AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Fraud

Showing 11 to 20 of 34 articles

Clear Filters

Procedure code overutilization detection from healthcare claims using unsupervised deep learning methods.

BMC medical informatics and decision making
BACKGROUND: Fraud, Waste, and Abuse (FWA) in medical claims have a negative impact on the quality and cost of healthcare. A major component of FWA in claims is procedure code overutilization, where one or more prescribed procedures may not be relevan...

Applications of Bayesian Neural Networks in Outlier Detection.

Big data
Anomaly detection is crucial in a variety of domains, such as fraud detection, disease diagnosis, and equipment defect detection. With the development of deep learning, anomaly detection with Bayesian neural networks (BNNs) becomes a novel research t...

New Software Interface for Registering Rapid Antigen Test Results to Prevent Fraud.

Disaster medicine and public health preparedness
Donald O. Besong has already documented that the online registration of unsupervised lateral flow test results poses concerns in the case of a serious pandemic where there are not enough medics to read scans or watch videos of candidates' results (Be...

Artificial Intelligence Solutions to Detect Fraud in Healthcare Settings: A Scoping Review.

Studies in health technology and informatics
Over the past decade, Artificial Intelligence (AI) technologies have quickly become implemented in protecting data, including detecting fraud in healthcare organizations. This scoping review aims to explore AI solutions utilized in fraud detection oc...

Feature engineering solution with structured query language analytic functions in detecting electricity frauds using machine learning.

Scientific reports
Detecting fraud related to electricity consumption is usually a difficult challenge as the input datasets are sometimes unreliable due to missing and inconsistent records, faults, misinterpretation of meter reading remarks, status, etc. In this paper...

Monitoring and Analysis of Venture Capital and Corporate Fraud Based on Deep Learning.

Computational intelligence and neuroscience
With the continuous expansion of global investment institutions, the development of the investment industry is gradually accelerating, but the risks behind the investment are also constantly increasing. Using the data of A-share companies in China's ...

E-Commerce Fraud Detection Model by Computer Artificial Intelligence Data Mining.

Computational intelligence and neuroscience
This study aims to identify e-commerce fraud, solve the financial risks of e-commerce enterprises through big data mining (BDM), further explore more effective solutions through Information fusion technology (IFT), and create an e-commerce fraud dete...

Credit card fraud detection using a hierarchical behavior-knowledge space model.

PloS one
With the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every yea...

A Novel Feature-Engineered-NGBoost Machine-Learning Framework for Fraud Detection in Electric Power Consumption Data.

Sensors (Basel, Switzerland)
This study presents a novel feature-engineered-natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data pre-p...

Detection of fraud in ginger powder using an automatic sorting system based on image processing technique and deep learning.

Computers in biology and medicine
Ginger is a well-known product in the food and pharmaceutical industries. Ginger is one of the spices which are adulterated for economic gain. The lack of marketability of grade 3 chickpeas (small and broken chickpeas) and their very low price have m...