scholarly journals Drug Adverse Event Detection in Health Plan Data Using the Gamma Poisson Shrinker and Comparison to the Tree-based Scan Statistic

Pharmaceutics ◽  
2013 ◽  
Vol 5 (4) ◽  
pp. 179-200 ◽  
Author(s):  
Jeffrey Brown ◽  
Kenneth Petronis ◽  
Andrew Bate ◽  
Fang Zhang ◽  
Inna Dashevsky ◽  
...  
Keyword(s):  
Adverse Event ◽  
Health Plan ◽  
Event Detection ◽  
Scan Statistic ◽  
Drug Adverse Event ◽  
Gamma Poisson Shrinker

Evaluation of Drug Adverse Event Intake and Reporting in a Medical Information Service

2011 ◽  
Vol 45 (6) ◽  
pp. 767-773 ◽  
Author(s):  
Pamela D. Allen ◽  
Robert J. Fuentes ◽  
Michael J. Hoopes ◽  
Greg Susla
Keyword(s):  
Adverse Event ◽  
Medical Information ◽  
Information Service ◽  
Drug Adverse Event

scholarly journals A novel method for drug-adverse event extraction using machine learning

2019 ◽  
Vol 17 ◽  
pp. 100190 ◽  
Author(s):  
Kajal Negi ◽  
Arun Pavuri ◽  
Ladle Patel ◽  
Chirag Jain
Keyword(s):  
Machine Learning ◽  
Adverse Event ◽  
Event Extraction ◽  
Drug Adverse Event ◽  
Novel Method

scholarly journals Deep Learning for Adverse Event Detection from Web Search

2020 ◽  
pp. 1-1
Author(s):  
Faizan Ahmad ◽  
Ahmed Abbasi ◽  
Brent Kitchens ◽  
Donald A Adjeroh ◽  
Daniel Zeng
Keyword(s):  
Adverse Event ◽  
Deep Learning ◽  
Event Detection ◽  
Web Search

Budgetary impact of adding ziv-aflibercept to a United States health plan formulary as a post-oxaliplatin biologic option for patients with metastatic colorectal cancer (mCRC).

2015 ◽  
Vol 33 (3_suppl) ◽  
pp. 765-765 ◽  
Author(s):  
Keith Huff ◽  
Edward Drea ◽  
Daniel Hennessy ◽  
Stephen F. Thompson ◽  
Ravinder Dhawan
Keyword(s):  
United States ◽  
Adverse Event ◽  
Health Plan ◽  
Treatment Options ◽  
Incidence Rates ◽  
Biologic Agents ◽  
Biologic Agent ◽  
Utilization Rate ◽  
Budgetary Impact ◽  
Number Of Patients

765 Background: To assess the economic impact of the availability of ziv-aflibercept for patients with mCRC following disease progression after oxaliplatin, a user-adaptable budget impact model was developed for payers, based on use of 5-fluorouracil, leucovorin and irinotecan (FOLFIRI) chemotherapy alone or combined with a biologic agent, including ziv-aflibercept. Methods: A Markov-like model simulated transition from progression-free survival (PFS) to progressive disease (PD) and death. Over the model time frame of 1 year, each 90-day cycle adjusted for the probability of patients experiencing PFS, PD or death. Treatment options were FOLFIRI alone or combined with a biologic agent, ziv-aflibercept, bevacizumab, cetuximab or panitumumab. April 2013 United States utilization rates were used as baseline, with biologic agent drug costs as per July 2014 Medicare Part B Drug Average Sales Price. PFS and overall survival were calculated from clinical trial data of biologic agents combined with chemotherapy. Adverse event cost calculations used documented incidence rates and published cost sources. Other costs were for biomarker testing, drug administration/monitoring and death/terminal care. Results: The estimated number of patients receiving FOLFIRI alone or with a biologic agent for mCRC was 57 per million-member health plan. Using April 2013 utilization rates (including ziv-aflibercept at 3.4%), estimated treatment costs were $5.259M, a per-member-per-month (PMPM) cost of $0.0438. Changes in utilization rates of biologic agents, including, for example, ziv-aflibercept at 20.1%, would decrease costs by 2.1% ($108,176) to $5.151M annually, a PMPM decrease of $0.009. The model suggests cost savings primarily due to lower unit costs for ziv-aflibercept and lower death-related costs, rather than decreased adverse event management. Conclusions: The model estimates a PMPM cost saving with increased utilization of biologics, including ziv-aflibercept. Sensitivity analysis of utilization rate changes supports these findings. Including further user-defined scenarios allows plan-specific modeling of budgetary impact.

Outpatient urticaria diagnosis codes have limited predictive value for same-day influenza vaccine adverse event detection

2010 ◽  
Vol 63 (4) ◽  
pp. 407-411 ◽  
Author(s):  
Kristen M. Moore ◽  
April Duddy ◽  
Grace M. Lee ◽  
Priscilla Velentgas ◽  
Dale R. Burwen ◽  
...  
Keyword(s):  
Adverse Event ◽  
Influenza Vaccine ◽  
Event Detection ◽  
Predictive Value ◽  
Diagnosis Codes
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