999 90.999 9
化验费0.989 40.648 20.978 40.956 4
材料费0.999 90.999 90.999 90.999 9
诊察费0.739 70.739 30.964 20.938 0
检查费0.536 30.535 90.942 20.777 0
床位费0.509 50.509 10.925 20.877 7
护理费0.559 50.559 20.940 10.915 4
), ArticleFig(id=1322530066089755622, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, language=CN, label=表5, caption=

机器学习模型各项费用预测效能（R2值）

, figureFileSmall=null, figureFileBig=null, tableContent=
费用岭回归Lasso回归随机森林决策树
中成药费0.249 80.249 70.908 20.732 4
西药费0.651 10.650 90.958 30.912 0
治疗费0.999 90.999 90.999 90.999 9
化验费0.989 40.648 20.978 40.956 4
材料费0.999 90.999 90.999 90.999 9
诊察费0.739 70.739 30.964 20.938 0
检查费0.536 30.535 90.942 20.777 0
床位费0.509 50.509 10.925 20.877 7
护理费0.559 50.559 20.940 10.915 4
), ArticleFig(id=1322530066169447399, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, language=EN, label=Table 6, caption=

Ranking of impact characteristics importance of each item expenses（importance）

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费用第1位第2位第3位第4位第5位
中成药费住院天数(0.319）医疗机构等级（二级医院，0.119）常见合并症(高脂血症,0.060）性别（女，0.043）常见合并症（高血压,0.043）
西药费住院天数(0.461）常见合并症（高血压,0.063）年龄（>50~70岁，0.063）出院科室（心内科，0.046）医疗机构等级（三级医院，0.038）
治疗费年龄（>50~70岁，0.233）住院天数(0.219）常见合并症（高血压,0.195）年龄（>85岁，0.085）医疗机构等级（三级医院，0.047）
化验费住院天数(0.408）入院科室（肾内科，0.069）年龄（>50~70岁，0.067）医疗机构等级（三级医院，0.066）性别（女，0.053）
材料费住院天数(0.210）常见合并症（高血压,0.189）年龄（>85岁，0.187）年龄（>50~70岁，0.174）医疗机构等级（三级医院，0.047）
诊察费住院天数(0.771）医疗机构等级（三级医院，0.053）常见合并症（高血压,0.029）性别（女，0.015）年龄（>85岁，0.011）
检查费住院天数(0.429）医疗机构等级（三级医院，0.080）性别（女，0.054）年龄（>50~70岁，0.049）常见合并症（高血压,0.042）
床位费住院天数(0.429）离院方式（死亡，0.068）性别（女，0.055）年龄（>50~70岁，0.051）常见合并症（糖尿病，0.037）
护理费住院天数(0.406）性别（女，0.088）常见合并症（高血压,0.048）入院科室（心内科，0.048）出院科室（心内科，0.048）
), ArticleFig(id=1322530066253333480, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168033045500133, language=CN, label=表6, caption=

