Machine learning approaches towards risk assessment and prediction of adverse pregnancy outcomes

用于风险评估和预测不良妊娠结局的机器学习方法

基本信息

批准号：
10453757
负责人：
DAVID M. HAAS
金额：
$ 43.72万
依托单位：
INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
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：
2020-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10453757
关键词：
Adverse event Ally Artificial Intelligence Characteristics Clinic Clinical Clinical Data Collaborations Computational Biology Data Data Collection Data Scientist Development Discipline Disease Electronic Health Record Enrollment Environmental Risk Factor Fetal Growth Retardation First Pregnancy Trimester Genetic Genetic Structures Genome Genotype Gestational Diabetes Goals Indiana Individual Intervention Knowledge Life Style Machine Learning Maternal Health Medical Medical Genetics Medical Informatics Mind Modeling Molecular Molecular Genetics Monitor Mothers National Institute of Child Health and Human Development Nulliparity Onset of illness Outcome Study Pathway interactions Patients Pattern Performance Perinatal mortality demographics Pharmacogenomics Physiological Pre-Eclampsia Precision Health Pregnancy Pregnancy Outcome Premature Birth Prevention strategy Public Health Records Research Research Personnel Resolution Resources Risk Risk Assessment Scientist Second Pregnancy Trimester Social support Stress Techniques United States National Institutes of Health Universities Woman adverse pregnancy outcome antenatal cohort demographics design dietary experience fetal fetus at risk improved individualized prevention insight machine learning model method development model development novel perinatal morbidity personalized risk prediction phenotypic data precision medicine predictive modeling prospective response risk prediction sound structured data success

项目摘要

PROJECT SUMMARY The primary objectives of this project include understanding the interplay between molecular, genetic and clinical factors related to adverse pregnancy outcomes (APOs), method development for accurate risk assessment of APOs well before they occur, and method development for collecting additional clinical data in routine treatment of at-risk-subjects. Towards these goals we have assembled a team of investigators with clinical, translational, and computational expertise capable of identifying novel contributors to APOs as well as facilitating clinician-patient interactions using data-driven and theoretically sound machine learning approaches. Our strategies will rely on advanced machine learning as well as integration of clinical, genetic, and molecular data and hold promise to bring precision medicine to the treatment and experience of women during and post pregnancy. We will predominantly rely on the data collected during the national “Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be”; i.e., the nuMoM2b study. Using the cohort of 10,038 nulliparous women, we will efficiently accomplish 3 Aims: to integrate genetic, clinical, and molecular features towards a deep understanding of APOs; to develop machine learning models for advanced risk prediction; and to engage in active data collection towards risk assessment and model development. Using a close collaboration between computational and clinical scientists, we believe this proposal will result in important advances in understanding the molecular and clinical aspects of APOs as well as assessing the risk for APOs and thus providing tangible contributions to maternal health.

项目摘要该项目的主要目标包括了解分子，遗传之间的相互作用和与不良妊娠结局有关的临床因素（APO），准确的方法开发在APO发生之前对APO的风险评估，以及用于收集额外的方法开发处于风险受试者的常规治疗中的临床数据。达到这些目标，我们组建了一个团队具有临床，转化和计算专业知识的研究人员，能够识别新颖使用数据驱动和理论声音机器学习方法。我们的策略将依靠高级机器学习以及临床，遗传和分子数据的整合，并有望带来精确医学对妇女在怀孕期间的治疗和经验。我们将主要依赖于国家“无效妊娠结局研究中收集的数据：监测准母亲”；即NUMOM2B研究。使用10,038个无效妇女的队列，我们将有效地完成3个目标：将遗传，临床和分子特征整合到深处对Apos的理解；开发用于先进风险预测的机器学习模型；然后参与积极的数据收集，以进行风险评估和模型开发。使用关闭计算和临床科学家之间的合作，我们认为该建议将导致了解APO的分子和临床方面以及评估的重要进展 APO的风险，从而为孕产妇健康提供了切实的贡献。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Using Association Rules to Understand the Risk of Adverse Pregnancy Outcomes in a Diverse Population.

使用关联规则了解不同人群中不良妊娠结果的风险。

DOI：
发表时间：
2023
期刊：
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
影响因子：
0
作者：
Chu,Hoyin;Ramola,Rashika;Jain,Shantanu;Haas,DavidM;Natarajan,Sriraam;Radivojac,Predrag
通讯作者：
Radivojac,Predrag

A Probabilistic Approach to Extract Qualitative Knowledge for Early Prediction of Gestational Diabetes.

DOI：
10.1007/978-3-030-77211-6_59
发表时间：
2021-06
期刊：
Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )
影响因子：
0
作者：
Karanam A;Hayes AL;Kokel H;Haas DM;Radivojac P;Natarajan S
通讯作者：
Natarajan S

Searching and visualizing genetic associations of pregnancy traits by using GnuMoM2b.

使用 GnuMoM2b 搜索和可视化妊娠性状的遗传关联。

DOI：
10.1093/genetics/iyad151
发表时间：
2023
期刊：
Genetics
影响因子：
3.3
作者：
Yan,Qi;Guerrero,RafaelF;Khan,RaiyanR;Surujnarine,AndyA;Wapner,RonaldJ;Hahn,MatthewW;Raja,Anita;Salleb-Aouissi,Ansaf;Grobman,WilliamA;Simhan,Hyagriv;Blue,NathanR;Silver,Robert;Chung,JudithH;Reddy,UmaM;Radivojac,Predrag
通讯作者：
Radivojac,Predrag