Informatics Core

信息核心(IC)将翻译UCSF收集的慢性下腰痛(CLBP)患者数据 深入研究可改善cLBP结果的临床智能转换模型。通过将加州大学旧金山分校的 凭借这一无与伦比的基础设施基于电子病历数据执行人工智能研究的丰富临床经验， 该中心有潜力产生创新的合作研究，显著改善cLBP的结果 对病人来说。IC的首要目标是与加州大学旧金山分校的其他研究和临床人员共生 核心开发用于解释成像和其他电子健康记录(EHR)数据的新人工智能方法 以显著改善慢性下腰痛的护理。为了充分利用加州大学洛杉矶分校REACH所收集的数据，IC 将帮助开发和临床应用统计和机器学习方法，包括 明确的目标。在目标1中，为了根据cLBP表型识别患者亚组，我们将使用 重新使用所有其他核心，以使用现有的精选EHR和新收集的cLBP患者数据以更好地 了解cLBP疾病的途径。分析治疗目标之间的权衡和协同效应，IC 将应用典型相关分析来发现临床洞察力、潜在的协同效应和权衡 各种治疗结果(疼痛、身体和心理社会残疾指标)，以发展机器学习 预测患者特定治疗反应的方法。最后，IC将应用最先进的卷积 用神经网络技术预测医学成像的临床相关结果以优化这一点 为患者提供临床洞察和个性化治疗计划。

The Informatics Core (IC) will translate patient data on chronic lower back pain (cLBP) collected by UCSF REACH into translational models of clinical intelligence that improve cLBP outcomes. By coupling UCSF's extensive clinical experience with this second-to-none infrastructure to perform AI research based on EHR data, the IC has the potential to produce innovative collaborative research that significantly improves cLBP outcomes for patients. The overarching goal of IC is to work in symbiosis with other UCSF REACH Research and Clinical cores to develop novel AI methodologies for interpreting imaging and other Electronic Health Record (EHR) data to significantly improve cLBP care. In order to enable the full utilization of data collected by UCSF REACH, IC will aid in the development and clinical application of statistical and machine learning methods with the following specific aims. In in Aim 1, to identify patient subgroups based on cLBP phenotypes, we will work with the Reall other cores to use the existing curated EHR and newly collected data from cLBP patients to better understand cLBP disease pathways. To analyze trade-offs and synergies between treatment objectives, IC will apply canonical correlation analysis to discover clinical insights potential synergies and trade-offs between various treatment outcomes (pain, physical and psychosocial disability metrics) to develop machine learning methods for predicting patient-specific treatment response. Finally, IC will apply state-of-the art convolutional neural network techniques in predicting clinically relevant outcomes from medical imaging to optimize this clinical insight and personalize treatment plans for patients.