Bibliografías temáticas / Claim detection

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Literatura académica sobre el tema "Claim detection"

Autor: Grafiati

Publicado: 10 de marzo de 2023

Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Claim detection"

Sai Santosh Goud Bandari. "Machine Learning (ML) based Anomaly Detection in Insurance Industries." Journal of Information Systems Engineering and Management 10, no. 32s (2025): 13–21. https://doi.org/10.52783/jisem.v10i32s.5182.

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Handling claims presents significant difficulties for the insurance sector particularly in cases of duplicate claims, missing information, and false claims. Conventional manual techniques are prone to mistakes and inefficiencies, which substantially raises running expenses. This work presents an automated machine learning (ML) based solution for these problems. DBSCAN Clustering, Isolation Forest Classifier, and Random Forest Classifier are three specific ML techniques applied here. Early intervention is possible with the Random Forest Classifier as it can detect claims with lacking proof. Whi

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K., P. PORKODI. "FRAUD CLAIM DETECTION USING SPARK." IJIERT - International Journal of Innovations in Engineering Research and Technology 4, no. 2 (2017): 10–13. https://doi.org/10.5281/zenodo.1462257.

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Objective:To reduce the fraud claims in health insurances companies and to improve outcomes in health care industry Analysis:In the existing system,Apache hadoop and Apache hive is used for processing data,it is a batch processing syste m. In proposed system,Apache spark is used for processing streaming data. Findings:EHR record is used as data source,it contains unique id for patients across world,so it is very easy to detect fraud claim with help of patientid. Apache spark processing streaming data on regular basis. But in existing system Apache hadoop and Apache hive takes hours of

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Agarwal, Shashank. "An Intelligent Machine Learning Approach for Fraud Detection in Medical Claim Insurance: A Comprehensive Study." Scholars Journal of Engineering and Technology 11, no. 09 (2023): 191–200. http://dx.doi.org/10.36347/sjet.2023.v11i09.003.

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Medical claim insurance fraud poses a significant challenge for insurance companies and the healthcare system, leading to financial losses and reduced efficiency. In response to this issue, we present an intelligent machine- learning approach for fraud detection in medical claim insurance to enhance fraud detection accuracy and efficiency. This comprehensive study investigates the application of advanced machine learning algorithms for identifying fraudulent claims within the insurance domain. We thoroughly evaluate several candidate algorithms to select an appropriate machine learning algorit

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Prakosa, Hendri Kurniawan, and Nur Rokhman. "Anomaly Detection in Hospital Claims Using K-Means and Linear Regression." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 15, no. 4 (2021): 391. http://dx.doi.org/10.22146/ijccs.68160.

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BPJS Kesehatan, which has been in existence for almost a decade, is still experiencing a deficit in the process of guaranteeing participants. One of the factors that causes this is a discrepancy in the claim process which tends to harm BPJS Kesehatan. For example, by increasing the diagnostic coding so that the claim becomes bigger, making double claims or even recording false claims. These actions are based on government regulations is including fraud. Fraud can be detected by looking at the anomalies that appear in the claim data.This research aims to determine the anomaly of hospital claim

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Goutham, Bilakanti. "Enhancing Claim Processing Efficiency with Generative AI." International Journal of Leading Research Publication 3, no. 1 (2022): 1–11. https://doi.org/10.5281/zenodo.15196823.

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The use of Generative AI in claim processing, via diversified intake channels, including emails, faxed submissions, and intake channels that are call-center in nature. Under traditional claim processing, there is always a great amount of manual labor in obtaining, validating, and processing information vis-a-vis claims, which results in inefficiencies and delays. Advanced AI models such as NLP and GANs are used to automate data extraction, detection of anomalies, and decision-making, thereby reducing the processing time and the operational cost of processing claims. Automated intelligence incr

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IKUOMOLA, A. J., and O. E. Ojo. "AN EFFECTIVE HEALTH CARE INSURANCE FRAUD AND ABUSE DETECTION SYSTEM." Journal of Natural Sciences Engineering and Technology 15, no. 2 (2017): 1–12. http://dx.doi.org/10.51406/jnset.v15i2.1662.

