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Explainable unsupervised anomaly detection for healthcare insurance data.
BMC Med Inform Decis Mak
January 2025
Department of Electrical Engineering, ESAT-STADIUS, KU Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.
Background: Waste and fraud are important problems for health insurers to deal with. With the advent of big data, these insurers are looking more and more towards data mining and machine learning methods to help in detecting waste and fraud. However, labeled data is costly and difficult to acquire as it requires expert investigators and known care providers with atypical behavior.
View Article and Find Full Text PDFData-driven pipeline modeling for predicting unknown protein adulteration in dairy products.
Food Chem
December 2024
Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, PR China. Electronic address:
To preemptively predict unknown protein adulterants in food and curb the incidence of food fraud at its origin, data-driven models were developed using three machine learning (ML) algorithms. Among these, the random forest (RF)-based model achieved optimal performance, achieving accuracies of 96.2 %, 95.
View Article and Find Full Text PDFBlockchain and Internet of Things Technologies for Food Traceability in Olive Oil Supply Chains.
Sensors (Basel)
December 2024
Informatics Laboratory, Agricultural University of Athens, 11855 Athens, Greece.
This study presents a blockchain-based traceability system designed specifically for the olive oil supply chain, addressing key challenges in transparency, quality assurance, and fraud prevention. The system integrates Internet of Things (IoT) technology with a decentralized blockchain framework to provide real-time monitoring of critical quality metrics. A practical web application, linked to the Ethereum blockchain, enables stakeholders to track each stage of the supply chain via tamper-proof records.
View Article and Find Full Text PDFA robust and interpretable ensemble machine learning model for predicting healthcare insurance fraud.
Sci Rep
January 2025
School of Information Engineering, Changji University, Changji, 831100, Xinjiang, China.
Healthcare insurance fraud imposes a significant financial burden on healthcare systems worldwide, with annual losses reaching billions of dollars. This study aims to improve fraud detection accuracy using machine learning techniques. Our approach consists of three key stages: data preprocessing, model training and integration, and result analysis with feature interpretation.
View Article and Find Full Text PDFArtificial Intelligence (AI) and Men's Health Clinic Efficiency and Clinic Billing.
Curr Urol Rep
December 2024
Department of Urology, Lahey Hospital and Medical Center, MA, Burlington, USA.
Purpose Of Review: Artificial Intelligence (AI) has produced a significant impact across various industries, including healthcare. In the outpatient clinic setting, AI offers promising improvements in efficiency through Chatbots, streamlined medical documentation, and personalized patient education materials. On the billing side, AI technologies hold potential for optimizing the selection of appropriate billing codes, automating prior authorizations, and enhancing healthcare fraud detection.
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