SBIR Phase I: Food Allergy Management Platform

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是帮助美国3200万患有食物过敏（FA）的人。FA给患者及其家属带来心理、医疗和经济后果。 该项目旨在利用行为科学和数字化工具的最新进展，创建一个平台，支持新批准的口服免疫疗法（OIT）治疗的实施。OIT是一种积极主动的方法，与其他反应性治疗相反，可以实现更好的患者结局。 拟议的项目将通过数据收集增加对FA的科学认识。 这个小企业创新研究（SBIR）第一阶段项目旨在帮助FA患者和临床医生实施OIT治疗。OIT具有巨大的潜力，可以在FA反应发生之前将其严重程度降至最低，甚至消除与传统治疗相比的最终健康风险和不便体验。尽管如此，由于需要为每个患者定制以及患者和医生之间的数据流，初步的OIT经验显示出局限性。这项研究的主要目标是开发和验证一个数字平台，该平台利用行为科学方法和机器学习（ML）来促进OIT治疗的依从性，剂量管理，治疗个性化和患者-临床医生沟通。具体工作将涉及：1）开发、培训和评估一系列能够提供新见解和预测事件的专有ML算法; 2）开发和测试平台完整性所需的其他软件资产（移动的应用程序、符合HIPAA的数据库和API，可与第三方系统集成）;以及3）在FA患者的真实的经验中验证该系统。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to help the 32 million people in the US suffering from Food Allergy (FA). FA brings psychological, medical and financial consequences for patients and their relatives. The proposed project aims to leverage the latest advances on behavioral science and digital tools to create a platform that supports the implementation of the newly FDA-approved Oral Immunotherapy (OIT) treatments. OIT is a proactive approach that, contrary to other reactive treatments, achieves better patient outcomes. The proposed project will increase scientific understanding about FA through data collection. This Small Business Innovation Research (SBIR) Phase I project seeks to help FA patients and clinicians in the implementation of OIT treatments. OIT has enormous potential to minimize the severity of an FA reaction before it happens, even eliminating the eventual health risks and inconvenient experiences compared to traditional treatments. Still, the preliminary OIT experiences have shown limitations due to needs for customization for each patient and data flow between patients and doctors. The main objective of this research is to develop and validate a digital platform that leverages behavioral science approaches and Machine Learning (ML) to facilitate adherence to OIT treatments, dosing management, treatment personalization, and patient-clinician communication. The specific work will involve: 1) Developing, training and evaluating a series of proprietary ML algorithms capable of providing new insights and predicting events; 2) Developing and testing other software assets required for the completeness of the platform (mobile app, HIPAA-compliant database and API to integrate with 3rd-party systems); and 3) validating the system in real experiences with FA patients.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.