Markov model with machine learning integration for fraud detection in health insurance [PDF]
arXiv, 2021 Fraud has led to a huge addition of expenses in health insurance sector in India. The work is aimed to provide methods applied to health insurance fraud detection. The work presents two approaches - a markov model and an improved markov model using gradient boosting method in health insurance claims.
arxiv
Deep learning for prediction of population health costs [PDF]
arXiv, 2020 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. Here, we developed a deep neural network to predict future cost from health insurance claims records.
arxiv
HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking [PDF]
arXiv, 2023 In the digital age, seeking health advice on the Internet has become a common practice. At the same time, determining the trustworthiness of online medical content is increasingly challenging. Fact-checking has emerged as an approach to assess the veracity of factual claims using evidence from credible knowledge sources.
arxiv
Regularization and Hierarchical Prior Distributions for Adjustment with Health Care Claims Data: Rethinking Comorbidity Scores [PDF]
arXiv, 2017 Health care claims data refer to information generated from interactions within health systems. They have been used in health services research for decades to assess effectiveness of interventions, determine the quality of medical care, predict disease prognosis, and monitor population health. While claims data are relatively cheap and ubiquitous, they
arxiv
Evaluating Large Language Models for Health-related Queries with Presuppositions [PDF]
arXiv, 2023 As corporations rush to integrate large language models (LLMs) to their search offerings, it is critical that they provide factually accurate information that is robust to any presuppositions that a user may express. In this work, we introduce UPHILL, a dataset consisting of health-related queries with varying degrees of presuppositions.
arxiv
A Risk Based approach for the Solvency Capital requirement for Health Plans [PDF]
arXiv, 2020 The study deals with the assessment of risk measures for Health Plans in order to assess the Solvency Capital Requirement. For the estimation of the individual health care expenditure for several episode types, we suggest an original approach based on a three-part regression model. We propose three Generalized Linear Models (GLM) to assess claim counts,
arxiv
A Social Network Analysis Framework for Modeling Health Insurance Claims Data [PDF]
arXiv, 2018 Health insurance companies in Brazil have their data about claims organized having the view only for providers. In this way, they loose the physician view and how they share patients. Partnership between physicians can view as a fruitful work in most of the cases but sometimes this could be a problem for health insurance companies and patients, for ...
arxiv
A Self-Attention Network for Hierarchical Data Structures with an Application to Claims Management [PDF]
arXiv, 2018 Insurance companies must manage millions of claims per year. While most of these claims are non-fraudulent, fraud detection is core for insurance companies. The ultimate goal is a predictive model to single out the fraudulent claims and pay out the non-fraudulent ones immediately. Modern machine learning methods are well suited for this kind of problem.
arxiv
ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detection [PDF]
arXiv, 2020 In this paper we introduce ArCOV19-Rumors, an Arabic COVID-19 Twitter dataset for misinformation detection composed of tweets containing claims from 27th January till the end of April 2020. We collected 138 verified claims, mostly from popular fact-checking websites, and identified 9.4K relevant tweets to those claims. Tweets were manually-annotated by
arxiv
Integrated causal-predictive machine learning models for tropical cyclone epidemiology [PDF]
arXiv, 2020 Strategic preparedness has been shown to reduce the adverse health impacts of hurricanes and tropical storms, referred to collectively as tropical cyclones (TCs), but its protective impact could be enhanced by a more comprehensive and rigorous characterization of TC epidemiology.
arxiv