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Due to the complexity of the processes within healthcare insurance systems and the large number of participants involved, it is very difficult to supervise the systems for fraud. The healthcare service providers’ fraud and abuse has become a serious problem. The practices such as billing for services that were never rendered, performing unnecessary medical services and misrepresenting non-covered treatment as covered treatments etc. not only contribute to the problem of rising health care expenditure but also affect the health of the patients. Traditional methods of detecting health care fraud

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Faseela, V. S., and Dr.P.Thangam. "A Review on Health Insurance Claim Fraud Detection." International Journal of Engineering Research & Science 4, no. 9 (2018): 26–28. https://doi.org/10.5281/zenodo.1441226.

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Abstract— The anomaly or outlier detection is one of the applications of data mining. The major use of anomaly or outlier detection is fraud detection. Health care fraud leads to substantial losses of money each year in many countries. Effective fraud detection is important for reducing the cost of Health care system. This paper reviews the various approaches used for detecting the fraudulent activities in Health insurance claim data. The approaches reviewed in this paper are Hierarchical Hidden Markov Models and Non Negative Matrix Factorization. T

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Nortey, Ezekiel N. N., Reuben Pometsey, Louis Asiedu, Samuel Iddi, and Felix O. Mettle. "Anomaly Detection in Health Insurance Claims Using Bayesian Quantile Regression." International Journal of Mathematics and Mathematical Sciences 2021 (February 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/6667671.

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Research has shown that current health expenditure in most countries, especially in sub-Saharan Africa, is inadequate and unsustainable. Yet, fraud, abuse, and waste in health insurance claims by service providers and subscribers threaten the delivery of quality healthcare. It is therefore imperative to analyze health insurance claim data to identify potentially suspicious claims. Typically, anomaly detection can be posited as a classification problem that requires the use of statistical methods such as mixture models and machine learning approaches to classify data points as either normal or

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Siva, Krishna Jampani. "Fraud Detection in Insurance Claims Using AI." Journal of Scientific and Engineering Research 6, no. 1 (2019): 302–10. https://doi.org/10.5281/zenodo.14637405.

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The insurance industry has faced issues with fraudulent claims, which have resulted in financial losses and operational inefficiencies. Integrating Artificial Intelligence offers a transformative way of detecting fraud by analyzing patterns in claim histories and customer profiles, along with external datasets. The use of AI-driven techniques, such as machine learning algorithms, natural language processing, and anomaly detection models, now allows insurers to detect fraud with greater precision and efficiency. These systems use supervised and unsupervised learning methods for outlier detectio

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Sagar, Amula Arun. "Insurance Fraud Detection Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 7 (2025): 1799–804. https://doi.org/10.22214/ijraset.2025.73264.

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The detection of fraudulent claims has become a significant challenge in the insurance industry, where manual review processes and rule-based systems often fall short in identifying complex, evolving fraud patterns. This project presents a datadriven approach to fraud detection using a real-world insurance dataset composed of 1000 policyholder records, with features including customer demographics, claim details, incident types, and vehicle information. The study employs supervised machine learning algorithms—Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost)—to classify insu

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Tesis sobre el tema "Claim detection"

Alamri, Abdulaziz. "The detection of contradictory claims in biomedical abstracts." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/15893/.

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Research claims in the biomedical domain are not always consistent, and may even be contradictory. This thesis explores contradictions between research claims in order to determine whether or not it is possible to develop a solution to automate the detection of such phenomena. Such a solution will help decision-makers, including researchers, to alleviate the effects of contradictory claims on their decisions. This study develops two methodologies to construct corpora of contradictions. The first methodology utilises systematic reviews to construct a manually-annotated corpus of contradictions.

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Yang, Li. "A comparison of unsupervised learning techniques for detection of medical abuse in automobile claims." California State University, Long Beach, 2013.

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Roberts, Terisa. "The use of credit scorecard design, predictive modelling and text mining to detect fraud in the insurance industry / Terisa Roberts." Thesis, North-West University, 2011. http://hdl.handle.net/10394/10347.

