ACCELERATED RWE Lung Cancer AML Bladder Breast Gastric-Esophageal Lung Liver Melanoma Pancreatic Prostate Renal All Other Cancers PRECISE INSIGHTS Advanced Data and AI for Lung Cancer On-demand access to the largest and most sophisticated oncology data for accelerated RWE and improved patient outcomes. See some of the 1000s of data elements we offer: Structured Fields Request a Demo LABS VISITS OBSERVATIONS PROCEDURES MEDICATIONS DIAGNOSES Expert Abstraction Request a Demo FULL NGS PANEL DISEASE PROGRESSION ADVERSE EVENTS HISTOLOGY Enriched Data Request a Demo COST & UTILIZATION SAFETY, COMORBIDITIES SPECIALTY PHARMACY & HUB PAYER & FORMULARY SOCIAL DETERMINANTS AI-Enriched Insights Request a Demo NSCLC HISTOLOGY CLASSIFIER CANCER SUBTYPE ECOG METASTATIC STATUS DATE OF INITIAL DX EGFR STATUS LINE OF THERAPY PATIENT ADHERENCE PATIENT ACQUISITION LUNG CANCER Research Studies Our scientists regularly publish leading RWE studies in the fields of clinical development and health economics and outcomes research. Impute Subtype Predict Slow Progressors LUNG CANCER RESEARCH STUDY Impute Subtype Cancer subtypes are rarely captured in structured EMR data but are important for clinical outcomes and cost analyses. ConcertAI developed an AI model to identify NSCLC patients from a heterogeneous cohort of lung cancer patients. See Full Study Methods Machine Learning Model ANALYSIS Treatment patterns Clinical effectiveness Progression-free survival Overall survival PATIENT FEATURES 0 Lab Tests Staging Information Surgery Information Medications Administered Results 56,748 Patients labeled as NSCLC or not-NSCLC based on expert nurse abstraction THIS DATA WAS DIVIDED INTO THREE SETS: 0% Train 0% Test 0% Validate THE MODEL IMPROVED ACCURACY: 0% Test machine learning model 0% Best-performing rules-based model THE MODEL HAD STRONG RELEVANCE SCORES: 0% Precision 0% Recall Conclusion We can develop and validate reliable machine learning models to label lung cancer subtypes. This could save substantial time and effort compared to expert manual curation. Research for ISPOR, 2020 See Full Study LUNG CANCER RESEARCH STUDY Predict Slow Progressors Few clinical characteristics of patient with exceptional response to existing cancer therapies are known. ConcertAI trained a deep learning model to predict slow progression, a proxy, in aNSCLC in the second line setting. See Full Study Methods Machine Learning Model PATIENT CRITERIA Pathologically confirmed aNSCLC without other primary cancer Dx Started 2nd-line therapy between 2013 and 2017 Labeled slow progressors if no evidence of progression or death within 180 days of index Results 2,205 Patients met selection criteria of the study PATIENTS LABELED SLOW PROGRESSORS: 0% THE MODEL IMPROVED ACCURACY: 0% Test machine learning model 0% Best-performing logistic regression model THE MODEL HAD STRONG RELEVANCE SCORES: 0% Precision 0% Recall Conclusion This deep learning model may be useful in discovering novel drivers of favorable response. Research for ASCO, 2020 See Full Study Use Cases Commercial Solutions Study Design Regulatory Use External Control Arms AML Bladder Breast Gastric-Esophageal Lung Liver Melanoma Pancreatic Prostate Renal All Other Cancers Products & Services Data Products Predictive Patient STAR SPRINT Services Research Publications ERACE Resources Content Webinars and Events News Blog About Us Our Team Careers Investors Contact Us Request a Demo © 2017-2021 ConcertAI. All Rights Reserved. ConcertAI is a SymphonyAI company. Privacy Policy Terms of Use Privacy Policy Accept