CN116646074A - 基于逻辑回归的脓毒症心力衰竭早期预测系统 - Google Patents
基于逻辑回归的脓毒症心力衰竭早期预测系统 Download PDFInfo
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Abstract
本发明公开了一种基于逻辑回归的脓毒症心力衰竭早期预测系统,涉及人工智能领域与医学诊断技术领域:主要组成部分包括数据存储及采集部分、数据处理部分、机器学习模型预测部分以及脓毒症心力衰竭预测结果显示共四个部分。本发明具有的优点包括:能实现高效、快速、精准、早期预测脓毒症心力衰竭发生的概率;弥补了当前国内早期预测脓毒症心力衰竭的技术空缺。本发明能通过输入脓毒症患者的临床生理监测指标数据,预测该患者发生心力衰竭的概率,从而及时提醒医生进行早期介入,改善患者预后。本发明适用于医院重症监护室的床旁监护及远程医疗预警。
Description
技术领域
本发明专利属于人工智能领域与医学诊断技术领域。
背景技术
脓毒症是由感染引起的宿主反应失调导致的器官功能障碍,是重症监护室中患者死亡的主要原因之一,并且随着年龄增长,脓毒症发病率和病死率会不断上升。心力衰竭是指各种心脏结构或功能性疾病导致心室充盈和收缩能力受损,临床上往往表现为呼吸困难、体力活动受限和液体潴留等。脓毒症极易并发多器官功能障碍综合征,而心脏作为维持机体器官循环的重要器官,很容易被波及。尤其对于老年人,由于高龄、合并多种基础疾病及器官功能减退等因素,心脏受累后易发生心力衰竭,加重病情。脓毒症心功能障碍是脓毒症最严重的并发症之一,通常表现为心肌收缩力下降、心室扩张以及对液体复苏等药物治疗效果不佳,它会直接影响患者预后的效果,甚至造成死亡。脓毒症心功能障碍患者身上更加容易出现循环障碍、心力衰竭和心律失常等,和短期住院死亡率密切相关,会导致脓毒症死亡率高达50%~90%。
大量临床研究证实,脓毒症极易诱发心力衰竭,会显著提高患者死亡率,且其临床表现复杂多样,病情变化较快,因此早期识别并及时介入,预防心衰的发生对改善脓毒症患者的病情和预后非常关键,探寻一种行之有效的治疗方法是临床上研究的重点及难点。由于脓毒症导致心功能障碍的发病机理十分复杂,涉及感染微生物和宿主之间的多个方面的相互作用,因此目前对于脓毒症心力衰竭的治疗尚无循证推荐意见。而脓毒症和心力衰竭的治疗往往可能会存在冲突,增加了治疗的难度,临床上仍然以治疗原发疾病脓毒症为主,在此基础上进行容量管理、维持组织灌注以及强心等,对患者的病情及心功能改善有一定的作用。尽管医疗水平的不断提高,脓毒症心力衰竭患者的再住院率和病死率仍然较高,整体愈后仍差,仍需对其进行进一步研究,以期减少病死率,改善患者预后。
所以我们希望通过对脓毒症患者心力衰竭的出现和恶化进行早期预测,提醒医生及时阻止脓毒症患者心衰的发生,以确保全身器官的充足灌注,改善患者预后。与传统的脓毒症心力衰竭诊断方法相比,使用机器学习算法能够更高效、更准确地实现早期预测,同时能带来可观的社会效益和经济效益。早期诊断并采取有效的防治措施可以改善预后、有效降低脓毒症病死率。
发明内容
本发明专利的目的在于提供一种基于逻辑回归的脓毒症心力衰竭早期预测系统,以解决上述背景技术中所提出的现有的早期预测方法存在的不足。本发明专利所用的数据采用脓毒症患者入院的实验室检查数据,通过这些早期获得的临床检测数据进行预测,对早期诊断更加有意义。
为了实现上述目的,本发明专利提供如下技术方案:一种基于逻辑回归的脓毒症心力衰竭早期预测系统,包括数据采集及存储部分、所述数据预处理部分、所述机器学习模型预测部分、所述脓毒症心力衰竭预测结果显示部分,包括如下步骤:
步骤1,对脓毒症患者的生理指标进行数据预处理,将预处理后的生理指标输入预测模型,所述预测模型为逻辑回归算法;
步骤2,所述预测模型计算后输出所述脓毒症患者发生心力衰竭的概率。
优选的,数据选取部分,包括如下步骤:步骤1,患者筛选;步骤2,生理数据提取。
优选的,数据处理部分,包括数据清洗和数据标准化。
优选的,逻辑回归模型预测部分,设置参数为:
①惩罚项penalty:l2。用于指定惩罚项中使用的规范,即对参数的约束,加约束的情况下,理论上应该可以获得泛化能力更强的结果。L2假设的模型参数满足高斯分布,使得模型更不会过拟合。
②对偶或原始方法dual:False。当样本数量>样本特征的时候,dual通常设置为False。
