AIMC Topic: Insurance Claim Review
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Real-world effectiveness of cariprazine in major depressive disorder and bipolar I disorder in the United States.
Journal of medical economics
Jun 10, 2025
AIMS: The efficacy of cariprazine for major depressive disorder (MDD) (adjunctive therapy) and bipolar I (BP-I) depression has been demonstrated in clinical trials. This study evaluated the real-world effectiveness of cariprazine in reducing depressi...
Establishing a Validation Framework of Treatment Discontinuation in Claims Data Using Natural Language Processing and Electronic Health Records.
Clinical pharmacology and therapeutics
Apr 8, 2025
Measuring medication discontinuation in claims data primarily relies on the gaps between prescription fills, but such definitions are rarely validated. This study aimed to establish a natural language processing (NLP)-based validation framework to ev...
Detecting Opioid Use Disorder in Health Claims Data With Positive Unlabeled Learning.
IEEE journal of biomedical and health informatics
Feb 10, 2025
Accurate detection and prevalence estimation of behavioral health conditions, such as opioid use disorder (OUD), are crucial for identifying at-risk individuals, determining treatment needs, monitoring prevention and intervention efforts, and recruit...
Machine-Learning Model Identifies Patients With Alpha-1 Antitrypsin Deficiency Using Claims Records.
COPD
Sep 23, 2024
Identifying patients with rare diseases like alpha-1 antitrypsin deficiency (AATD) is challenging. Machine-learning models may be trained to identify patients with rare diseases using large-scale, real-world databases, whereas electronic medical reco...
Collaborative artificial intelligence system for investigation of healthcare claims compliance.
Scientific reports
May 24, 2024
Healthcare fraud, waste and abuse are costly problems that have huge impact on society. Traditional approaches to identify non-compliant claims rely on auditing strategies requiring trained professionals, or on machine learning methods requiring labe...
Deep learning for prediction of population health costs.
BMC medical informatics and decision making
Feb 3, 2022
BACKGROUND: Accurate prediction of healthcare costs is important for optimally managing health costs. However, methods leveraging the medical richness from data such as health insurance claims or electronic health records are missing.
Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods.
PloS one
Sep 27, 2021
Identification of medical conditions using claims data is generally conducted with algorithms based on subject-matter knowledge. However, these claims-based algorithms (CBAs) are highly dependent on the knowledge level and not necessarily optimized f...
Improving Stroke Risk Prediction in the General Population: A Comparative Assessment of Common Clinical Rules, a New Multimorbid Index, and Machine-Learning-Based Algorithms.
Thrombosis and haemostasis
May 28, 2021
BACKGROUND: There are few large studies examining and predicting the diversified cardiovascular/noncardiovascular comorbidity relationships with stroke. We investigated stroke risks in a very large prospective cohort of patients with multimorbidity, ...
Predicting postoperative opioid use with machine learning and insurance claims in opioid-naïve patients.
American journal of surgery
Mar 26, 2021
BACKGROUND: The clinical impact of postoperative opioid use requires accurate prediction strategies to identify at-risk patients. We utilize preoperative claims data to predict postoperative opioid refill and new persistent use in opioid-naïve patien...
Drug Prescriptions
Preoperative Period
Arthralgia
Back Pain
Databases, Pharmaceutical
Insurance Claim Review
Prescription Drug Overuse
Abdominal Pain
Female
Analgesics, Opioid
Middle Aged
Aged
Humans
Headache
Comorbidity
Young Adult
Postoperative Period
Machine Learning
ROC Curve
Adult
Forecasting
Area Under Curve
Male
Retrospective Studies
Claims-Based Algorithms for Identifying Patients With Pulmonary Hypertension: A Comparison of Decision Rules and Machine-Learning Approaches.
Journal of the American Heart Association
Sep 29, 2020
Background Real-world healthcare data are an important resource for epidemiologic research. However, accurate identification of patient cohorts-a crucial first step underpinning the validity of research results-remains a challenge. We developed and e...
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