PharML 2021 (Machine Learning for Pharma and Healthcare Applications) at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021)
FDA has been promoting enrollment practices that could enhance the diversity of clinical trial populations, through broadening eligibility criteria. However, how to broaden eligibility remains a significant challenge. We propose an AI approach to Cohort Optimization (AICO) through transformer-based natural language processing of the eligibility criteria and evaluation of the criteria using real-world data. The method can extract common eligibility criteria variables from a large set of relevant trials and measure the generalizability of trial designs to real-world patients. It overcomes the scalability limits of existing manual methods and enables rapid simulation of eligibility criteria design for a disease of interest. A case study on breast cancer trial design demonstrates the utility of the method in improving trial generalizability.
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Clinical trials are essential for drug development but are extremely expensive and time-consuming to conduct. It is beneficial to study similar historical trials when designing a clinical trial. However, lengthy trial documents and lack of labeled data make trial similarity search difficult. We propose a zero-shot clinical trial retrieval method, Trial2Vec, which learns through self-supervision without annotating similar clinical trials. Specifically, the meta-structure of trial documents (e.g., title, eligibility criteria, target disease) along with clinical knowledge (e.g., UMLS knowledge ba…
Data Science and AI, Novartis（诺华数据科学与人工智能部门）；Global Drug Development, Novartis Pharma AG（诺华制药全球药物开发部）
Natural language processing (NLP) of clinical trial documents can be useful in new trial design. Here we identify entity types relevant to clinical trial design and propose a framework called CT-BERT for information extraction from clinical trial text. We trained named entity recognition (NER) models to extract eligibility criteria entities by fine-tuning a set of pre-trained BERT models. We then compared the performance of CT-BERT with recent baseline methods including attention-based BiLSTM and Criteria2Query. The results demonstrate the superiority of CT-BERT in clinical trial NLP.
University of Washington（华盛顿大学）；Northeastern University（东北大学）；George Mason University（乔治梅森大学）
Identifying cohorts of patients based on eligibility criteria such as medical conditions, procedures, and medication use is critical to recruitment for clinical trials. Such criteria are often most naturally described in free-text, using language familiar to clinicians and researchers. In order to identify potential participants at scale, these criteria must first be translated into queries on clinical databases, which can be labor-intensive and error-prone. Natural language processing (NLP) methods offer a potential means of such conversion into database queries automatically. However they mu…
School of Medicine, Tsinghua University（清华大学医学院）；Alibaba DAMO Academy（阿里巴巴达摩院）；Alibaba Group（阿里巴巴集团）；Indiana University Bloomington（印第安纳大学伯明顿分校）
Clinical trials provide essential guidance for practicing Evidence-Based Medicine, though often accompanying with unendurable costs and risks. To optimize the design of clinical trials, we introduce a novel Clinical Trial Result Prediction (CTRP) task. In the CTRP framework, a model takes a PICO-formatted clinical trial proposal with its background as input and predicts the result, i.e. how the Intervention group compares with the Comparison group in terms of the measured Outcome in the studied Population. While structured clinical evidence is prohibitively expensive for manual collection, we …
Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation
Institute for Clarity in Documentation（清晰文档研究所）；Inria Paris-Rocquencourt（巴黎- Rocquencourt 国家信息与自动化研究所）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒实验室）；University of Chicago（芝加哥大学）；Stanford University（斯坦福大学）；Purdue University（普渡大学）；University of Southern California（南加州大学）
Electronic health records (EHR) offer unprecedented opportunities for in-depth clinical phenotyping and prediction of clinical outcomes. Combining multiple data sources is crucial to generate a complete picture of disease prevalence, incidence and trajectories. The standard approach to combining clinical data involves collating clinical terms across different terminology systems using curated maps, which are often inaccurate and/or incomplete. Here, we propose sEHR-CE, a novel framework based on transformers to enable integrated phenotyping and analyses of heterogeneous clinical datasets witho…
Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
Clinical Cohort Studies (CCS), such as randomized clinical trials, are a great source of documented clinical research. Ideally, a clinical expert inspects these articles for exploratory analysis ranging from drug discovery for evaluating the efficacy of existing drugs in tackling emerging diseases to the first test of newly developed drugs. However, more than 100 articles are published daily on a single prevalent disease like COVID-19 in PubMed. As a result, it can take days for a physician to find articles and extract relevant information. Can we develop a system to sift through the long list…