各项费用的影响特征重要度排名（重要度）

, figureFileSmall=null, figureFileBig=null, tableContent=
费用第1位第2位第3位第4位第5位
中成药费住院天数(0.319）医疗机构等级（二级医院，0.119）常见合并症(高脂血症,0.060）性别（女，0.043）常见合并症（高血压,0.043）
西药费住院天数(0.461）常见合并症（高血压,0.063）年龄（>50~70岁，0.063）出院科室（心内科，0.046）医疗机构等级（三级医院，0.038）
治疗费年龄（>50~70岁，0.233）住院天数(0.219）常见合并症（高血压,0.195）年龄（>85岁，0.085）医疗机构等级（三级医院，0.047）
化验费住院天数(0.408）入院科室（肾内科，0.069）年龄（>50~70岁，0.067）医疗机构等级（三级医院，0.066）性别（女，0.053）
材料费住院天数(0.210）常见合并症（高血压,0.189）年龄（>85岁，0.187）年龄（>50~70岁，0.174）医疗机构等级（三级医院，0.047）
诊察费住院天数(0.771）医疗机构等级（三级医院，0.053）常见合并症（高血压,0.029）性别（女，0.015）年龄（>85岁，0.011）
检查费住院天数(0.429）医疗机构等级（三级医院，0.080）性别（女，0.054）年龄（>50~70岁，0.049）常见合并症（高血压,0.042）
床位费住院天数(0.429）离院方式（死亡，0.068）性别（女，0.055）年龄（>50~70岁，0.051）常见合并症（糖尿病，0.037）
护理费住院天数(0.406）性别（女，0.088）常见合并症（高血压,0.048）入院科室（心内科，0.048）出院科室（心内科，0.048）
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基于机器学习的中老年冠心病共病病人医疗费用影响因素研究
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王成亮 , 杨雅棋 , 朱裕 , 闫娟娟 *, *
护理研究 | 科研论著 2026,40(2): 214-219
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护理研究 |科研论著 2026 , 40 (2) : 214 -219
基于机器学习的中老年冠心病共病病人医疗费用影响因素研究
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王成亮, 杨雅棋, 朱裕, 闫娟娟*, *
作者信息
  • 1山西中医药大学，山西 030619
通讯作者:
闫娟娟，E-mail:
YAN Juanjuan, E-mail:
作者简介:

王成亮，硕士研究生在读

Research on the influencing factors of medical expenses of middle-aged and elderly patients with coronary heart disease complicated with comorbidities based on machine learning
Chengliang WANG, Yaqi YANG, Yu ZHU, Juanjuan YAN
Affiliations
  • 1Shanxi University of Chinese Medicine, Shanxi 030619 China
    • Corresponding Author:
    YAN Juanjuan, E-mail:
出版时间: 2026-01-25 doi: 10.12102/j.issn.1009-6493.2026.02.006
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摘要
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目的

基于机器学习分析中老年冠心病共病病人医疗费用的影响因素。

方法

于2022年1月—2023年1月选取中部某省会城市27所公立医院的3 002例住院冠心病共病病人为研究对象，收集病人数据，基于岭回归、Lasso回归、随机森林、决策树4种机器学习模式分析中老年冠心病共病病人医疗费用的影响因素。

结果

随机森林模型比其他模型拟合效果更优；病人医疗费用构成中，材料费（27.72%）、西药费（19.43%）占比较高，护理费（4.02%）、诊察费（1.15%）占比较低。住院天数、医疗机构等级（三级医院）、性别（女）、年龄（>50~70岁）、出院科室（心内科）、入院科室（心内科）、常见合并症（高血压）是影响中老年冠心病共病病人医疗费用的主要因素。

结论

管理者应根据中老年冠心病共病病人医疗费用影响因素制定有效措施，杜绝医疗资源浪费，减轻中老年冠心病共病病人负担。

机器学习  /  中老年  /  冠心病  /  共病  /  医疗费用  /  影响因素  /  护理管理
Objective

To analyze the influencing factors of medical expenses of middle-aged and elderly patients with coronary heart disease complicated with comorbidities based on machine learning model.

Methods

A total of 3 002 patients with coronary heart disease complicated with complications hospitalized in the 27 public hospitals in a provincial capital city in central China was selected as subjects from January 2022 to January 2023.The patient data were collected.The influencing factors of medical expenses of middle-aged and elderly patients with coronary heart disease complicated with comorbidities were analyzed based on four machine learning models,namely ridge regression,Lasso regression,random forest,and decision tree.

Results

Compared with other models,the random forest model had better fitting effect.The material cost(27.72%) and western medicine cost(19.43%) accounted for relatively high proportion of the patient's medical expenses,while the nursing cost(4.02%) and consultation cost(1.15%) accounted for relatively low proportion.The hospitalization days,the level of medical institution(tertiary hospital),gender(female),age(>50~70 years old), discharge department(cardiology),admission department(cardiology),and common complication(hypertension) were the main influencing factors of medical expenses of middle-aged and elderly patients with coronary heart disease complicated with comorbidities.

