explainable AI, Data Analytics and Industrial Engineering Methods for Primary Care

为了应对慢性病的挑战，申请人和他的团队引入了一种新的范式，在本文中被称为预测+预防，或P+ P。科学事实是，如果采取适当的生活方式干预措施，大多数慢性疾病实际上是可以预防的。正如在过去二十年中著名医学期刊上的一些文章所报道的那样，慢性病可预防性背后的科学证据是压倒性的。至于历史事实，这是我们从医学（拉丁文medicina，意思是治愈的艺术）传统实践中继承的一个几百年的范例的结果：一般来说，病人需要等到他们生病才能去看医生，然后希望治愈他们。现在，随着大量电子病历数据的可用性，以及信息技术和数据分析的巨大进步，患者不需要等到生病。现在可以大规模地预测一个人在患上慢性病之前很久就患上这种疾病的风险。因此，“预测”一词出现在“预测+预防”的表达中。 P+P模式的实施将包括：（1）通过使用EMR数据预测人们患慢性病的可能性来识别高风险患者，然后（2）将高风险患者的列表传输给第三方，例如远程医疗提供商或人力资源公司，然后他们会接触这些患者（通过电子邮件、电话、社交媒体、面对面或任何其他合适的方法）与他们合作，改善他们的生活方式，使他们远离慢性病的发作。 在本建议中，研究将主要集中在P+P范式的预测方面和预防的有限方面。更具体地说，拟议的研究项目将涉及以下5个项目，使用人工智能方法和数据分析（例如，前三个项目涉及决策树、有记忆和无记忆的马尔可夫链、贝叶斯网、混合模型、EM算法、核函数和支持向量机、蒙特卡洛模拟、无监督学习等），后两个项目涉及工业工程方法（决策理论、工程经济学）： 1-预测项目1 -单一慢性病病例 2-预测项目2 -慢性疾病的晚期和并发症 3-预测项目3 -患有多种合并症的患者 4-决策科学项目DS -评估医生及其工作人员收集的生物标志物信息的价值。 5-工程经济学项目EE -预防经济学 这项研究将涉及高素质的人员（HQP），即3名博士生，3名硕士生和1名实验室技术人员。

To tackle the chronic illnesses challenge, the applicant and his team introduced a new paradigm, referred to herein as Predict + Prevent, or P+P. Two important facts underlie this paradigm --- one is scientific and the other is historic. The scientific fact is that most chronic illnesses are actually preventable, if the appropriate lifestyle interventions are applied. The scientific evidence behind the preventability of chronic illnesses is overwhelming, as was reported during the last two decades in a number of articles in renowned medical journals. As for the historic fact, it is the result of a centuries-old paradigm we inherited from the way medicine (from Latin medicina', which means the art of healing') has traditionally been practiced: in general, patients need to wait until they get sick to go see their doctors who will then hopefully heal them. Now, with the availability of large sets of Electronic Medical Records data, and the tremendous advancements in information technology and data analytics, patients don't need to wait until they become sick. It is now possible to predict, at scale, the risk for a person to get ill with a chronic disease long before she contracts this disease. Hence the term “Predict” in the expression Predict + Prevent. The implementation of the P+P paradigm would consist of: (1) identify the high-risk patients, by predicting the likelihood for people to develop chronic diseases using EMR data, and then, (2) transfer lists of high-risk patients to a third-party, such as telehealth providers or human resources companies, who would then reach out to these patients (by e-mail, phone, social media, in-person or any other suitable method) and work with them on improving their lifestyle, to move them away from the onset of chronic diseases. In this proposal, the research will be focused mostly on the prediction side of the P+P paradigm and on limited aspects of prevention. More specifically, the proposed research project will address the following 5 projects listed below, using artificial intelligence methods and data analytics (e.g., decision trees, Markov chains with and without memory, Bayesian nets, mixture models, the EM algorithm, kernels and support vector machines, Monte Carlo simulations, unsupervised learning, and so on) for the first three projects, and industrial engineering methods (decision theory, engineering economics) for the last two projects: 1- Project on Prediction 1 - The single chronic disease cases 2- Project on Prediction 2 - Advanced stages and complications of a chronic disease 3- Project on Prediction 3 - Patients with multiple comorbidities 4- Project on Decision Sciences DS - Assessing the value of biomarkers information collected by doctors and their staff members. 5- Project on Engineering Economics EE - The economics of prevention This research will involve highly qualified personnel (HQP), namely 3 PhD students, 3 Master's students and 1 Lab technician.