Semantic Evaluation of Environmental and Nutrition Factors Impacting Female Reproductive Disorders

影响女性生殖疾病的环境和营养因素的语义评估

• 批准号: 10534658
• 负责人: Lauren E Chan
• 金额: $ 4.22万
• 依托单位: OREGON STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-16 至 2023-12-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10534658
• 关键词: Abdominal Pain; Affect; Back Pain; Bioinformatics; Caring; Chemicals; Chronic; Classification; Clinical; Clinical Data; Computer Models; Computing Methodologies; Data; Data Science; Decision Making; Decision Trees; Diagnosis; Diagnostic; Diagnostic Imaging; Differential Diagnosis; Disease; Disease Management; Early identification; Environmental Exposure; Etiology; Evaluation; Exposure to; Female; Fertility; Fertility Disorders; Fibroid Tumor; Financial Hardship; Food; Future; Genetic; Genetic Diseases; Genetic Polymorphism; Genomics; Genotype; Goals; Gold; Gynecologic; Health; Healthcare; Hemorrhage; Human; Individual; Infertility; Investigation; Language; Lead; Leadership; Literature; Machine Learning; Medical; Menstrual cycle; Mental Health; Metabolism; Methods; Modality; Modeling; National Institute of Child Health and Human Development; North Carolina; Nutrient; Nutritional; Ontology; Ovarian Cysts; Participant; Patient Self-Report; Patients; Pelvic Pain; Phenotype; Prevention; Procedures; Professional Competence; Psyche structure; Quality of life; Recommendation; Recording of previous events; Registries; Reproduction; Reproductive Health; Research; Risk; Risk Assessment; Risk Factors; Science; Scientist; Semantics; Services; Signs and Symptoms; Source; Standardization; Strategic Planning; Surveys; Symptoms; Techniques; Terminology; Therapeutic Intervention; Training; Translational Research; Tubal Ligation; Universities; Uterine Fibroids; Validation; Women' s Health; Work; biomedical ontology; clinical care; cost; data integration; dietary; dietary supplements; disease classification; disease diagnosis; disorder prevention; disorder risk; endometriosis; experience; fertility preservation; gene environment interaction; improved; insight; interoperability; learning strategy; lens; method development; modifiable risk; novel; novel strategies; nutrition; persistent symptom; phenomics; phenotypic data; psychologic; random forest; reproductive; reproductive system disorder; response; skills; symptom management; tool

Chronic female reproductive disorders such as endometriosis, fibroids, and ovarian cysts can lead individuals to suffer from severe pelvic, abdominal, and/or back pain; heavy menstrual cycles; severe bloating; and reduced fertility. While some individuals with these disorders present with similar symptoms, others can appear asymptomatic until they experience infertility, or the disorder is identified during a medical procedure like tubal ligation. Identifying asymptomatic individuals or differentially diagnosing reproductive disorders for individuals with similar symptoms without pursuing expensive and invasive gold standard diagnostic imaging is difficult. Misdiagnosis and delays in diagnosis are common for reproductive disorders, fueling prolonged symptoms and delays in appropriate therapeutic intervention. Environmental exposures and nutrition have been hypothesized as modifiable risk factors for female reproductive disorders. As such, understanding these risk factors is essential for clinicians to provide recommendations for reproductive disorder prevention and diagnosis. Survey tools are commonly used to collect environmental and nutrition exposure information, but their limited computability makes it challenging to integrate these data with other biomedical data such as phenotypes or genotypes to evaluate reproductive disorder risk. The general objective of this research is to establish new methods for development of semantically computable survey data and their integration with other data modalities and subsequent machine learning for evaluating health risks using biomedical ontologies. The specific goal of this application is to apply these techniques to evaluate clinical data and exposures impacting female reproductive disorders and differentially classify endometriosis, fibroid, and ovarian cyst risk using semantically encoded versus unencoded data. This objective is in alignment with the NICHD Strategic Plan Research Theme of ‘promoting gynecological, andrological, and reproductive health’ through the utilization of ‘integrated genetic and phenotypic exposure data to understand the underlying mechanisms of conditions such as endometriosis, fibroids’. Specific Aim 1 will computationally model female reproductive disorder phenotypes, genotypes, and exposures, focusing on endometriosis, fibroids, and ovarian cysts. Specific Aim 2 will ontologically encode environmental exposures and evaluate reproductive disorder risk, supporting future reproductive research and clinical care improvements. The training plan outlined in this proposal will allow the Candidate to obtain essential training to: 1) Apply novel inference modeling methods for evaluating environmental exposure and nutrition factors influencing reproductive health, 2) Expand skills in data science techniques that can be applied to nutrition and other types of data integration in biomedical sciences, 3) Develop further expertise in nutrition and metabolism and their impact on human reproductive health, 4) Learn methods for clinical and translational requirements analysis and validation approaches, and 5) Develop leadership and career skills required to become a successful independent research scientist.

慢性女性生殖疾病，如子宫内膜异位症，肌瘤和卵巢囊肿，可导致个体遭受严重的骨盆，腹部和/或背部疼痛;月经周期重;严重腹胀;和生育能力下降。虽然一些患有这些疾病的人表现出类似的症状，但其他人可能会出现无症状，直到他们经历不孕症，或者在输卵管结扎等医疗程序中发现这种疾病。识别无症状个体或鉴别诊断生殖障碍的个体具有相似的症状，而不追求昂贵的和侵入性的金标准诊断成像是困难的。误诊和延误诊断是常见的生殖系统疾病，助长长期症状和延误适当的治疗干预。环境暴露和营养被假设为女性生殖障碍的可改变的风险因素。因此，了解这些风险因素对于临床医生提供生殖疾病预防和诊断建议至关重要。调查工具通常用于收集环境和营养暴露信息，但其有限的可计算性使得将这些数据与其他生物医学数据（如表型或基因型）整合以评估生殖障碍风险具有挑战性。本研究的总体目标是建立新的方法，用于开发语义可计算的调查数据，并将其与其他数据模式和随后的机器学习相结合，用于使用生物医学本体来评估健康风险。本申请的具体目标是应用这些技术评价影响女性生殖疾病的临床数据和暴露，并使用语义编码与未编码数据对子宫内膜异位症、纤维瘤和卵巢囊肿风险进行差异化分类。这一目标与NICHD战略计划研究主题“通过利用综合遗传和表型暴露数据来了解子宫内膜异位症，纤维瘤等疾病的潜在机制，促进妇科，男性和生殖健康”保持一致。具体目标1将计算模型女性生殖障碍的表型，基因型和风险，重点是子宫内膜异位症，肌瘤和卵巢囊肿。具体目标2将对环境暴露进行本体编码，并评估生殖障碍风险，支持未来的生殖研究和临床护理改进。本建议书中概述的培训计划将使候选人获得必要的培训，以便：1）应用新的推理建模方法来评估影响生殖健康的环境暴露和营养因素，2）扩展数据科学技术的技能，这些技术可以应用于生物医学科学中的营养和其他类型的数据集成，3）进一步发展营养和代谢及其对人类生殖健康的影响方面的专业知识，4）学习临床和翻译要求分析和验证方法的方法，5）培养成为一名成功的独立研究科学家所需的领导能力和职业技能。