Areas of Research:
Weighted comparator cohorts using entropy balancing
Target & Control cohort development using diagnosis, drug and procedure codes, based on knowledge of clinical manifestation
Weighted comparator cohorts using entropy balancing
Longitudinal patient journey; patient journey using mutual information
Machine-learning and predictive modeling to find undiagnosed or rare disease patients (without ICD-10 codes), to predict treatment switch, and risk of disease progression
Cost of illness studies identifying individual cost contributors
2-step ML based HEOR identifying risk factors and model association
In Rare Disease, EVERSANA is Leading the Way
+25
Experience with Different Disease States
7
Exclusive Rare Disease Products In Market
4
Active Commercial Engagements in Cell and Gene Therapy
Advanced Analytics Case Studies
Identifying Rare Disease HCPs with Omnichannel TargetingHow A Machine Learning Model Identified Potential Switch Targets Generating ~80% of New Patients For the Brand
자세히 알아보기 Finding APDS Patients Using Predictive ModelsUtilizing Machine Learning to Predict Patients Without a Specific ICD Code
자세히 알아보기 Assess MPS II Diagnosis and Treatment Referral Pathways and Identify Potential Patients via Predictive ModelingMachine Learning and Algorithmic Network Mapping in Rare Diseases
자세히 알아보기 Predictive Modeling for Treatment of Relapsing-Remitting Multiple SclerosisUnderstanding First-Line Treatment and Factors Leading to Treatment Switching via Machine Learning
자세히 알아보기 Assessing the Telehealth Treatment Landscape and Building Predictive Models to Identify Patients/Providers Most Likely to Use TelehealthStrengthen the Robustness of a Machine Learning Model by Incorporating EMR Data
자세히 알아보기 Identifying Potential High-Risk Factor Arrhythmia Patients Using Predictive ModelingA Robust Approach Combining Clinical, Demographic and Social Determinants of Health Data
자세히 알아보기 Scoring and Segmenting Key Opinion Leader Physicians With Innovative and Customizable Machine Learning TechniquesInfluence Mapping for Healthcare Providers in Oncology
자세히 알아보기 Improving an AATD Predictive Model Using EMR DataStrengthen the Robustness of a Machine Learning Model by Incorporating EMR Data
자세히 알아보기 Use of Machine Learning to Identify Gastroparesis Patients Suitable for Nasal Spray MetoclopramideUtilizing Supervised and Unsupervised Learning, With Hierarchical Patient Embeddings to Build Predictive Models
자세히 알아보기 Predictive Modeling for Treatment Switching in Paroxysmal Nocturnal Hemoglobinuria PatientsDesigning Machine Learning Models From Both Patient and Physician Perspectives
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