Background and Significance: Selecting cohorts for a clinical trial typically requires costly and time-consuming manual chart reviews resulting in poor participation. To help automate the process, National NLP Clinical Challenges (N2C2) conducted a shared challenge by defining 13 criteria for clinical trial cohort selection and by providing training and test datasets. This research was motivated by the N2C2 challenge.
Methods: We broke down the task into 13 independent subtasks corresponding to each criterion and implemented subtasks using rules or a supervised machine learning model. Each task critically depended on knowledge resources in the form of task-specific lexicons, for which we developed a novel model-driven approach. The approach allowed us to first expand the lexicon from a seed set and then remove noise from the list, thus improving the accuracy.
Results: Our system achieved an overall F measure of 0.9003 at the challenge, and was statistically tied for the first place out of 45 participants. The model-driven lexicon development and further debugging the rules/code on the training set improved overall F measure to 0.9140, overtaking the best numerical result at the challenge.
Discussion: Cohort selection, like phenotype extraction and classification, is amenable to rule-based or simple machine learning methods, however, the lexicons involved, such as medication names or medical terms referring to a medical problem, critically determine the overall accuracy. Automated lexicon development has the potential for scalability and accuracy.
An Open Natural Language Processing Development Framework for EHR-based Clinical Research: A case demonstration using the National COVID Cohort Collaborative (N3C)
Mayo Clinic（妙佑医疗国际）；Tufts Clinical and Translational Science Institute（塔夫茨临床与转化科学研究所）；Tufts Medical Center（塔夫茨医疗中心）；University of Kentucky（肯塔基大学）；University of Kansas Medical Center（堪萨斯大学医学中心）；University of Minnesota at Twin Cities（明尼苏达大学双城分校）；University of Florida（佛罗里达大学）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；University of Michigan Medical School（密歇根大学医学院）；Wake Forest Baptist Medical Center（维克森林浸信会医疗中心）；Johns Hopkins University（约翰斯·霍普金斯大学）；Stony Brook University（石溪大学）；Sage Bionetwork（Sage生物网络）
虽然我们关注临床自然语言处理（NLP）的最新进展，但我们可以注意到，由于透明度、可解释性和可用性有限，临床和转化研究社区对采用NLP模型存在一些阻力。在这项研究中，我们提出了一个开放的自然语言处理开发框架。我们通过为国家COVID队列协作（N3 C）实施NLP算法对其进行了评估。基于对从COVID-19相关临床记录中提取信息抽取的兴趣，我们的工作包括1）使用COVID-19体征和症状作为用例的开放数据注释过程，2）社区驱动的规则组成平台，以及3）合成文本数据生成工作流程，以生成用于信息抽取任务的文本，而不涉及人类受试者。语料库来自三个不同的机构（梅奥诊所，肯塔基大学，明尼苏达大学）的文本。金标准注释与一个单一的机构（梅奥）的规则进行了测试。这导致Mayo、Minnesota和Kentucky测试数据集的F分数分别为0.876、0.706和0.694。该研究作为N3 C NLP子组的联盟努力，证明了创建联合NLP算法开发和基准测试平台以加强多机构临床NLP研究和采用的可行性。虽然我们在这项工作中使用COVID-19作为用例，但我们的框架足够通用，可以应用于临床NLP的其他感兴趣领域。
Emulating Clinician Cognition via Self-Evolving Deep Clinical Research
Gaoling School of Artificial Intelligence（首都经济贸易大学人工智能学院）；Renmin University of China（中国人民大学）；Peking University Third Hospital（北京大学第三医院）；Baidu Inc.（百度公司）；Chinese PLA General Hospital（中国人民解放军总医院）；Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly（联合NTU-UBC老年积极生活方式卓越研究中心）；Nanyang Technological University（南洋理工大学）
The Smart Data Extractor, a Clinician Friendly Solution to Accelerate and Improve the Data Collection During Clinical Trials
Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；Imagine Institute（Imagine研究所）；Hôpital Universitaire Necker-Enfants malades（内克尔-Enfants malades大学医院）；APHP（巴黎公立医院集团）；Université de Paris Cité（巴黎西岱大学）
Cognitive modelling with multilayer networks: Insights, advancements and future challenges
University of Exeter（埃克塞特大学）；University of Pisa（比萨大学）；Institute of Information Science and Technologies, National Research Council（国家研究委员会信息科学与技术研究所）；University of Ghent（根特大学）；Technion, Israel Institute of Technology（以色列理工学院）；University of Kansas（堪萨斯大学）
Dementia-R1: Reinforced Pretraining and Reasoning from Unstructured Clinical Notes for Real-World Dementia Prognosis
Graduate School of AI, KAIST, Republic of Korea（韩国科学技术院人工智能研究生院）；Haeundae Paik Hospital, Inje University, Republic of Korea（韩国延世大学海云台医院）；Asan Medical Center, University of Ulsan College of Medicine, Republic of Korea（韩国釜山大学医学院阿桑医院）
