Assessing the feasibility and external validity of natural language processing-extracted data for advanced lung cancer patients.

Background: Manual extraction of real-world clinical data for research can be time-consuming and prone to error. We assessed the feasibility of using natural language processing (NLP), an AI technique, to automate data extraction for patients with advanced lung cancer (aLC). We assessed the external validity of our NLP-extracted data by comparing our findings to those reported in the literature.

The relationship among vedolizumab drug concentrations, biomarkers of inflammation, and clinical outcomes in a Canadian real-world study.

Background And Aims: Therapeutic drug monitoring is used to optimize anti-tumour necrosis factor biologic effectiveness in inflammatory bowel disease, but its role with other biological classes is unclear. This study explores relationships between post-induction vedolizumab trough concentrations and biochemical outcomes in a real-world study of individuals with inflammatory bowel disease.

Methods: This retrospective analysis of data from a national patient support program between 2018 and 2020, included 436 individuals with Crohn's disease or ulcerative colitis receiving vedolizumab.

Breast cancer learning health system: Patient information from a data and analytics platform characterizes care provided.

Purpose: In a learning health system (LHS), data gathered from clinical practice informs care and scientific investigation. To demonstrate how a novel data and analytics platform can enable an LHS at a regional cancer center by characterizing the care provided to breast cancer patients.

Methods: Socioeconomic information, tumor characteristics, treatments and outcomes were extracted from the platform and combined to characterize the patient population and their clinical course.

Oropharyngeal Cancer Staging Health Record Extraction Using Artificial Intelligence.

Importance: Accurate, timely, and cost-effective methods for staging oropharyngeal cancers are crucial for patient prognosis and treatment decisions, but staging documentation is often inaccurate or incomplete. With the emergence of artificial intelligence in medicine, data abstraction may be associated with reduced costs but increased efficiency and accuracy of cancer staging.

Objective: To evaluate an algorithm using an artificial intelligence engine capable of extracting essential information from medical records of patients with oropharyngeal cancer and assigning tumor, nodal, and metastatic stages according to American Joint Committee on Cancer eighth edition guidelines.

Real-World Outcomes of Patients with Advanced Epidermal Growth Factor Receptor-Mutated Non-Small Cell Lung Cancer in Canada Using Data Extracted by Large Language Model-Based Artificial Intelligence.

Real-world evidence for patients with advanced -mutated non-small cell lung cancer (NSCLC) in Canada is limited. This study's objective was to use previously validated DARWEN artificial intelligence (AI) to extract data from electronic heath records of patients with non-squamous NSCLC at University Health Network (UHN) to describe mutation prevalence, treatment patterns, and outcomes. Of 2154 patients with NSCLC, 613 had advanced disease.