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Principal Investigator: Yong Chen
Organization: COLUMBIA UNIVERSITY HEALTH SCIENCES
Fiscal Year: 2024
Award: $676,523
Funding agency: National Library of Medicine
SUMMARY
Evidence-based medicine faces increasingly mounting challenges. With the explosively growing scientific
literature, it will be harder than ever to identify the best evidence available, especially given the large volume of
non-traditional and emerging sources of evidence: e.g., evidence derived from trial registries and data
repositories; observational datasets; publications without peer review; and scientific blogging. Individual studies
using conventional methods for evidence generation, especially randomized controlled trials, may be significantly
flawed in their planning, conduct, analysis, or reporting, resulting in ethical violations, wasted scientific resources,
and dissemination of misinformation with subsequent health harm. Furthermore, a new randomized controlled
trial should be initiated or interpreted in the context of the existing evidence. However, clinical evidence
extraction, appraisal, and aggregation remain laborious human tasks given its free-text format. To support
evidence-based research so that new research hypothesis selection and testing can be well-grounded on the
existing scientific literature and existing evidence can be easily accessible and computable to researchers,
patients, or clinicians, we will develop novel, scalable, and generalizable methods for clinical evidence extraction
and appraisal so that we can help the public identify reliable evidence easily. We will contribute computable
evidence representations and accompanying natural language processing pipelines, achieving symbiosis
between the two to support core tasks for evidence-based medicine, such as faceted evidence retrieval (e.g.,
“retrieve all the randomized controlled trials publications about the efficacy of HCQ on severe COVID-19 patients,
with each study having a sample size over 200”), extraction and representation of clinical findings (e.g., “HCQ
for people infected with COVID-19 has little or no effect on the risk of death, and probably no effect on
progression to mechanical ventilation”), and evidence quality ranking and biases detection.
Therefore, we propose four specific aims:
Aim 1. — Represent and extract Population, Intervention, Comparison, and Outcome (PICO) information.
Aim 2. — Represent and extract clinical findings and their metadata relevant for evidence quality ranking
and study biases detection.
Aim 3. — Develop and validate an extensible living clinical evidence knowledge graph based on the FAIR
principles.
Aim 4. — Develop and validate an Augmented Intelligence (AI) system for evidence appraisal.
INNOVATION There is no scalable and generalizable informatics solution for literature-based, fine-grained
clinical evidence extraction and representation, evidence quality ranking, evidence biases detection, and user-
augmented clinical evidence aggregation and appraisal. ClinEX will be the first solution to achieve these goals.
Terms: <Adoption><Benchmarking><Best Practice Analysis><COVID crisis><COVID epidemic><COVID infected patient><COVID pandemic><COVID patient><COVID positive patient><COVID-19 crisis><COVID-19 epidemic><COVID-19 era><COVID-19 global health crisis><COVID-19 global pandemic><COVID-19 health crisis><COVID-19 infected patient><COVID-19 infection><COVID-19 pandemic><COVID-19 patient><COVID-19 period><COVID-19 positive patient><COVID-19 public health crisis><COVID-19 virus infection><COVID-19 years><COVID19 infection><COVID19 patient><COVID19 positive patient><Clinical><Clinical Research><Clinical Study><Clinical Trials><Collaborations><Data><Data Banks><Data Bases><Data Set><Databanks><Databases><Detection><Eligibility><Eligibility Determination><Engineering><Ethics><Evaluation><Evidence Based Medicine><Expert Systems><FAIR data><FAIR guiding principles><FAIR principles><Face><Findable, Accessible, Interoperable and Re-usable><Findable, Accessible, Interoperable, and Reusable><Generations><Goals><Grain><Health><Healthcare><Human><Individual><Informatics><Information Retrieval><Information extraction><Intelligence><Intelligent systems><Intervention><Intervention Strategies><Investigators><Knowledge><Link><Literature><Measures><Mechanical ventilation><Meta-Analysis><Metadata><Methods><Misinformation><Modeling><Modern Man><NLP pipeline><Natural Language Processing><Natural