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Predictive Analytics in Hemodialysis: Enabling Precision Care for Patient with ESKD

血液透析中的预测分析：为 ESKD 患者提供精准护理

基本信息

批准号：
10605248
负责人：
Benjamin Alan Goldstein
金额：
$ 53.61万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10605248
关键词：
Biological Markers Cardiac Caring Cessation of life Chronic Classification Clinical Clinical Data Clinical Management Clinical Research Collaborations Companions Complex Coupled Coupling Data Decision Making Dialysis procedure Disease Electronic Health Record End stage renal failure Event Excision Functional disorder Future Guidelines Health Hemodialysis Heterogeneity Hospitalization Hypertension Hypotension Individual Infection Laboratories Left Life Life Expectancy Longevity Machine Learning Malnutrition Medical Methods Modeling Monitor Nature Outcome Patient Care Patients Persons Phenotype Predictive Analytics Preventive Preventive care Prognosis Provider Quality of Life Assessment Research Risk Risk Assessment System Testing Time Translations Transplantation Update Weight Gain Work analytical tool associated symptom clinical care clinical decision-making clinical phenotype deep learning disease phenotype experience hemodynamics individual patient innovation insight learning strategy member mortality novel personalized care personalized decision predictive tools prospective survival prediction survivorship tool usability web-based tool

项目摘要

ABSTRACT End stage kidney disease (ESKD) is a complex disease with individuals having variable life- expectancies, with 25% dying within 1 year and 41% surviving at least 5 years. While providers recognize that patients are different – and ought to be differently – there are no tools to reliably forecast individual life expectancy and aid in treatment individualization. Instead, providers are left with often unclear or incomplete guidelines on how best to manage patients. In order to provide precision care for patients on hemodialysis (HD), there is a critical need to be able to (1) dynamically assess life expectancy for medical decision-making; and (2) identify distinct clinical phenotypes to enhance clinical monitoring and care planning. Our central hypothesis is that there is heterogeneity in patient survivorship and disease trajectory that, when known, can be used to provide more personalized and effective care. By coupling novel machine learning approaches for survival prediction with granular clinical data on HD patients, we will be able to develop the analytic tools necessary to support precision care. At the completion of this proposal we will have tools to dynamically assess a patient's life expectancy and insights into heterogeneous disease phenotypes for patients with ESKD. These tools will allow providers to make informed treatment decisions as well as lay the groundwork for further precision research into optimized patient care.

摘要终末期肾病（ESKD）是一种复杂的疾病，个体具有可变的寿命，预期，25%在1年内死亡，41%存活至少5年。虽然供应商认识到病人是不同的-应该是不同的-没有可靠的工具，预测个体预期寿命，并帮助治疗个体化。相反，供应商关于如何最好地管理患者的指导方针往往不明确或不完整。为了为血液透析（HD）患者提供精确护理，迫切需要能够（1）动态评估医疗决策的预期寿命;（2）识别不同的临床表型，以加强临床监测和护理规划。我们的核心假设是，患者生存率和疾病轨迹存在异质性，当已知时，用于提供更加个性化和有效的护理。通过结合新颖的机器学习通过HD患者的粒度临床数据进行生存预测的方法，我们将能够开发必要的分析工具，以支持精确护理。在完成这一建议我们将有工具来动态评估病人的预期寿命和见解， ESKD患者的异质性疾病表型。这些工具将允许供应商做出明智的治疗决定，并为进一步的精确研究奠定基础优化病人护理

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer

DOI：
发表时间：
2020-11
期刊：
Advances in neural information processing systems
影响因子：
0
作者：
Zidi Xiu;Junya Chen;Ricardo Henao;B. Goldstein;L. Carin;Chenyang Tao
通讯作者：
Zidi Xiu;Junya Chen;Ricardo Henao;B. Goldstein;L. Carin;Chenyang Tao

Performance of the National Early Warning Score in Hospitalized Patients With Kidney Failure on Maintenance Hemodialysis.

DOI：
10.1016/j.xkme.2022.100506
发表时间：
2022-08
期刊：
KIDNEY MEDICINE
影响因子：
3.9
作者：
Cavalier, Joanna;Zhao, Congwen;Scialla, Julia;Bedoya, Armando;Goldstein, Benjamin A.
通讯作者：
Goldstein, Benjamin A.

Variational Disentanglement for Rare Event Modeling

DOI：
10.1609/aaai.v35i12.17253
发表时间：
2021-05
期刊：
Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence
影响因子：
0
作者：
Zidi Xiu;Chenyang Tao;M. Gao;Connor Davis;B. Goldstein;Ricardo Henao
通讯作者：
Zidi Xiu;Chenyang Tao;M. Gao;Connor Davis;B. Goldstein;Ricardo Henao

Variational Learning of Individual Survival Distributions.