Organization
ConcertAI
17 abstracts
Abstract
Efficient site selection for clinical trials using simulated annealing.Org: ConcertAI, Cambridge, MA,
Abstract
Dynamic machine learning model to forecast patient availability for clinical trials.Org: ConcertAI, Cambridge University Hospitals NHS Foundation Trust, Bengaluru, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA,
Abstract
Assessment of electronic health record (EHR) –based machine learning (ML) in predicting risk of brain metastasis among patients with early-stage non–small-cell lung cancer (eNSCLC).Org: ConcertAI, Cambridge University Hospitals NHS Foundation Trust, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA,
Abstract
Enhancement in line of therapy (LoT) derivation from real-world data (RWD) from electronic health records (EHR) via integration of medical claims data.Org: ConcertAI,
Abstract
Development of natural language processing (NLP) models for extracting key features from unstructured notes to create real-world data (RWD) assets for clinical research at scale.Org: ConcertAI, Syneos Health, Merck & Co., Inc.,
Abstract
Electronic health record (EHR)-based machine learning (ML) to predict disease recurrence after surgical resection of early-stage non-small cell lung cancer (eNSCLC).Org: ConcertAI, Perelman School of Medicine, University of Pennsylvania,
Abstract
Evaluating clinical trial inclusion/exclusion criteria from claims using generative artificial intelligence.Org: ConcertAI, Perelman School of Medicine, University of Pennsylvania,
Abstract
Association between severe adverse event management and overall survival in patients treated with immune checkpoint inhibitor with advanced non–small-cell lung cancer.Org: Beaumont RCSI Cancer Center, Vanderbilt University Medical Center, ConcertAI, Bristol Myers Squibb, Bristol-Myers Squibb,
Abstract
Identification of biomarkers for early progression in muscle invasive bladder cancer (MIBC) using real-world data.Org: ConcertAI,
Abstract
Digital Trial Solutions eScreening: Software to rank patients by their predicted clinical trial eligibility using real-world data and machine learning.Org: ConcertAI, Cambridge, MA, Plymouth Meeting, PA,
Abstract
Site of metastasis (SoM) and its impact on clinical outcomes in 8 cancer cohorts.Org: ConcertAI, Bengaluru, Karnataka, Indiana University – Purdue University Indianapolis, Labcorp Drug Development Inc., Princeton, NJ,
Abstract
Real-world response endpoints in patients with mNSCLC treated with chemotherapy across real-world datasets.Org: Friends of Cancer Research, Washington, DC, American Society of Clinical Oncology, COTA Inc, Syapse, Tempus Labs, Inc.,
Abstract
A real-world study of US patients with metastatic ovarian, fallopian tube, and peritoneal cancer (mOFPC) using integrated electronic health records (EHR) and claims datasets.Org: ConcertAI, Labcorp Oncology, Duke University Medical Center, Duke Cancer Institute, Department of Pathology, Durham, NC, Labcorp Drug Development Inc., Princeton, NJ,
Abstract
Electronic health record (EHR) and genomics-based machine learning (ML) to predict therapeutic effectiveness among patients with hormone-receptor positive (HR)+/HER2- advanced breast cancer (aBC).Org: ConcertAI, Perelman School of Medicine, University of Pennsylvania,
Abstract
Treatment patterns and outcomes among locally advanced cervical cancer patients receiving concurrent chemoradiotherapy.Org: ConcertAI, Cambridge University Hospitals NHS Foundation Trust, MaaT Pharma, Merck & Co., Inc., Rahway, NJ,
Abstract
Real-world outcomes in patients (pts) with metastatic triple-negative breast cancer (mTNBC) treated with sacituzumab govitecan (SG) in 2L+ in the United States (US).Org: Gilead Sciences Europe Ltd., ConcertAI, Gilead Sciences Inc.,
Abstract
Biomarker testing and treatment patterns in US patients (pts) with advanced/metastatic non‑small cell lung cancer (NSCLC) harboring MET amplification.Org: West Cancer Center & Research Institute, Merck, ConcertAI,