CAREER: A systems engineering approach to elucidate and treat multi-factorial pathology

职业：阐明和治疗多因素病理学的系统工程方法

• 批准号: 1944247
• 负责人: Cassie Mitchell
• 金额: $ 53.37万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1944247&HistoricalAwards=false
• 关键词: CAREER; systems; engineering; approach; elucidate

Millions of patients suffer worldwide from multi-factorial disease, which is a disease with no single cause but rather numerous contributing factors. Due to their complex nature, most multi-factorial diseases are currently incurable. Examples include the devastating neurodegenerative diseases of Alzheimer’s Dementia (AD), frontotemporal dementia (FTD), and Amyotrophic Lateral Sclerosis (ALS), which all impact brain function and the ability to perform normal daily tasks. Effectively measuring multiple simultaneous contributing factors throughout the disease course is extremely challenging in a traditional lab or clinical setting. The goal of this CAREER project is to develop new complex computer models that integrate and simultaneously analyze data from thousands of studies examining individual disease factors measured in the lab or clinic. The developed computer models prioritize the most promising factors and develop optimal combination treatment strategies. Computer prioritization increases the likelihood of clinical trial success and expedites the rate of new treatment availability to patients. While this project focuses on predicting treatments for AD, FTD, and ALS, the developed new technology can be applied to numerous other multi-factorial diseases. Educational activities for this project focus on undergraduate research internship curricula to increase opportunities; professional mentoring of students with disabilities; integrated advocacy of patients with AD, FTD, and ALS through local and national organizations; and improved collegiate education for neuroengineering via development of a new integrative class that integrates therapy design with medical school lectures on clinical neurologic disease. In addition to graduate research assistants, this project is estimated to provide STEM research internships for about 100 undergraduates and high school interns with an emphasis on under-represented groups.The investigator’s long-term goal is to use “pathology dynamics” (a branch of pathophysiology that deals with the motion, equilibrium, or homeostasis of physiological systems under the action of pathological forces) to enhance predictive medicine, whose primary purpose is to improve, expedite and personalize healthcare by developing computer models that forecast disease progression and treatment response. Towards this goal, the goal of this CAREER project is to construct new literature mining and predictive medicine models that leverage pathology dynamics to tackle multi-factorial disease(s). Most multi-factorial diseases are intractable and not responsive to traditional therapeutic approaches. The project’s driving hypothesis is that pathology dynamics is the key to unlocking unique signatures that can differentiate a spectrum of multi-factorial diseases that share similar symptoms, etiology, and biomarkers, but are currently clinical “diagnoses of exclusion” due to the lack of sensitive and specific clinical diagnostic tests. Three multi-factorial neuropathology test cases--Alzheimer’s Disease (AD), Amyotrophic Lateral Sclerosis (ALS), and frontotemporal dementia (FTD)--will be used to characterize the ability of pathology dynamics-based models to improve diagnostic, prognostic, and therapeutic prediction. The Research Plan is organized under three Aims. The FIRST Aim is to construct databases to capture, quantify, and aggregate entire fields’ literature. The investigator’s optimized student-driven assembly line (a hierarchy of high school and undergraduate students trained for biocuration tasks) will fully recapture journal article data along with key experimental methods/protocols, which enable meaningful data aggregation and analysis. The assembly line will first complete full published preclinical data recapture and corresponding databases for ALS, AD, and FTD (approximately 40,000 articles), which will be followed by construction of integrative clinical databases that consist of de-identified patient data for AD, ALS, and FTD. Efforts will also be made to develop and integrate additional biocuration automation for full data recapture with a goal of increasing biocuration automation from 40% to 75%. The SECOND Aim is to develop protocols for literature relationship extraction and ranking. Text mining with semantic inference networks will be used to identify multi-scalar relationships from 30+ million PubMed articles using the United Medical Language System ontology for keyword categorization and adapted unsupervised rank aggregation to prioritize relationships of interest. The THIRD Aim is to construct “pathology dynamics” models for the multi-factorial diseases using the data curated in Aim 1 and the relationships identified and ranked in Aim 2. Unsupervised models will be constructed for pathology dynamics phenotyping and supervised machine learning models will be constructed for diagnostic and therapeutic prediction. The models will be used in comparing rankings of literature relationships to experimentally measured relationships. In summary, the deliverables of this project include: novel multi-scalar databases for full curation of the AD, ALS, and FTD corpuses; new biocuration automation and relationship-based literature mining technology; and de novo systems-dynamics based preclinical and clinical predictive medicine models of AD, ALS, and FTD that can be used to predict etiology, diagnosis, treatment, and prognosis.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.

