Marginal Structural Models Using Calibrated Weights With SuperLearner: Application to Type II Diabetes Cohort

2型糖尿病 机器学习 医学 人工智能 背景(考古学) 因果推理 二甲双胍 人口 计算机科学 糖尿病 数学 统计 内分泌学 古生物学 环境卫生 生物
作者
Sumeet Kalia,Olli Saarela,Tao Chen,Braden O’Neill,Christopher Meaney,Jessica Gronsbell,Babak Aliarzadeh,Ervin Sejdić,Michael Escobar,Rahim Moineddin,Conrad Pow,Frank Sullivan,Michelle Greiver
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:26 (8): 4197-4206 被引量:1
标识
DOI:10.1109/jbhi.2022.3175862
摘要

As different scientific disciplines begin to converge on machine learning for causal inference, we demonstrate the application of machine learning algorithms in the context of longitudinal causal estimation using electronic health records. Our aim is to formulate a marginal structural model for estimating diabetes care provisions in which we envisioned hypothetical (i.e. counterfactual) dynamic treatment regimes using a combination of drug therapies to manage diabetes: metformin, sulfonylurea and SGLT-2i. The binary outcome of diabetes care provisions was defined using a composite measure of chronic disease prevention and screening elements [27] including (i) primary care visit, (ii) blood pressure, (iii) weight, (iv) hemoglobin A1c, (v) lipid, (vi) ACR, (vii) eGFR and (viii) statin medication. We used several statistical learning algorithms to describe causal relationships between the prescription of three common classes of diabetes medications and quality of diabetes care using the electronic health records contained in National Diabetes Repository. In particular, we generated an ensemble of statistical learning algorithms using the SuperLearner framework based on the following base learners: (i) least absolute shrinkage and selection operator, (ii) ridge regression, (iii) elastic net, (iv) random forest, (v) gradient boosting machines, and (vi) neural network. Each statistical learning algorithm was fitted using the pseudo-population generated from the marginalization of the time-dependent confounding process. Covariate balance was assessed using the longitudinal (i.e. cumulative-time product) stabilized weights with calibrated restrictions. Our results indicated that the treatment drop-in cohorts (with respect to metformin, sulfonylurea and SGLT-2i) may have improved diabetes care provisions in relation to treatment naïve (i.e. no treatment) cohort. As a clinical utility, we hope that this article will facilitate discussions around the prevention of adverse chronic outcomes associated with type II diabetes through the improvement of diabetes care provisions in primary care.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
FashionBoy应助sunguangbin采纳,获得10
2秒前
唐帅发布了新的文献求助10
3秒前
3秒前
5秒前
Hello应助wangwang采纳,获得10
5秒前
龙成阳完成签到,获得积分10
7秒前
hhhc完成签到,获得积分10
8秒前
song发布了新的文献求助10
9秒前
bkagyin应助111采纳,获得10
10秒前
lwtsy发布了新的文献求助10
10秒前
高高小凝完成签到,获得积分10
10秒前
11秒前
11秒前
12秒前
笙笙完成签到,获得积分10
13秒前
高高小凝发布了新的文献求助10
14秒前
15秒前
Steven发布了新的文献求助30
16秒前
笙笙发布了新的文献求助10
17秒前
谨言完成签到 ,获得积分10
17秒前
FIN应助sns八丘采纳,获得20
18秒前
19秒前
无奈的如彤完成签到,获得积分20
19秒前
小七发布了新的文献求助10
19秒前
sanch完成签到 ,获得积分10
21秒前
lwtsy完成签到,获得积分10
22秒前
染墨完成签到,获得积分10
24秒前
sns八丘给sns八丘的求助进行了留言
24秒前
FashionBoy应助ma采纳,获得10
25秒前
27秒前
归海含烟完成签到,获得积分10
28秒前
跳脚的虾完成签到 ,获得积分10
31秒前
33秒前
cmicha发布了新的文献求助10
37秒前
可爱完成签到 ,获得积分10
39秒前
isak完成签到,获得积分10
42秒前
42秒前
丘比特应助可爱采纳,获得10
45秒前
HS发布了新的文献求助10
46秒前
大模型应助wuxunxun2015采纳,获得10
47秒前
高分求助中
【此为提示信息,请勿应助】请按要求发布求助,避免被关 20000
Production Logging: Theoretical and Interpretive Elements 3000
CRC Handbook of Chemistry and Physics 104th edition 1000
Izeltabart tapatansine - AdisInsight 600
Introduction to Comparative Public Administration Administrative Systems and Reforms in Europe, Third Edition 3rd edition 500
Distinct Aggregation Behaviors and Rheological Responses of Two Terminally Functionalized Polyisoprenes with Different Quadruple Hydrogen Bonding Motifs 450
Individualized positive end-expiratory pressure in laparoscopic surgery: a randomized controlled trial 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3761742
求助须知:如何正确求助?哪些是违规求助? 3305515
关于积分的说明 10134536
捐赠科研通 3019564
什么是DOI,文献DOI怎么找? 1658216
邀请新用户注册赠送积分活动 791974
科研通“疑难数据库(出版商)”最低求助积分说明 754751