Conclusions

Managers should formulate effective measures according to the influencing factors of medical expenses in middle-aged and elderly patients with coronary heart disease with complication to eliminate the waste of medical resources and reduce the burden of middle-aged and elderly patients with coronary heart disease complicated with comorbidities.

machine learning  /  middle-aged and elderly  /  coronary heart disease  /  comorbidity  /  medical expenses  /  influencing factors  /  nursing management
王成亮, 杨雅棋, 朱裕, 闫娟娟. 基于机器学习的中老年冠心病共病病人医疗费用影响因素研究. 护理研究, 2026 , 40 (2) : 214 -219 . DOI: 10.12102/j.issn.1009-6493.2026.02.006
Chengliang WANG, Yaqi YANG, Yu ZHU, Juanjuan YAN. Research on the influencing factors of medical expenses of middle-aged and elderly patients with coronary heart disease complicated with comorbidities based on machine learning[J]. Chinese Nursing Research, 2026 , 40 (2) : 214 -219 . DOI: 10.12102/j.issn.1009-6493.2026.02.006
正文
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冠心病是一种常见的慢性非传染性疾病，其发病率逐年增高[1]。冠心病病人常伴随糖尿病、高血压、心房颤动等[2]慢性并发症，不仅对病人身心造成严重损害，还增加了病人及其家庭的经济负担。探究冠心病共病模式下的医疗费用影响因素意义重大。近年来，国内外学者开始将机器学习算法应用于医疗费用研究[3-4]，与传统线性回归相比，机器学习算法对数据要求较低，且能够更精准地对数据进行分析[1]。本研究使用4种机器学习算法对冠心病共病病人费用进行预测建模并计算影响病人医疗费用的特征重要度，以期为控制冠心病病人的医疗费用、减轻病人疾病经济负担提供建议。
1　对象与方法
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1.1　研究对象
于2022年1月—2023年1月选取中部某省会城市27所公立医院的3 002例住院冠心病病人为研究对象。纳入标准：1）主疾病诊断为冠心病［国际疾病分类第10次修订版（ICD-10）编码I01、I02、I05-I09、I11、I13、I20-I25、I26、I27、I30-I52］；2）年龄≥30岁；3）合并冠心病以外的其他疾病。排除标准：1）性别、主疾病等关键信息缺失；2）病历系统费用字段不符合逻辑、缺失超过1/3。
1.2　数据收集方法
包括人口统计信息（性别等）、临床信息（入院科室、出院科室、离院方式、住院天数等）、诊断信息以及相关医疗费用信息（西药费、材料费、护理费等）。
1.3　数据处理
费用信息缺失不超过1/3时采用随机森林算法填补数据。使用One-hot编码对离散变量进行转化，继而对特征因素进行归一化，使其收敛于[0，1]。
1.4　合并症归类
基于病人病例资料，依据国际疾病分类第10次修订版确定疾病编码，本研究所纳入的冠心病常见合并症包括12种，即心房颤动、心脏瓣膜病、高脂血症、糖尿病、中枢神经系统疾病、陈旧性心肌梗死、慢性阻塞性肺疾病、慢性肾功能不全、高血压、癌症、贫血、睡眠呼吸障碍症[5]。
1.5　特征变量赋值
创建哑变量处理分类型的信息特征字段，见表1。
1.6　研究方法
