# ClinEX - Clinical Evidence Extraction, Representation, and Appraisal

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2024 · $676,523

## Abstract

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.

## Key facts

- **NIH application ID:** 10931597
- **Project number:** 5R01LM014344-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Yong Chen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $676,523
- **Award type:** 5
- **Project period:** 2023-09-20 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10931597

## Citation

> US National Institutes of Health, RePORTER application 10931597, ClinEX - Clinical Evidence Extraction, Representation, and Appraisal (5R01LM014344-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10931597. Licensed CC0.

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