Language Processing pipeline><Outcome><Patients><Peer Review><Persons><Policy Maker><Population><Probability><Protocol Screening><PubMed><Public Health><Publications><Publishing><Qualifying><Randomized, Controlled Trials><Registries><Reporting><Research><Research Design><Research Personnel><Research Resources><Researchers><Resources><Retrieval><SARS-CoV-2 epidemic><SARS-CoV-2 global health crisis><SARS-CoV-2 global pandemic><SARS-CoV-2 infected patient><SARS-CoV-2 infection><SARS-CoV-2 pandemic><SARS-CoV-2 patient><SARS-CoV-2 positive patient><SARS-CoV2 infection><SARS-coronavirus-2 epidemic><SARS-coronavirus-2 pandemic><Sample Size><Science><Scientific Publication><Severe Acute Respiratory Syndrome CoV 2 epidemic><Severe Acute Respiratory Syndrome CoV 2 pandemic><Severe acute respiratory syndrome coronavirus 2 epidemic><Severe acute respiratory syndrome coronavirus 2 infection><Severe acute respiratory syndrome coronavirus 2 pandemic><Source><Study Type><Symbiosis><System><Technology><Testing><Text><Trust><Update><Work><augmented intelligence><base><bases><benchmark><clinical relevance><clinical trial protocol><clinically relevant><cognitive task><cohort><commensalism><computable knowledge><coronavirus disease 2019 crisis><coronavirus disease 2019 epidemic><coronavirus disease 2019 global health crisis><coronavirus disease 2019 global pandemic><coronavirus disease 2019 health crisis><coronavirus disease 2019 infected patient><coronavirus disease 2019 infection><coronavirus disease 2019 pandemic><coronavirus disease 2019 patient><coronavirus disease 2019 positive patient><coronavirus disease 2019 public health crisis><coronavirus disease crisis><coronavirus disease epidemic><coronavirus disease infected patient><coronavirus disease pandemic><coronavirus disease patient><coronavirus disease positive patient><coronavirus disease-19 global pandemic><coronavirus disease-19 pandemic><coronavirus disease-19 patient><coronavirus patient><data base><data depository><data re-use><data repository><data reuse><data set repository><data sharing><data translator><dataset repository><death risk><deep learning><deep learning method><deep learning strategy><design><designing><drug repositioning><drug repurposing><ethical><evidence base><experience><explainable AI><explainable artificial intelligence><faces><facial><fitness><graph knowledge base><graph knowledgebase><health care><human study><improved><infected with COVID-19><infected with COVID19><infected with SARS-CoV-2><infected with SARS-CoV2><infected with coronavirus disease 2019><infected with severe acute respiratory syndrome coronavirus 2><interoperability><interpretable AI><interpretable artificial intelligence><interventional strategy><knowledge graph><life-threatening COVID><life-threatening COVID-19><life-threatening SARS-CoV-2><life-threatening coronavirus disease><life-threatening coronavirus disease 2019><life-threatening severe acute respiratory syndrome coronavirus 2><mechanical respiratory assist><mechanically ventilated><meta data><mortality risk><natural language understanding><novel><patient infected with COVID><patient infected with COVID-19><patient infected with SARS-CoV-2><patient infected with coronavirus disease><patient infected with coronavirus disease 2019><patient infected with severe acute respiratory syndrome coronavirus 2><patient with COVID><patient with COVID-19><patient with COVID19><patient with SARS-CoV-2><patient with coronavirus disease><patient with coronavirus disease 2019><patient with severe acute respiratory distress syndrome coronavirus 2><randomized control trial><repurposing agent><repurposing medication><serious COVID><serious COVID-19><serious SARS-CoV-2><serious coronavirus disease><serious coronavirus disease 2019><serious severe acute respiratory syndrome coronavirus 2><severe COVID><severe COVID-19><severe COVID19><severe SARS-CoV-2><severe acute respiratory syndrome coronavirus 2 global health crisis><severe acute respiratory syndrome coronavirus 2 global pandemic><severe acute respiratory syndrome coronavirus 2 infected patient><severe acute respiratory syndrome coronavirus 2 patient><severe acute respiratory syndrome coronavirus 2 positive patient><severe coronavirus disease><severe coronavirus disease 19><severe coronavirus disease 2019><severe severe acute respiratory syndrome coronavirus 2><student mentoring><student-led learning><study characteristics><study design><systematic review><task analysis><user centered design><wasting>