③停止求解的标准tol:1e-4。即求解到多少位小数的时候停止,此时认为已经求出最优解。
④正则化系数λ的倒数c:0.01。数值越小表示具有越强的正则化。
⑤是否存在截距或偏差fit_intercept:True(存在偏差)。
⑥用于标示分类模型中各种类型的权重class_weight:balanced。类库会根据训练样本量来计算权重。某种类型样本量越多,则权重越低,样本量越少,则权重越高。类权重计算方法为:样本数/(类别数量*将输出每个类的样本数)。
优选的,脓毒症心力衰竭预测结果显示部分,显示脓毒症患者发生脓毒症心力衰竭的概率(输出一个0~1之间的值,约接近于0表明发生脓毒症心力衰竭的概率越小,约接近于1表明发生脓毒症心力衰竭的概率越大)。
在上述技术方案中,本发明专利提供的一种基于逻辑回归的脓毒症心力衰竭早期预测系统,具有以下有益效果:
1、该发明专利,能快速获得对脓毒症心力衰竭的预测结果。
2、该发明专利,预测脓毒症心力衰竭的准确率较高,AUC能达到0.949,正确率(Accuracy)能达到0.929,召回率能达到0.928。
3、该发明专利,能实现提前脓毒症心力衰竭的早期预测。
4、该发明专利,通过重要特征排序,发现对脓毒症心力衰竭预测最关键的6个生理指标,分别为动脉二氧化碳分压、纤维蛋白原、血小板、年龄、动脉收缩压、血糖。
附图说明
图1为本发明专利实施例提供的系统结构组成。
图2为本发明专利实施例提供的系统整体流程示意图。
图3为本发明专利实施例提供的患者筛选流程示意图。
具体实施方式
以下通过实施例形式的具体实施方式,对本发明的上述内容再作进一步的详细说明。但不应将此理解为本发明上述主题的范围仅限于以下的实例。凡基于本发明上述内容所实现的技术均属于本发明的范围。需要特别说明的是,实施例中未具体说明的数据采集、传输、储存和处理等步骤的算法均可通过现有技术已公开的内容实现。
基于逻辑回归的脓毒症心力衰竭早期预测系统主要组成部分,包括数据采集及存储部分、数据处理部分、机器学习模型预测部分及脓毒症心力衰竭预测结果显示部分。通过输入发明内容中数据采集及存储部分提及的24个生理指标的数据,即可得到脓毒症心力衰竭发生的概率(取值范围介于0~1之间)。
一种基于逻辑回归的脓毒症心力衰竭早期预测系统,其特征在于,所述数据采集及存储部分、所述数据预处理部分、所述机器学习模型预测部分、所述脓毒症心力衰竭预测结果显示部分,包括如下步骤:步骤1,对脓毒症患者的生理指标进行数据预处理,将预处理后的生理指标输入预测模型,所述预测模型为逻辑回归算法;步骤2,所述预测模型计算后输出所述脓毒症患者发生心力衰竭的概率。
1.研究对象
将MIMICIII数据库中ICU患者的临床资料,根据下述纳入标准及排除标准,筛选出符合条件的脓毒症心力衰竭患者。
(1)纳入标准:①年龄≥18岁;②符合疾病和相关健康问题的国际统计分类(ICD-9)的脓毒症、败血症及其子分类的诊断标准,得到疾病名称为脓毒症、严重脓毒症、沙门菌性败血病、炭疽性败血症、链球菌性败血病、肺炎球菌性败血病、厌氧菌性败血病、其他特定的败血症、非特异性败血症、疱疹性败血病、耐甲氧西林金黄色葡萄球菌败血症、由革兰氏阴性菌引起的败血症、由流感嗜血杆菌引起的败血症、由大肠杆菌引起的败血症、假单胞菌致败血病、沙雷菌致败血病、其他由革兰氏阴性菌引起的败血症、分娩期间的全身性感染(未指明护理情况或不适用)败血症、产褥期脓毒症(未指明护理发作或不适用)、产褥期脓毒症(分娩,并提到产后并发症)、产褥期败血症(产后情况或并发症)③仅发生心力衰竭而不发生肝衰竭、肾脏衰竭和呼吸衰竭(肺衰竭)。
(2)排除标准心力:①年龄<18岁;②没有记录心力衰竭标记(即是否发生心力衰竭)的脓毒症患者;③所需临床资料或实验室数据不完整或数据缺失过多的患者。
2.数据采集
(1)基本信息:年龄、性别
