PREPARE: PERSONALIZED REHABILITATION VIA NOVEL AI PATIENT STRATIFICATION STRATEGIES

准备：通过新颖的人工智能患者分层策略进行个性化康复

• 批准号: 10086219
• 金额: $ 20.99万
• 依托单位: TRILATERAL RESEARCH LTD
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10086219
• 关键词: PREPARE; PERSONALIZED; REHABILITATION; VIA; NOVEL

PREPARE aims at advancing rehabilitation care for patients with chronic non-communicable diseases. As rehabilitation is a complex, multifaceted, and highly personal process, we currently lack reliable patient stratification and outcome prediction tools. While big data approaches provide a path forward, existing data sets pose numerous challenges. These challenges can be overcome by combining advances in clinical research, socio-behavioral and public health research, data science, and advanced statistical and AI learning methods. We will apply machine learning techniques on our large-scale patient data sets including key sociodemographic, living conditions, and behavioral information to stratify patients based on expected outcomes. A subsequent analysis will consider all potential predictors for rehabilitation outcome. Baseline strata and modifiers will be used to develop a comprehensive model of each clinical situation to increase management quality, improve outcomes, and reduce costs. As proof of principle we will develop a platform for sharing model results, exploiting the open-science EHDEN platform, and showcase the novel approach through pilot cases of nine pathologies which constitute the most dominant causes for rehabilitation worldwide: hand disorders, hip and knee prosthesis, intermittent claudication, lower limb loss, Parkinson’s disease/Parkinsonisms, scoliosis, spine disorders, temporo-mandibular articulation, and hypertension. We will also develop a certification roadmap. PREPARE will result in innovative, robust, personalized, and validated data-driven computational prediction and stratification tools to support healthcare professionals and patients in selecting the optimal therapy strategy.

准备工作旨在促进慢性非传染性疾病患者的康复护理。由于康复是一个复杂的、多方面的、高度个人化的过程，我们目前缺乏可靠的患者分层和结果预测工具。虽然大数据方法提供了一条前进的道路，但现有的数据集构成了许多挑战。这些挑战可以通过结合临床研究、社会行为和公共卫生研究、数据科学以及先进的统计和人工智能学习方法的进步来克服。我们将在我们的大规模患者数据集上应用机器学习技术，包括关键的社会人口统计、生活条件和行为信息，以基于预期结果对患者进行分层。随后的分析将考虑康复结果的所有潜在预测因素。将使用基线分层和修改器来开发每种临床情况的综合模型，以提高管理质量、改善结果并降低成本。作为原则的证明，我们将开发一个共享模型结果的平台，利用开放科学的Ehden平台，并通过九种病理的试点案例展示新的方法，这些疾病构成了全球康复的最主要原因：手部疾病、髋关节和膝关节假体、间歇性跛行、下肢丧失、帕金森病/帕金森症、脊柱侧弯、脊柱疾病、颞下颌关节和高血压。我们还将制定认证路线图。Prepare将带来创新的、强大的、个性化的和经过验证的数据驱动的计算预测和分层工具，以支持医疗保健专业人员和患者选择最佳治疗策略。