AIMC Topic: Patient Selection
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ImpACT Project: Improving Access to Clinical Trials in Victoria, an Artificial Intelligence-Based Approach.
JCO clinical cancer informatics
Jan 9, 2025
PURPOSE: Enhancing the speed and efficiency of clinical trial recruitment is a key objective across international health systems. This study aimed to use artificial intelligence (AI) applied in the Victorian Cancer Registry (VCR), a population-based ...
Artificial Intelligence-Driven Patient Selection for Preoperative Portal Vein Embolization for Patients with Colorectal Cancer Liver Metastases.
Journal of vascular and interventional radiology : JVIR
Dec 3, 2024
PURPOSE: To develop a machine learning algorithm to improve hepatic resection selection for patients with metastatic colorectal cancer (CRC) by predicting post-portal vein embolization (PVE) outcomes.
Humans
Portal Vein
Patient-Specific Modeling
Liver Regeneration
Patient Selection
Hepatectomy
Adult
Embolization, Therapeutic
Colorectal Neoplasms
Middle Aged
Machine Learning
Male
Clinical Decision-Making
Liver Neoplasms
Decision Support Techniques
Female
Treatment Outcome
Retrospective Studies
Reproducibility of Results
Predictive Value of Tests
Aged
Metastatic Versus Localized Disease as Inclusion Criteria That Can Be Automatically Extracted From Randomized Controlled Trials Using Natural Language Processing.
JCO clinical cancer informatics
Nov 27, 2024
PURPOSE: Extracting inclusion and exclusion criteria in a structured, automated fashion remains a challenge to developing better search functionalities or automating systematic reviews of randomized controlled trials in oncology. The question "Did th...
Structural analysis and intelligent classification of clinical trial eligibility criteria based on deep learning and medical text mining.
Journal of biomedical informatics
Nov 26, 2024
OBJECTIVE: To enhance the efficiency, quality, and innovation capability of clinical trials, this paper introduces a novel model called CTEC-AC (Clinical Trial Eligibility Criteria Automatic Classification), aimed at structuring clinical trial eligib...
Harnessing explainable artificial intelligence for patient-to-clinical-trial matching: A proof-of-concept pilot study using phase I oncology trials.
PloS one
Oct 24, 2024
This study aims to develop explainable AI methods for matching patients with phase 1 oncology clinical trials using Natural Language Processing (NLP) techniques to address challenges in patient recruitment for improved efficiency in drug development....
Learning to match patients to clinical trials using large language models.
Journal of biomedical informatics
Oct 9, 2024
OBJECTIVE: This study investigates the use of Large Language Models (LLMs) for matching patients to clinical trials (CTs) within an information retrieval pipeline. Our objective is to enhance the process of patient-trial matching by leveraging the se...
A radiographic artificial intelligence tool to identify candidates suitable for partial knee arthroplasty.
Archives of orthopaedic and trauma surgery
Oct 3, 2024
INTRODUCTION: Knee osteoarthritis is a prevalent condition frequently necessitating knee replacement surgery, with demand projected to rise substantially. Partial knee arthroplasty (PKA) offers advantages over total knee arthroplasty (TKA), yet its u...
Enhancing Patient Selection in Sepsis Clinical Trials Design Through an AI Enrichment Strategy: Algorithm Development and Validation.
Journal of medical Internet research
Sep 4, 2024
BACKGROUND: Sepsis is a heterogeneous syndrome, and enrollment of more homogeneous patients is essential to improve the efficiency of clinical trials. Artificial intelligence (AI) has facilitated the identification of homogeneous subgroups, but how t...
Artificial intelligence-based model for the recurrence of hepatocellular carcinoma after liver transplantation.
Surgery
Aug 23, 2024
BACKGROUND: Artificial intelligence-based models might improve patient selection for liver transplantation in hepatocellular carcinoma. The objective of the current study was to develop artificial intelligence-based deep learning models and determine...
Recruitment in Appalachian, Rural and Older Adult Populations in an Artificial Intelligence World: Study Using Human-Mediated Follow-Up.
JMIR formative research
Aug 22, 2024
BACKGROUND: Participant recruitment in rural and hard-to-reach (HTR) populations can present unique challenges. These challenges are further exacerbated by the need for low-cost recruiting, which often leads to use of web-based recruitment methods (e...
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