Multi-objective representation learning methods for interpetable predictions of patient outcomesusing electronic health records
使用电子健康记录对患者结果进行可重复预测的多目标表示学习方法
基本信息
- 批准号:10684907
- 负责人:
- 金额:$ 23.66万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-09-01 至 2024-08-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAffectArchivesAreaAutomobile DrivingCardiovascular DiseasesClinicalCommunitiesComplexCouplesDataData ReportingData SetDatabasesDevelopmentDiseaseDisease ProgressionElectronic Health RecordEngineeringFeedbackGoalsGraphHandHealthHealth SciencesHealth systemHeart DiseasesHeart failureHospitalizationHospitalsInpatientsKnowledgeLearningMachine LearningMapsMeasurementMeasuresMentorsMethodologyMethodsMiningModelingNatureOutcomePathologyPatient CarePatient-Focused OutcomesPatientsPennsylvaniaPerformancePharmaceutical PreparationsPhasePopulationProcessPropertyProtocols documentationQuality of lifeRecommendationReplacement ArthroplastyResearchResearch PersonnelStructureTechniquesTestingTextTimeTrainingTranslatingUniversity HospitalsVisitWorkcare costscluster computingdata complexitydeep learningdeep neural networkdesigndisease diagnosisdisorder subtypegradient boostingheart disease riskhigh dimensionalityhospital readmissionimprovedinsightlearning strategymachine learning methodmachine learning modelnetwork architectureneural networknovelopen source tooloperationpatient health informationpoint of carepredictive modelingreadmission ratesreadmission riskrisk mitigationrisk prediction modelstatistical and machine learningtemporal measurement
项目摘要
Project Summary/Abstract
This project proposes new methods for representing data in electronic health records (EHR) to improve pre-
dictive modeling and interpretation of patient outcomes. EHR data offer a promising opportunity for advancing
the understanding of how clinical decisions and patient conditions interact over time to influence patient health.
However, EHR data are difficult to use for predictive modeling due to the various data types they contain (con-
tinuous, categorical, text, etc.), their longitudinal nature, the high amount of non-random missingness for certain
measurements, and other concerns. Furthermore, patient outcomes often have heterogenous causes and re-
quire information to be synthesized from several clinical lab measures and patient visits. The core challenge
at hand is overcoming the mismatch between data representations in the EHR and the assumptions underly-
ing commonly used statistical and machine learning (ML) methods. To this end, this project proposes novel
wrapper-based methods for learning informative features from EHR data. Both methods propose specialized
operators to handle sequential data, time delays, and variable interactions, and have the capacity to discover
underlying clinical rules/decisions that affect patient outcomes. Importantly, both methods also produce archives
of possible models that represent the best trade-offs between complexity and accuracy, which assists in model
interpretation. These method advances are made possible by encoding a rich set of data operations as nodes
in a directed acyclic graph, and optimizing the graph structures using multi-objective optimization. The central
hypothesis of this research is that multi-objective optimization can learn effective data representations from
the EHR to produce accurate, explanatory models of patient outcomes. Preliminary work has shown that these
methods can effectively learn low-order data representations that improve the predictive ability of several state-
of-the-art ML methods. This technique demonstrates good scaling properties with high-dimensional biomedical
data. Aim 1 (K99) is to develop a multi-objective feature engineering method that pairs with existing ML methods
to iteratively improve their performance by constructing new features from the raw data and using feedback from
the trained model to guide feature construction. In Aim 2 (K99), this method is applied to form predictive models
of the risk of heart disease and heart failure using longitudinal EHR data. The resultant models will be inter-
preted with the help of mentors in order to translate predictions into clinical recommendations. For Aim 3 (R00),
a second method is proposed that uses a similar framework to optimize existing neural network approaches in
order to simplify their structure as much as possible while maintaining accuracy. The goal of Aim 4 (R00) is
to identify hospital patients who are at risk of readmission and propose point-of-care strategies to mitigate that
risk. This goal is facilitated through the application of the proposed methods to patient data collected from the
Hospital of the University of Pennsylvania, the Geisinger Health System, and publicly available EHR databases.
项目总结/文摘
项目成果
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Interpretation of machine learning predictions for patient outcomes in electronic health records.
机器学习对电子健康记录中患者结果的预测的解释。
- DOI:
- 发表时间:2019
- 期刊:
- 影响因子:0
- 作者:Cava,WilliamLa;Bauer,Christopher;Moore,JasonH;Pendergrass,SarahA
- 通讯作者:Pendergrass,SarahA
Semantic variation operators for multidimensional genetic programming.
多维遗传规划的语义变异算子。
- DOI:10.1145/3321707.3321776
- 发表时间:2019
- 期刊:
- 影响因子:0
- 作者:LaCava,William;Moore,JasonH
- 通讯作者:Moore,JasonH
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{{ truncateString('William La Cava', 18)}}的其他基金
Multi-objective representation learning methods for interpetable predictions of patient outcomesusing electronic health records
使用电子健康记录对患者结果进行可重复预测的多目标表示学习方法
- 批准号:
10453863 - 财政年份:2021
- 资助金额:
$ 23.66万 - 项目类别:
Multi-objective representation learning methods for interpetable predictions of patient outcomesusing electronic health records
使用电子健康记录对患者结果进行可重复预测的多目标表示学习方法
- 批准号:
10477327 - 财政年份:2021
- 资助金额:
$ 23.66万 - 项目类别:
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