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The use of analytical techniques for fraud detection and the design of fraud detection systems have been topics of several research projects in the past and have seen varying degrees of success in their practical implementation. In particular, several authors regard the use of credit risk scorecards for fraud detection as a useful analytical detection tool. However, research on analytical fraud detection for the South African insurance industry is limited. Furthermore, real world restrictions like the availability and quality of data elements, highly unbalanced datasets, interpretability chall

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Ceglia, Cesarina. "A comparison of parametric and non-parametric methods for detecting fraudulent automobile insurance claims." Thesis, California State University, Long Beach, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10147317.

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Fraudulent automobile insurance claims are not only a loss for insurance companies, but also for their policyholders. In order for insurance companies to prevent significant loss from false claims, they must raise their premiums for the policyholders. The goal of this research is to develop a decision making algorithm to determine whether a claim is classified as fraudulent based on the observed characteristics of a claim, which can in turn help prevent future loss. The data includes 923 cases of false claims, 14,497 cases of true claims and 33 describing variables from the years 1994 to 1

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Azu, Irina Mateko. "Creating a green baloney detection kit for green claims made in the CNW report : Dust to Dust : the energy cost of new vehicles : from concept to disposal." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45787.

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Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008. Includes bibliographical references (p. 16). In order to assess the veracity of a green claim made by CNW marketing research Inc., I created a green baloney detection kit. It will serve as a guiding post by which anyone can assess the potential environmental impact of any action taken on the basis of the claims made by CNW in their dust to dust report. In their report they state that after doing an extensive life cycle analysis of several cars sold in the United States in 2005, they found that hi

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Ben, Gamra Siwar. "Contribution à la mise en place de réseaux profonds pour l'étude de tableaux par le biais de l'explicabilité : Application au style Tenebrisme ("clair-obscur")." Electronic Thesis or Diss., Littoral, 2023. http://www.theses.fr/2023DUNK0695.

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La détection de visages à partir des images picturales du style clair-obscur suscite un intérêt croissant chez les historiens de l'art et les chercheurs afin d'estimer l'emplacement de l'illuminant et ainsi répondre à plusieurs questions techniques. L'apprentissage profond suscite un intérêt croissant en raison de ses excellentes performances. Une optimisation du Réseau "Faster Region-based Convolutional Neural Network" a démontré sa capacité à relever efficacement les défis et à fournir des résultats prometteurs en matière de détection de visages à partir des images clai-obscur. Cependant, ce

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Mukkananchery, Abey. "Iterative Methods for the Reconstruction of Tomographic Images with Unconventional Source-detector Configurations." VCU Scholars Compass, 2005. http://scholarscompass.vcu.edu/etd/1244.

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X-ray computed tomography (CT) holds a critical role in current medical practice for the evaluation of patients, particularly in the emergency department and intensive care units. Expensive high resolution stationary scanners are available in radiology departments of most hospitals. In many situations however, a small, inexpensive, portable CT unit would be of significant value. Several mobile or miniature CT scanners are available, but none of these systems have the range, flexibility or overall physical characteristics of a truly portable device. The main challenge is the design of a geometr

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CHEN, YAN. "Comparisons and Applications of Quantitative Signal Detections for Adverse Drug Reactions (ADRs): An Empirical Study Based On The Food And Drug Administration (FDA) Adverse Event Reporting System (AERS) And A Large Medical Claims Database." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1203534085.

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Chen, Yan. "Comparisons and applications of quantitative signal detections for adverse drug reactions (ADRs) an empirical study based On The food And drug administration (FDA) adverse event reporting system (AERS) and a large medical claims database /." Cincinnati, Ohio : University of Cincinnati, 2008. http://www.ohiolink.edu/etd/view.cgi?acc_num=ucin1203534085.

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Thesis (Ph.D. of Pharmacy Practice and Administrative Sciences)--University of Cincinnati, 2008. Advisor: Jeff Guo PhD. Title from electronic thesis title page (viewed May 9, 2008). Keywords: data mining algorithms; adverse drug reactions; adverse event reporting system; signal detection; case-control study; antipsychotic; bipolar disorder. Includes abstract. Includes bibliographical references.

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BARACCHI, DANIELE. "Novel neural networks for structured data." Doctoral thesis, 2018. http://hdl.handle.net/2158/1113665.

