I-Corps: A machine learning model based on neural networks trained to recognize correlations and patterns that indicates possible medical complications

这个I-Corps项目的更广泛影响/商业潜力是开发一个健康和保健软件应用程序，支持患有糖尿病前期/糖尿病、胆固醇、高血压、不孕症和肥胖症等慢性病的个人。.许多人有问题，偏好和健康问题，这些问题没有得到常规初级保健医生（PCP）护理的充分解决。基于算法建议，拟议的技术旨在将用户与其他医疗保健提供者联系起来，这些提供者在治疗睡眠障碍，压力管理，哺乳，心理健康，营养和骨盆底治疗师等问题方面具有专业知识。 目前，患者依赖于他们的PCP/OBGYN的建议和转介来了解这些类型的护理。 此外，公司或保险公司可能会提供拟议的产品作为其福利计划的一部分，类似于戒烟或压力管理的预防计划。核心人工智能算法也可以应用于其他医疗保健领域。这个I-Corps项目是基于开发一套包含机器学习模型的算法。这些模型经过训练，可以根据电子健康记录（例如基因图谱）和物联网传感器数据（例如氧气水平、心率等）等输入来识别可能指示可能的医疗并发症的相关性和模式。研究的重点是探索这种传感器融合方法的变革潜力，以及用户输入的必要样本量，以制定风险评分并预测基于证据的医疗护理途径，特别是针对孕产妇/产后用户。虽然仍然认识到由于数据集“盲点”而产生的潜在有害建议，例如在医疗保健中的其他人工智能应用中遇到的建议，但这些发现可以通过用户友好的“轻推”来确定要提供给用户的最佳建议，从而利用行为心理学的进步。优先考虑隐私，数据收集的方法将依赖于用户选择同意和专有的端到端静默身份验证机制，而不是更容易受到黑客攻击和特权升级的OAuth/RSA令牌。 在用户选择同意的情况下，原始患者数据以及个性化建议可以直接提供给提供者，以进一步告知护理。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 I-Corps project is the development of a health and wellness software application that supports individuals with chronic illnesses such as prediabetes/diabetes, cholesterol, hypertension, infertility and obesity among others. . Many individuals have questions, preferences, and health issues that are not adequately addressed by routine Primary Care Physician (PCP) care. Based on algorithmic recommendations, the proposed technology is designed to connect users with alternative health care providers who have expertise in treating concerns such as sleep disorders, stress management, lactation, mental health, nutrition, and pelvic floor therapists. Currently, patients rely on recommendations and referrals from their PCP/OBGYN for knowledge about these types of care. In addition, corporations or insurance companies may offer the proposed product as part of their benefits packages similar to preventative programs in tobacco cessation or stress management. The core AI algorithms also may be applied to other healthcare sectors.This I-Corps project is based on the development of a set of algorithms comprising machine learning models. The models are trained to recognize correlations and patterns that could indicate possible medical complications, based on inputs such as electronic health records (e.g. genetic profiles) and IoT sensor data (e.g. oxygen level, heart rate etc.). The research focus is to explore the transformative potential of this sensor-fusion approach, with the requisite sample size of user inputs, to develop risk scores and predict evidence-based medical care pathways, specifically for maternal/post-partum users. While remaining cognizant of potential harmful recommendations generated because of dataset "blind-spots" such as those encountered in other AI applications in healthcare, these findings could then determine optimal recommendations to be delivered to users through user-friendly "nudges" leveraging advancements in behavioral psychology. Prioritizing privacy, the method of data collection would rely on user opt-in consent and a proprietary end-to-end silent authentication mechanism instead of OAuth/RSA tokens that are more vulnerable to hacking and privilege escalation. With a user's opt-in consent, raw patient data as well personalized recommendations could be made available directly to a provider to further inform care.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.