(2)ICU中脓毒症患者数据:①血常规:白细胞(White Blood Cell,WBC)、血小板(Platelet);②肾功能:总胆红素(Total Bilirubin,TB)、直接胆红素(Direct Bilirubin,DB)、乳酸脱氢酶(Lactate Dehydrogenase,LDH)、天冬氨酸转氨酶与丙氨酸转氨酶比值(AST/ALT;Aspartate transaminase,AST;Alanine transaminase,ALT);③肾功能:血清肌酐(Creatinine,Cr)、血尿素氮(Blood Urea Nitrogen,BUN);④凝血功能:纤维蛋白原(Fibrinogen,FIB)、凝血酶原时向(Prothrombin time,PT);⑤血气分析:酸碱度(PH)、动脉二氧化碳分压(PaCO2)、动脉血氧分压(PaO2)、吸入氧浓度(FiO2);⑥血流动力学:动脉收缩压(Arterial Blood Pressure systolic,ABPs)、无创血压收缩压(Non Invasive BloodPressure systolic,NIBPs);⑦血生化检查:肌酸激酶(Creatine Kinase,CK)、葡萄糖(Glucose)、乳酸(Lactate);⑧其他:格拉斯哥昏迷评分(Glasgow Coma Scale,GCS)、心率(Heart Rate,HR)、呼吸频率(Respiratory Rate,RR)。
(3)模型输入数据为结果评估前一天,每一项生理指标的检测数据(若多次测量,则取其平均值)。
3.数据清洗和数据归一化
包括以下步骤:
(1)同一患者保留其第一次因脓毒症而入院或在ICU中诊断为脓毒症的数据;
(2)删除患者姓名、检查日期、患者ID等信息,仅保留生理指标数据;
(3)统一年龄数据格式为整数;
(4)对于性别的数字化处理,用“1”表示男性,“0”表示女性;
(5)将发生脓毒症心力衰竭的患者标签取为“1”,未发生脓毒症心力衰竭的患者标签取为“0”;
(6)对于空缺数据之处通过KNN(K Nearest Neighbors,K-近邻)算法来填补;
(7)对于异常值的处理,首先使用上四分位数和下四分位数来进行判定(正常值的范围在[Q2-1.5×Q3]至[Q1+1.5×Q2]之间,其中Q1为上四分位数,Q2为下四分位数,Q3为中位差Q3=Q1-Q2),被判定为异常值之处用该指标对应的中位数来代替;
(8)对数据进行归一化处理,即(原始数据-最小值)/(最大值-最小值)。
4.模型构建
根据逻辑回归原理进行构建模型其中的参数设置:惩罚项为l2,对偶或原始方法为False,停止求解的标准为1e-4(即求解到多少位小数的时候停止,此时认为已经求出最优解),正则化系数λ的倒数为0.01(数值越小表示具有越强的正则化),是否存在截距或偏差为True(存在偏差),用于标示分类模型中各种类型的权重为balanced,设置5折交叉验证,其余参数设置默认。
Claims (10)
1.一种基于逻辑回归的脓毒症心力衰竭早期预测系统,其特征在于,所述数据采集及存储部分、所述数据预处理部分、所述机器学习模型预测部分、所述脓毒症心力衰竭预测结果显示部分共4个部分,包括如下步骤:步骤S1,对脓毒症患者的生理指标数据进行预处理,将预处理后的数据输入预测模型,所述预测模型为逻辑回归算法;步骤S2,所述预测模型计算后输出所述脓毒症患者发生心力衰竭的概率。
2.根据权利要求1所述,其特征在于,所述数据采集及存储部分,包括如下步骤:步骤1,患者筛选;步骤2,生理数据提取。
3.根据权利要求2所述,其特征在于,所述步骤1患者筛选部分,将ICU患者的临床资料根据下述纳入标准及排除标准,筛选出符合条件的脓毒症心力衰竭患者,纳入标准:①年龄≥18岁;②符合疾病和相关健康问题的国际统计分类为脓毒症、败血症及其子分类的诊断标准;排除条件为:①没有记录心力衰竭标记的患者;②所需临床资料或实验室数据不完整或数据缺失过多的患者。
4.根据权利要求2所述,其特征在于,所述步骤2生理数据提取部分,提取ICU中脓毒症患者共24个生理指标的数据,包括:白细胞(WBC)、血小板(Platelet)、总胆红素(TotalBilirubin,TB)、直接胆红素(Direct Bilirubin,DB),乳酸脱氢酶(LactateDehydrogenase,LDH)、天冬氨酸转氨酶与丙氨酸转氨酶比值(AST/ALT;Aspartatetransaminase,AST;Alanine transaminase,ALT)、血清肌酐(Creatinine,Cr)、血尿素氮(Blood Urea Nitrogen,BUN)、纤维蛋白原(Fibrinogen,FIB)、凝血酶原时间(Prothrombintime,PT)、酸碱度(PH)、动脉二氧化碳分压(PaCO2)、动脉血氧分压(PaO2)、吸入氧浓度(FiO2)、动脉收缩压(Arterial Blood Pressure systolic,ABPs)、无创血压收缩压(NonInvasive Blood Pressure systolic,NIBPs)、肌酸激酶(Creatine Kinase,CK)、葡萄糖(Glucose)、乳酸(Lactate)、格拉斯哥昏迷评分(Glasgow Coma Scale,GCS)、心率(HeartRate,HR)、呼吸频率(Respiratory Rate,RR)、年龄、性别。