将样本量以7∶3比例分为训练集和测试集，以住院总费用、中成药费、西药费、材料费、化验费、诊察费、检查费、治疗费、护理费、床位费分别作为结局变量，以病人性别、年龄、入院科别、出院科别、住院天数、离院方式、常见的12个合并症等18个特征变量为自变量分别构建岭回归、Lasso回归、随机森林、决策树4种机器学习模型。使用5折交叉验证法对模型调参，使用均方误差（MSE）、均方根误差（RMSE）、决定系数（R2）评价模型预测效能，筛选最佳预测模型并计算影响各项费用的特征重要度。1）岭回归：岭回归是一种专用于共线性数据分析的有偏估计回归方法，实质上是一种改良的最小二乘估计法，对不适定问题进行回归分析时最常使用的一种正则化方法。2）Lasso回归：Lasso回归是一种常见的线性回归方法，其特点是在拟合广义线性模型的同时进行变量筛选和复杂度调整。无论因变量是连续还是离散，均可用Lasso回归建模预测。3）随机森林：随机森林是一种集成学习方法，是通过建立多个决策树并将他们融合起来得到一个更加准确和稳定的模型，其特点是不用降维也能处理高维数据，且自带特征重要度。4）决策树：决策树是一种简单易用的非参数分类器。它不需要对数据有任何的先验假设，计算速度较快，结果容易解释，且稳健性强。
1.7　统计学方法
采用Stata 17.0对数据进行清洗与赋值；采SPSS 27.0进行统计描述，定性资料以频数及百分比（%）表示；采用Jupyter notebook实现机器学习相关算法编写与运行；采用Sklearn库进行数据分析与处理；机器学习算法模型均通过Python语言实现。
2　结果
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2.1　病人基本特征和费用构成情况
3 002例病人中，男1 538例（51.23%），女1 464例（48.77%）；30~50岁288例（9.6%），>50~70岁1 507例（50.2%），>70~85岁949例（31.6%），>85岁258例（8.6%）；一级医院32例（1.1%），二级医院121例（4.0%），三级医院2 849例（94.9%）；入院科室为心内科2 384例（79.4%）；出院科室为心内科2 417例（80.5%）；通过医嘱离院方式离院2 837 例（94.5%），除医嘱离院外其他方式离院165例（5.5%）；住院天数为1~56 d，平均住院天数为9 d。
2.2　冠心病共病病人常见合并症
冠心病共病病人常见合并症中，占比最高的为高血压（37.8%），其次为糖尿病（16.5%）、高脂血症（9.3%）。不同性别冠心病共病病人常见合并症情况见表2。
2.3　冠心病共病病人医疗费用
病人平均医疗费用为16 818.6元，费用占比最高的是材料费（27.72%），其次是西药费（19.43%）、治疗费（16.52%）、检查费（13.42%）。不同特征病人医疗费用情况见表3。
2.4　医疗总费用的机器学习模型拟合结果
建立医疗总费用的4种机器学习预测模型，使用MSE、RMSE、R2评价模型拟合效能，MSE、RMSE越小，R2越大，模型拟合效能越好，结果显示，4种模型中随机森林模型预测效能最高，见表4。
2.5　各项费用的机器学习模型拟合情况
基于中成药费、西药费、治疗费、化验费、材料费、诊察费、检查费、床位费、护理费构建4种机器学习预测模型，结果所示，随机森林模型在大部分费用中的预测效能优于其他3种模型。见表5。
2.6　基于随机森林的病人医疗费用影响特征重要度
使用预测效能较优的随机森林模型计算影响病人医疗总费用特征的重要度，结果显示，随机森林模型中影响病人医疗总费用特征的重要度依次为：住院天数（0.414）、医疗机构等级（三级医院，0.097）、性别（女，0.052）、年龄（>50~70岁，0.048）、出院科室（心内科，0.045）、入院科室（心内科，0.043）、常见合并症（高血压，0.038），见图1。
使用随机森林模型计算各项费用特征的重要度，结果显示，除治疗费外，其他单项费用的特征重要度均为住院天数排第1位，可见住院天数为影响病人费用的重要因素。重要度排名前5位的特征中，除住院天数外，常见合并症（高血压）、年龄（>50~70岁） 、性别（女) 、医疗机构等级（三级医院)出现的频率较高，各项费用的影响特征重要度排名见表6。
3　讨论
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3.1　不同项目医疗费用占比不同
