Results 1 to 10 of about 38,003 (25)

Emotional Framing in the Spreading of False and True Claims [PDF]

, 2023
The explosive growth of online misinformation, such as false claims, has affected the social behavior of online users. In order to be persuasive and mislead the audience, false claims are made to trigger emotions in their audience. This paper contributes to understanding how misinformation in social media is shaped by investigating the emotional ...
arxiv   +1 more source

Managing health insurance using blockchain technology [PDF]

Healthcare Technologies, Chap. 4, pp. 89-125, 2023
Health insurance plays a significant role in ensuring quality healthcare. In response to the escalating costs of the medical industry, the demand for health insurance is soaring. Additionally, those with health insurance are more likely to receive preventative care than those without health insurance.
arxiv   +1 more source

Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy [PDF]

Computers in Biology and Medicine 129 (2021) 104130, 2020
Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments. It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g ...
arxiv   +1 more source

Self-supervision for health insurance claims data: a Covid-19 use case [PDF]

arXiv, 2021
In this work, we modify and apply self-supervision techniques to the domain of medical health insurance claims. We model patients' healthcare claims history analogous to free-text narratives, and introduce pre-trained `prior knowledge', later utilized for patient outcome predictions on a challenging task: predicting Covid-19 hospitalization, given a ...
arxiv  

Towards Detecting Cascades of Biased Medical Claims on Twitter [PDF]

arXiv, 2023
Social media may disseminate medical claims that highlight misleading correlations between social identifiers and diseases due to not accounting for structural determinants of health. Our research aims to identify biased medical claims on Twitter and measure their spread.
arxiv  

A strategy to identify event specific hospitalizations in large health claims database [PDF]

arXiv, 2021
Health insurance claims data offer a unique opportunity to study disease distribution on a large scale. Challenges arise in the process of accurately analyzing these raw data. One important challenge to overcome is the accurate classification of study outcomes. For example, using claims data, there is no clear way of classifying hospitalizations due to
arxiv  

Applications of Machine Learning to the Identification of Anomalous ER Claims [PDF]

arXiv, 2022
Improper health insurance payments resulting from fraud and upcoding result in tens of billions of dollars in excess health care costs annually in the United States, motivating machine learning researchers to build anomaly detection models for health insurance claims. This article describes two such strategies specifically for ER claims.
arxiv  

Deep Claim: Payer Response Prediction from Claims Data with Deep Learning [PDF]

arXiv, 2020
Each year, almost 10% of claims are denied by payers (i.e., health insurance plans). With the cost to recover these denials and underpayments, predicting payer response (likelihood of payment) from claims data with a high degree of accuracy and precision is anticipated to improve healthcare staffs' performance productivity and drive better patient ...
arxiv  

Correlating Medi-Claim Service by Deep Learning Neural Networks [PDF]

arXiv, 2023
Medical insurance claims are of organized crimes related to patients, physicians, diagnostic centers, and insurance providers, forming a chain reaction that must be monitored constantly. These kinds of frauds affect the financial growth of both insured people and health insurance companies.
arxiv  

Explainable Automated Fact-Checking for Public Health Claims [PDF]

arXiv, 2020
Fact-checking is the task of verifying the veracity of claims by assessing their assertions against credible evidence. The vast majority of fact-checking studies focus exclusively on political claims. Very little research explores fact-checking for other topics, specifically subject matters for which expertise is required. We present the first study of
arxiv