全世界数以百万计的患者患有多因素疾病，这是一种没有单一原因而是众多致病因素的疾病。由于其复杂的性质，大多数多因素疾病目前是无法治愈的。例如，阿尔茨海默氏症(AD)、额颞叶痴呆(FTD)和肌萎缩侧索硬化症(ALS)等毁灭性的神经退行性疾病都会影响大脑功能和执行正常日常工作的能力。在传统的实验室或临床环境中，有效地测量整个疾病过程中的多个同时影响因素是非常具有挑战性的。这个职业项目的目标是开发新的复杂的计算机模型，集成并同时分析来自数千项研究的数据，这些研究检查在实验室或临床中测量的单个疾病因素。开发的计算机模型优先考虑最有希望的因素，并开发最佳组合治疗策略。计算机优先排序增加了临床试验成功的可能性，并加快了患者获得新治疗的速度。虽然这个项目的重点是预测AD、FTD和ALS的治疗方法，但开发的新技术可以应用于许多其他多因素疾病。该项目的教育活动侧重于本科生研究实习课程以增加机会；对残疾学生的专业指导；通过地方和国家组织对AD、FTD和ALS患者的综合宣传；以及通过开发一个新的综合课程来改进大学神经工程教育，该课程将治疗设计与医学院的临床神经疾病讲座相结合。除了研究生研究助理，该项目预计将为大约100名本科生和高中实习生提供STEM研究实习机会，重点是代表性不足的群体。研究人员的长期目标是使用“病理动力学”(病理生理学的一个分支，研究病理力量作用下生理系统的运动、平衡或动态平衡)来加强预测医学，其主要目的是通过开发预测疾病进展和治疗反应的计算机模型来改善、加快和个性化医疗保健。朝着这个目标，这个职业项目的目标是构建新的文献挖掘和预测医学模型，利用病理动力学来处理多因素疾病(S)。大多数多因素疾病是难治性的，对传统的治疗方法没有反应。该项目的驱动假说是，病理动力学是解锁独特特征的关键，这些特征可以区分一系列具有相似症状、病因学和生物标志物的多因素疾病，但由于缺乏敏感和具体的临床诊断测试，目前是临床“排除诊断”。三个多因素神经病理学测试案例--阿尔茨海默病(AD)、肌萎缩侧索硬化症(ALS)和额颞部痴呆(FTD)--将被用来表征基于病理动力学的模型改善诊断、预后和治疗预测的能力。研究计划是在三个目标下组织的。第一个目标是构建数据库，以捕获、量化和汇总整个领域的文献。研究人员优化的以学生为导向的流水线(为生物计算任务培训的高中生和本科生的层次结构)将完全重新获取期刊文章数据以及关键的实验方法/协议，从而实现有意义的数据聚合和分析。装配线将首先完成ALS、AD和FTD的完整发布的临床前数据重新捕获和相应的数据库(约40,000篇文章)，随后将构建整合的临床数据库，其中包括AD、ALS和FTD的未识别的患者数据。还将努力开发和整合更多的生物浓缩自动化，以全面重新获取数据，目标是将生物浓缩自动化从40%提高到75%。第二个目标是开发用于文献关系提取和排序的协议。结合语义推理网络的文本挖掘将用于识别3000多万篇PubMed文章中的多标量关系，使用联合医学语言系统本体进行关键词分类，并采用非监督等级聚合来确定感兴趣的关系的优先级。第三个目标是利用目标1中整理的数据和目标2中识别和排序的关系，构建多因素疾病的“病理动力学”模型。将构建用于病理动力学表型的非监督模型，将构建用于诊断和治疗预测的有监督机器学习模型。这些模型将被用来比较文学关系的排名和实验测量的关系。总而言之，该项目的成果包括：用于全面管理AD、ALS和FTD语料库的新的多标量数据库；新的生物定位自动化和基于关系的文献挖掘技术；以及Dnevo Systems-基于动力学的AD、ALS和FTD的临床前和临床预测医学模型，可用于预测病因、诊断、治疗和预后。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables.

SeizFt：使用可穿戴设备进行癫痫发作检测的可解释机器学习。

• DOI: 10.3390/bioengineering10080918
• 发表时间: 2023-08-02
• 期刊: BIOENGINEERING-BASEL
• 影响因子: 4.6
• 作者: Al-Hussaini, Irfan;Mitchell, Cassie S.
• 通讯作者: Mitchell, Cassie S.

sEBM: scaling Event Based Models to predict disease progression via implicit biomarker

sEBM：扩展基于事件的模型以通过隐式生物标志物预测疾病进展

• 发表时间: 2023
• 期刊: Information processing in medical imaging
• 作者: Tandon, R.

Zero-Shot Information Extraction for Clinical Meta-Analysis using Large Language Models

• DOI: 10.18653/v1/2023.bionlp-1.37
• 发表时间: 2023
• 作者: David Kartchner;Selvi Ramalingam;Irfan Al-Hussaini;Olivia Kronick;Cassie S. Mitchell

CCS Explorer: Relevance Prediction, Extractive Summarization, and Named Entity Recognition from Clinical Cohort Studies

• DOI: 10.1109/bigdata55660.2022.10020807
• 发表时间: 2022-11
• 期刊: 2022 IEEE International Conference on Big Data (Big Data)
• 作者: Irfan Al-Hussaini;D. An;Albert Lee;Sarah Bi;Cassie S. Mitchell

Pathology Dynamics at BioLaySumm: the trade-off between Readability, Relevance, and Factuality in Lay Summarization

• DOI: 10.18653/v1/2023.bionlp-1.63
• 发表时间: 2023
• 作者: Irfan Al-Hussaini;Austin Wu;Cassie S. Mitchell