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Complex relational structures are used to represent data in many scientific fields such as chemistry, bioinformatics, natural language processing and social network analysis. It is often desirable to classify these complex objects, a problem which is increasingly being dealt with machine learning approaches. While a number of algorithms have been shown to be effective in solving this task for graphs of moderate size, dealing with large structures still poses significant challenges due to the difficulty in scaling exhibited by the existing techniques. In this thesis we introduce a framework to

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Libros sobre el tema "Claim detection"

Caldwell, Laura. Claim of innocence. Mira Books, 2011.

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Joseph, Hansen. Death claims. No Exit Press, 1996.

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Joseph, Hansen. Death claims. Alyson Books, 2001.

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Kiker, Douglas. Murder on Clam Pond. Random House, 1986.

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Kiker, Douglas. Murder on Clam Pond. Thorndike Press, 1986.

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Pronzini, Bill. Crazybone: A "nameless detective" novel. Thorndike Press, 2000.

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Harris, Charlaine. Crimes au clair de lune: Une anthologie de nouvelles inédites. Édition du Club Québec loisirs, 2011.

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Pronzini, Bill. Crazy bone: A "nameless detective" novel. Carroll & Graf, 2000.

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Phelan, Twist. Family claims: A Pinnacle Peak mystery. Poisoned Pen Press, 2006.

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Holtschlag, David J. Detection of conveyance changes in St. Clair River using historical water-level and flow data with inverse one-dimensional hydrodynamic modeling. U.S. Dept. of the Interior, U.S. Geological Survey, 2009.

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Capítulos de libros sobre el tema "Claim detection"

Cheema, Gullal S., Eric Müller-Budack, Christian Otto, and Ralph Ewerth. "Claim Detection in Social Media." In Event Analytics across Languages and Communities. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64451-1_11.

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AbstractIn recent years, the problem of misinformation on the web has become widespread across languages, countries and various social media platforms. One problem central to stopping the spread of misinformation is identifying claims and prioritising them for fact-checking. Although there has been much work on automated claim detection from text recently, the role of images and their variety still need to be explored. As posts and content shared on social media are often multimodal, it has become crucial to view the problem of misinformation and fake news from a multimodal perspective. In thi

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Duan, Xueyu, Mingxue Liao, Xinwei Zhao, Wenda Wu, and Pin Lv. "An Unsupervised Joint Model for Claim Detection." In Communications in Computer and Information Science. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7983-3_18.

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Pecher, Branislav, Ivan Srba, Robert Moro, Matus Tomlein, and Maria Bielikova. "FireAnt: Claim-Based Medical Misinformation Detection and Monitoring." In Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67670-4_38.

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Peddireddy, Bhargavi, P. V. Rohith Kumar Reddy, and B. Srisatya Kapardi. "Health Insurance Claim Fraud Detection Using Artificial Neural Networks." In Cognitive Science and Technology. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9266-5_13.

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Lippi, Marco, Francesca Lagioia, Giuseppe Contissa, Giovanni Sartor, and Paolo Torroni. "Claim Detection in Judgments of the EU Court of Justice." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00178-0_35.

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Hafid, Salim, Wassim Ammar, Sandra Bringay, and Konstantin Todorov. "Cite-worthiness Detection on Social Media: A Preliminary Study." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65794-8_2.

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AbstractDetecting cite-worthiness in text is seen as the problem of flagging a missing reference to a scientific result (an article or a dataset) that should come to support a claim formulated in the text. Previous work has taken interest in this problem in the context of scientific literature, motivated by the need to allow for reference recommendation for researchers and flag missing citations in scientific work. In this preliminary study, we extend this idea towards the context of social media. As scientific claims are often made to support various arguments in societal debates on the Web,

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Allein, Liesbeth, and Marie-Francine Moens. "Checkworthiness in Automatic Claim Detection Models: Definitions and Analysis of Datasets." In Disinformation in Open Online Media. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61841-4_1.

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Picardi, Ilenia, Luca Serafini, and Marco Serino. "Disentangling Discursive Spaces of Knowledge Refused by Science: An Analysis of the Epistemic Structures in the Narratives Repertoires on Health During the Covid-19 Pandemic." In Manufacturing Refused Knowledge in the Age of Epistemic Pluralism. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-7188-6_6.