本研究结果显示，中老年冠心病病人住院费用构成中，材料费（27.72%）、西药费（19.43%）占比较高，与黄果等[6-7]研究结果相似，原因可能为：1）冠心病的治疗手段越来越先进，如经皮冠状动脉介入治疗（PCI）、纳米技术的应用[8]，在提高治疗效果的同时也增加了病人治疗费用；2）冠心病病人常存在并发症，如高血压、糖尿病等，需要进行药物治疗或者辅助治疗，使得相关费用增加。本研究结果显示，中老年冠心病病人住院费用构成中，诊察费（1.15%）、护理费（4.02%）占比较低，说明医护人员的劳动价值并未得到足够尊重，可能导致存在追逐利润[9]以及过度医疗的情况[10]。提示医疗机构在病人治疗过程中应完善监督机制，动态调整医疗服务价格，加强中老年群体慢性病的防治，同时杜绝医疗资源浪费、避免医护人员不当行为的发生。
3.2　不同人群医疗费用构成存在差异
本研究结果表明，男性和年龄30~50岁的病人治疗费高于西药费。治疗费往往与疾病状态有关，男性冠心病病人的严重合并症往往较多，所需治疗可能较复杂，产生的费用也较高。提示病人应关注自身病情，及时就诊，避免病情加重。
3.3　随机森林模型预测效能高
建立医疗总费用的4种机器学习预测模型，比较后发现，随机森林模型MSE为0.075 432、RMSE为0.205 967，低于其他3种模型；R2为0.957 57，高于其他3种模型，提示4种模型中随机森林模型预测效能最高。基于中成药费、西药费、治疗费、化验费、材料费、诊察费、检查费、床位费、护理费构建4种机器学习预测模型，结果显示，随机森林模型除化验费R2值排名第2位、治疗费和材料费R2值与其他3种模型相等外，其余项目R2值均高于其他模型，说明随机森林模型总体来看预测效能优于其他3种模型，结果较稳定。
3.4　住院天数、医疗机构等级、性别、年龄对病人医疗费用的影响较大
使用预测效能最优的随机森林模型计算影响病人医疗总费用的特征重要度，结果显示，住院天数、医疗机构等级、性别、年龄对病人医疗费用的影响较大，其中，住院天数是影响中老年病人医疗费用的决定性因素，与大多数研究结果[1,3,11-12]一致。病人住院时间较长，所耗费的材料费、药品费等医疗卫生资源增多，会导致病人医疗费用增加。三级医院病人医疗费用往往高于一级、二级医院[13]，三级医院作为我国最高级别的综合医院，其就诊病人往往病情严重，且病人接受的治疗手段、使用的设备相对先进，治疗过程中可能消耗更多的医疗资源，导致病人医疗费用增加。与男性相比，女性病人医疗费用较高，与黄果等[6]的研究不一致，可能与女性病人体质总体弱于男性导致其住院时间以及术后恢复时间相对较长、使用的药物周期较长等原因有关。>50~70岁的病人费用较高[14]，本研究中，冠心病病人以>50~70岁的病人居多，占比为50.2%，提示应将该年龄阶段的中老年病人作为控制费用重点人群。
3.5　病人常见合并症中，高血压占比较高，对病人医疗费用影响较大
本研究结果显示，冠心病病人常见合并症中，占比最高的为高血压（37.8%）；影响各项费用特征的重要度排名前5位的特征中，除住院天数外，高血压出现的频率较高。高血压病人需长期服药[15]，药物费用是影响病人住院费用的重要因素之一，长时间服用药物可导致病人医疗费用增加，同时高血压也可造成人体多个器官组织功能损害，导致其他并发症发病率升高，医疗费用进一步增高[16]。

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doi: 10.12102/j.issn.1009-6493.2026.02.006
  • 接收时间：2024-03-08
  • 首发时间：2026-10-28
  • 出版时间：2026-01-25
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  • 收稿日期：2024-03-08
  • 修回日期：2026-01-05
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    1山西中医药大学，山西 030619

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闫娟娟，E-mail:
YAN Juanjuan, E-mail:
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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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