5.根据权利要求1所述,其特征在于,所述数据预处理部分,包括数据清洗和数据归一化。
6.根据权利要求4所述,其特征在于,输入模型数据为结果评估前一天,每一项生理指标的检测数据(若多次测量,则取其平均值)。
7.根据权利要求5所述,其特征在于,所述数据清理的方法包括如下步骤:步骤a,若存在空缺指标则利用KNN(K Nearest Neighbors,K-近邻)算法对其进行空缺值填补;步骤b,用上四分位数和下四分位数来判断异常值,并用中位数替换异常值。
8.根据权利要求5所述,其特征在于,所述数据归一化为对原始数据进行如下处理:
(原始数据-最小值)/(最大值-最小值)。
9.根据权利要求1所述,其特征在于,所述机器学习模型预测部分为根据逻辑回归原理进行构建模型其中的参数设置:惩罚项为12,对偶或原始方法为False,停止求解的标准为1e-4,正则化系数λ的倒数为0.01,是否存在截距或偏差为True(存在偏差),用于标示分类模型中各种类型的权重为balanced,设置5折交叉验证,模型评价标准为AUC(AUC,AreaUnder Curve,ROC曲线下的面积),其余参数设置默认。
10.根据权利要求1所述,其特征在于,所述脓毒症心力衰竭预测结果显示部分,显示脓毒症患者发生心力衰竭的概率(0~1之间,约接近1表明发生心力衰竭的概率越大)。
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Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109119167A (zh) * | 2018-07-11 | 2019-01-01 | 山东师范大学 | 基于集成模型的脓毒症死亡率预测系统 |
CN115049069A (zh) * | 2022-06-01 | 2022-09-13 | 东南大学 | 一种可视化交互式的脓毒症早期智能预警方法 |
CN115132348A (zh) * | 2022-05-31 | 2022-09-30 | 四川大学华西医院 | 一种预测脓毒血症患者发生急性肾损伤的概率预测系统 |
CN115223706A (zh) * | 2022-06-20 | 2022-10-21 | 北京医院 | 适用于移动监护设备的脓毒症早期筛查模型 |
CN116013516A (zh) * | 2022-12-07 | 2023-04-25 | 上海市同济医院 | 一种脓毒症相关性急性肾损伤的死亡风险预测系统及方法 |
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Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109119167A (zh) * | 2018-07-11 | 2019-01-01 | 山东师范大学 | 基于集成模型的脓毒症死亡率预测系统 |
CN115132348A (zh) * | 2022-05-31 | 2022-09-30 | 四川大学华西医院 | 一种预测脓毒血症患者发生急性肾损伤的概率预测系统 |
CN115049069A (zh) * | 2022-06-01 | 2022-09-13 | 东南大学 | 一种可视化交互式的脓毒症早期智能预警方法 |
CN115223706A (zh) * | 2022-06-20 | 2022-10-21 | 北京医院 | 适用于移动监护设备的脓毒症早期筛查模型 |
CN116013516A (zh) * | 2022-12-07 | 2023-04-25 | 上海市同济医院 | 一种脓毒症相关性急性肾损伤的死亡风险预测系统及方法 |
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