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AbstractThis chapter provides an understanding of the social configurations with which Refused Knowledge Communities (RKCs) attribute credibility to knowledge about healthcare and wellbeing. This study focuses on how RKCs enrol knowledge claims and heterogeneous actors to build, maintain and legitimise forms of knowledge refused by science. The analysis relies on empirical materials related to the online discourses shared in the Alkaline Water (AW) and Five Biological Laws (5BLs) RKCs from January 2020 to December 2021—a time span characterised by the emergence of the Covid-19 pandemic and the

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Iskender, Neslihan, Robin Schaefer, Tim Polzehl, and Sebastian Möller. "Argument Mining in Tweets: Comparing Crowd and Expert Annotations for Automated Claim and Evidence Detection." In Natural Language Processing and Information Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80599-9_25.

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(Mary) Tai, Hsueh-Yung. "Applications of Big Data and Artificial Intelligence." In Digital Health Care in Taiwan. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05160-9_11.

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AbstractThis chapter introduces the application of National Health Insurance (NHI) big data in creating digital claim review tools and artificial intelligence (AI) training to improve review efficacy. By analyzing big data in the NHI medical information system, the National Health Insurance Administration (NHIA) can detect abnormal or unusual claims and efficiently reduce medical waste. AI models are further generated with the NHI big data to identify duplicated medical images and monitor the quality of uploaded images and test results from medical institutions.The NHIA also seeks external res

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Actas de conferencias sobre el tema "Claim detection"

Kotitsas, Sotiris, Panagiotis Kounoudis, Eleni Koutli, and Haris Papageorgiou. "Leveraging fine-tuned Large Language Models with LoRA for Effective Claim, Claimer, and Claim Object Detection." In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. https://doi.org/10.18653/v1/2024.eacl-long.156.

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Saha, Diya, Manjira Sinha, and Tirthankar Dasgupta. "EnClaim: A Style Augmented Transformer Architecture for Environmental Claim Detection." In Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.climatenlp-1.9.

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Kaushik, Priyanka, Saurabh Pratap Singh Rathore, Anand Singh Bisen, and Rachna Rathore. "Enhancing Insurance Claim Fraud Detection Through Advanced Data Analytics Techniques." In 2024 IEEE Region 10 Symposium (TENSYMP). IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752284.

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Majer, Laura, and Jan Šnajder. "Claim Check-Worthiness Detection: How Well do LLMs Grasp Annotation Guidelines?" In Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.fever-1.27.

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Ni, Jingwei, Minjing Shi, Dominik Stammbach, Mrinmaya Sachan, Elliott Ash, and Markus Leippold. "AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.104.

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Shah, Agam, Arnav Hiray, Pratvi Shah, et al. "Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis." In Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.fever-1.21.

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Salazar, Armida P., Rodolfo C. Raga, and Susan S. Caluya. "Detecting Anomalies in Medical Claims with Clustering Algorithm." In 2024 Asia Pacific Conference on Innovation in Technology (APCIT). IEEE, 2024. http://dx.doi.org/10.1109/apcit62007.2024.10673480.

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Hardalov, Momchil, Anton Chernyavskiy, Ivan Koychev, Dmitry Ilvovsky, and Preslav Nakov. "CrowdChecked: Detecting Previously Fact-Checked Claims in Social Media." In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.aacl-main.22.

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P., Archana Reddy, Divya Jyothi G., Velumury Varshita, Chennupati Akshitha, and Aiswariya Milan K. "Understanding Graph Neural Networks Models for Healthcare Fraud Detection in Insurance Claims." In 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2025. https://doi.org/10.1109/iitcee64140.2025.10915253.

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Aljufri, Muhammad Arifuddin, Shabira Widyadhari, Anani Asmani, Ach Muhyil Umam, and Diana Purwitasari. "Domain-Knowledge Based Feature Engineering in Fraud Detection Using Health Administrative Claims." In 2025 International Conference on Smart Computing, IoT and Machine Learning (SIML). IEEE, 2025. https://doi.org/10.1109/siml65326.2025.11080712.

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Informes sobre el tema "Claim detection"

Harman. PR-364-11706-R01 Testing In-Situ Coriolis Meter Verification Technology Detecting Corrosion and Erosion. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010855.