COVID-19 clinical trial design is a critical task in developing therapeutics for the prevention and treatment of COVID-19. In this study, we apply a deep learning approach to extract eligibility criteria variables from COVID-19 trials to enable quantitative analysis of trial design and optimization. Specifically, we train attention-based bidirectional Long Short-Term Memory (Att-BiLSTM) models and use the optimal model to extract entities (i.e., variables) from the eligibility criteria of COVID-19 trials. We compare the performance of Att-BiLSTM with traditional ontology-based method. The resu…
Language Interaction Network for Clinical Trial Approval Estimation
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Carle Illinois College of Medicine, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校卡尔伊利诺伊医学院）
Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology
Microsoft Research（微软研究院）；Providence Health & Services（普罗维登斯医疗服务机构）；Earle A. Chiles Research Institute（厄尔·A·奇尔斯研究所）；Providence Cancer Institute（普罗维登斯癌症研究所）
Georgia Institute of Technology（佐治亚理工学院）；Booz Allen Hamilton（博思艾伦汉密尔顿公司）
Due to increased access to clinical trial outcomes and analysis, researchers and scientists are able to iterate or improve upon relevant approaches more effectively. However, the metrics and related results of clinical trials typically do not follow any standardization in their reports, making it more difficult for researchers to parse the results of different trials. The objective of this paper is to describe an automated method utilizing natural language processing in order to describe the probable core outcomes of clinical trials, in order to alleviate the issues around disparate clinical t…
Clinical trials offer a fundamental opportunity to discover new treatments and advance the medical knowledge. However, the uncertainty of the outcome of a trial can lead to unforeseen costs and setbacks. In this study, we propose a new method to predict the effectiveness of an intervention in a clinical trial. Our method relies on generating an informative summary from multiple documents available in the literature about the intervention under study. Specifically, our method first gathers all the abstracts of PubMed articles related to the intervention. Then, an evidence sentence, which convey…
Recommending Clinical Trials for Online Patient Cases using Artificial Intelligence
National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）；National Cancer Institute, National Institutes of Health（美国国立卫生研究院国家癌症研究所）
TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model
School of Public Health, Zhejiang University（浙江大学公共卫生学院）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
The University of Melbourne（墨尔本大学）；RMIT University（皇家墨尔本理工大学）
This paper describes the submissions of the Natural Language Processing (NLP) team from the Australian Research Council Industrial Transformation Training Centre (ITTC) for Cognitive Computing in Medical Technologies to the TREC 2021 Clinical Trials Track. The task focuses on the problem of matching eligible clinical trials to topics constituting a summary of a patient's admission notes. We explore different ways of representing trials and topics using NLP techniques, and then use a common retrieval model to generate the ranked list of relevant trials for each topic. The results from all our s…
Clinical trials predicate subject eligibility on a diversity of criteria ranging from patient demographics to food allergies. Trials post their requirements as semantically complex, unstructured free-text. Formalizing trial criteria to a computer-interpretable syntax would facilitate eligibility determination. In this paper, we investigate an information extraction (IE) approach for grounding criteria from trials in ClinicalTrials(dot)gov to a shared knowledge base. We frame the problem as a novel knowledge base population task, and implement a solution combining machine learning and context f…
Using Large Language Models to Generate Clinical Trial Tables and Figures
Novartis Pharmaceuticals Corporation（诺华制药公司）；School of Biomedical Informatics The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心生物医学信息学院）；Novartis Pharma AG（诺华制药公司）；The University of Texas MD Anderson Cancer Center（德克萨斯大学 MD 安德森癌症中心）
Sorbonne Université（索邦大学）；Inserm（法国国家健康与医学研究院）；Univ Paris 13（巴黎第十三大学）；AP-HP（巴黎公共医疗集团）；LIMSI（信息科学与技术实验室）；CNRS（法国国家科学研究中心）；Univ. Paris-Sud（巴黎第十一大学）；Université Paris-Saclay（巴黎萨克雷大学）；Mines ParisTech（巴黎高等矿业学校）；Hôpital Avicenne（阿维森纳医院）；Université Paris Descartes - Sorbonnes Paris Cité（巴黎笛卡尔大学 - 巴黎城市索邦大学）
Objective: Natural language processing can help minimize human intervention in identifying patients meeting eligibility criteria for clinical trials, but there is still a long way to go to obtain a general and systematic approach that is useful for researchers. We describe two methods taking a step in this direction and present their results obtained during the n2c2 challenge on cohort selection for clinical trials. Materials and Methods: The first method is a weakly supervised method using an unlabeled corpus (MIMIC) to build a silver standard, by producing semi-automatically a small and very…
AD-CDO: A Lightweight Ontology for Representing Eligibility Criteria in Alzheimer's Disease Clinical Trials
University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；Indiana University School of Medicine（印第安纳大学医学院）；Mayo Clinic in Florida（佛罗里达梅奥诊所）