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Coriolis flow meter diagnostics have made numerous advances in the past five years. Manufactures claim that they can detect corrosion build-up and coriolis tube erosion with their diagnostic software. This manufacturer-blinded study provides diagnostic results from a coriolis meter flow tested in water and in air at three different test pressures. Using wax, the meter was fouled to three different thicknesses, and eroded using sand-laden air. Wax-fouled and eroded flow performance and diagnostic results are compared to baseline data to substantiate and refute manufacturers� claims.

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CT Lung Densitometry, Consensus QIBA Profile. Chair Charles Hatt and Miranda Kirby. • The Publisher is Radiological Society of North America (RSNA)/Quantitative Imaging Biomarkers Alliance (QIBA), 2020. https://doi.org/10.1148/qiba/20200904.

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The goal of a QIBA Profile is to achieve a repeatable and useful level of performance for measures of lung density from quantitative CT using the RA-950 HU and Perc15 biomarkers of emphysema. Please see Appendix C for more detailed information on the calculation of and rationale for RA-950 HU and Perc15 as the biomarkers of choice. The Claim (Section 2) describes the performance in terms of bias and precision of RA-950 HU and Perc15 for detecting change in lung density. The Activities (Section 3) describe how to generate RA-950 HU and Perc15 for longitudinal studies of the change in lung densi

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Gao, Krishnamurthy, and McNealy. L52313 Performance Improvements of Current ILI Technologies for Mechanical Damage Detection Phase 2. Pipeline Research Council International, Inc. (PRCI), 2009. http://dx.doi.org/10.55274/r0010681.

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This final report provides a comprehensive and in-depth review of the current status of in-line inspection technologies including, but not limited to, Magnetic (Axial MFL, Circumferential MFL), and Geometrical (Caliper) methods in terms of their capabilities, limitations and potentials in detection, discrimination and characterization of various forms of pipeline mechanical damage, such as dents, dents with corrosion, and dents with cracks, gouges and dents with gouges. Capabilities of current technologies presented herein are based on validation data supplied by PRCI members. This report revi

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99mTc SPECT-CT, Consensus QIBA Profile. Chair Yuni Dewaraja and Robert Miyaoka. Radiological Society of North America (RSNA)/Quantitative Imaging Biomarkers Alliance (QIBA), 2019. https://doi.org/10.1148/qiba/20191021.

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The quantification of 99mTc labeled biomarkers can add unique value in many different settings, ranging from clinical trials of investigation new drugs to the treatment of individual patients with marketed therapeutics. For example, goals of precision medicine include using companion radiopharmaceutical diagnostics as just-in-time, predictive biomarkers for selecting patients to receive targeted treatments, customizing doses of internally administered radiotherapeutics, and assessing responses to treatment. This Profile describes quantitative outcome measures that represent proxies of target c

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McNealy. L52295 Fundamentals and Performance Improvements of ILI Technologies for Mechanical Damage Inspection. Pipeline Research Council International, Inc. (PRCI), 2008. http://dx.doi.org/10.55274/r0010677.

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The objective of this effort is to assist the pipeline industry in selecting ILI technologies that are best suited for detecting and sizing the types of mechanical damage that may pose integrity concerns, and/or are required to be addressed by the existing Regulatory Rules. The practical need is driven by both the recent changes in Regulatory requirements, vis-à-vis mechanical damage, and the latest developments of ILI technologies aiming to detect and size such damage. Based on the information provided by the six participating ILI vendors, and taking fulladvantage of extensive previous work,

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Jarram, Paul, Phil Keogh, and Dave Tweddle. PR-478-143723-R01 Evaluation of Large Stand Off Magnetometry Techniques. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010841.

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Monitoring the integrity of buried ageing ferromagnetic pipelines is a significant problem for infrastructure operators. Typically inspection relies on pig surveys, lDCVG, CIPS and contact NDT methods that often require pipes to be uncovered and often at great expense. This report contains the results of trials carried out on a controlled test bed using a novel remote sensing technique known as Stress Concentration Tomography (SCT) which claims to be capable of detecting corrosion, metal defects and the effects of ground movement by mapping variations in the earth's magnetic field around pipel

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