Relevant bibliographies by topics / Claim detection

Academic literature on the topic 'Claim detection'

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Published: 10 March 2023

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Journal articles on the topic "Claim detection"

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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 (October 31, 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 to BPJS Kesehatan. The data used is BPJS claim data for 2015-2016. While the algorithm used is a combination of K-Means algorithm and Linear Regression. For optimal clustering results, density canopy algorithm was used to determine the initial centroid.Evaluation using silhouete index resulted in value of 0.82 with number of clusters 5 and RMSE value from simple linear regression modeling of 0.49 for billing costs and 0.97 for length of stay. Based on that, there are 435 anomaly points out of 10,000 data or 4.35%. It is hoped that with the identification of these, more effective follow-up can be carried out.
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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 (November 22, 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 and abuse are time-consuming and inefficient. In this paper, the health care insurance fraud and abuse detection system (HECIFADES) was proposed. The HECIFADES consist of six modules namely: claim, augment claim, claim database, profile database, profile updater and updated profiles. The system was implemented using Visual Studio 2010 and SQL. After testing, it was observed that HECIFADES was indeed an effective system for detecting fraudulent activities and yet very secured way for generating medical claims. It also improves the quality and mitigates potential payment risks and program vulnerabilities.
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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 anomalous. Additionally, health insurance claim data are mostly associated with problems of sparsity, heteroscedasticity, multicollinearity, and the presence of missing values. The analyses of such data are best addressed by adopting more robust statistical techniques. In this paper, we utilized the Bayesian quantile regression model to establish the relations between claim outcome of interest and subject-level features and further classify claims as either normal or anomalous. An estimated model component is assumed to inherently capture the behaviors of the response variable. A Bayesian mixture model, assuming a normal mixture of two components, is used to label claims as either normal or anomalous. The model was applied to health insurance data captured on 115 people suffering from various cardiovascular diseases across different states in the USA. Results show that 25 out of 115 claims (21.7%) were potentially suspicious. The overall accuracy of the fitted model was assessed to be 92%. Through the methodological approach and empirical application, we demonstrated that the Bayesian quantile regression is a viable model for anomaly detection.
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Bakeyalakshmi, P., and S. K. Mahendran. "Enhanced replica detection scheme for efficient analysis of intrusion detection in MANET." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 565. http://dx.doi.org/10.14419/ijet.v7i1.1.10169.

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Nowadays, detection scheme of intrusion is placing a major role for efficient access and analysis in Mobile Ad-hoc network (MANET). In the past, the detection scheme of Intrusion was used to identify the efficiency of the network and in maximum systems it performs with huge rate of false alarm. In this paper, an Effective approach of the Enhanced Replica Detection scheme (ERDS) based on Sequential Probability Ratio Test (SPRT) is proposed to detect the malicious actions and to have a secure path without claim in an efficient manner. Also, provides strategies to avoid attacker and to provide secure communication. In order to have an efficient analysis of intrusion detection the proposed approach is implemented based on the anomaly. To achieve this, the detection scheme is established based on SPRT and demonstrated the performances of detection with less claim. The simulation results of control overhead, packet delivery ratio, efficient detection, energy consumption and average claims are carried out for the analysis of performance to show the improvement than the existing by using the network simulator tool. Also, the performance of the proposed system illustrated the detection of intrusion in the normal and attacker states of the network.
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Rahayu, Tiny, Mia Rahma Tika, and Sapta Lestariyowidodo. "Analysis Of Outside Claim Fragmentation On BPJS Claims In Hospital." KESANS : International Journal of Health and Science 1, no. 1 (October 30, 2021): 22–27. http://dx.doi.org/10.54543/kesans.v1i1.6.

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The Social Security Organizing Agency (BPJS) has provisions regardingfraud in which one form of fraud is the breakdown of service episodes that are not in accordance with medical indications (serviceunbundling or fragmentation)it is done by health care providers in Health Facilities Referral Follow-Up (FKRTL) the action is done intentionally, to get financial benefits from public relations. Health Insurance program in the National Social Security System through fraudulent acts that are not in accordance with the provisions of the laws and regulations. The purpose of this study is to analyze the occurrence of Fragmentation in Hospital X. This research method uses quantitative methods from the data obtained. The results of this study of the Hospital conducted fragmentation in february 33 files and march 24 files and the number of fragmentation in mountax services. The hospital argued not to experience losses because the claim package that has been arranged by the Health Insurance Organizing Agency (BPJS) Instead of the Health Insurance Organizing Agency (BPJS) prohibits fragmentation because it includesfraud. In this study, the hospital can fragment because it is not applied standard operating procedures properly and in accordance with PERMENKES Number 16 of 2020 the hospital must have a fraud prevention team in order to conduct early detection.
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Lomas, Dennis. "Representation of basic kinds: Not a case of evolutionary internalization of universal regularities." Behavioral and Brain Sciences 24, no. 4 (August 2001): 686–87. http://dx.doi.org/10.1017/s0140525x01500084.

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Shepard claims that “evolutionary internalization of universal regularities in the world” takes place. His position is interesting and seems plausible with regard to “default” motion detection and aspects of colour constancy which he addresses. However, his claim is not convincing with regard to object recognition. [Shepard]
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Ricchetti-Masterson, Kristen, Molly Aldridge, John Logie, Nittaya Suppapanya, and Suzanne F. Cook. "Exploring Methods to Measure the Prevalence of Ménière's Disease in the US Clinformatics™ Database, 2010-2012." Audiology and Neurotology 21, no. 3 (2016): 172–77. http://dx.doi.org/10.1159/000441963.

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Recent studies on the epidemiology of the inner-ear disorder Ménière's disease (MD) use disparate methods for sample selection, case identification and length of observation. Prevalence estimates vary geographically from 17 to 513 cases per 100,000 people. We explored the impact of case detection strategies and observation periods in estimating the prevalence of MD in the USA, using data from a large insurance claims database. Using case detection strategies of ≥1, ≥2 and ≥3 ICD-9 claim codes for MD within a 1-year period, the 2012 prevalence estimates were 66, 27 and 14 cases per 100,000 people, respectively. For ≥1, ≥2 and ≥3 insurance claims within a 3-year observation period, the prevalence estimates were 200, 104 and 66 cases per 100,000 people, respectively. Estimates based on a single claim are likely to overestimate prevalence; this conclusion is aligned with the American Academy of Otolaryngology-Head and Neck Foundation criteria requiring ≥2 definitive episodes for a definite diagnosis, and it has implications for future epidemiologic research. We believe estimates for ≥2 claims may be a more conservative estimate of the prevalence of MD, and multiyear estimates may be needed to allow for adequate follow-up time.
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Glanz, J. "Papers Face Off Over Claim Of Neutrino Mass Detection." Science 269, no. 5231 (September 22, 1995): 1671–72. http://dx.doi.org/10.1126/science.269.5231.1671.

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VIAENE, S., G. DEDENE, and R. DERRIG. "Auto claim fraud detection using Bayesian learning neural networks." Expert Systems with Applications 29, no. 3 (October 2005): 653–66. http://dx.doi.org/10.1016/j.eswa.2005.04.030.

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Harrag, Fouzi, and Mohamed Khalil Djahli. "Arabic Fake News Detection: A Fact Checking Based Deep Learning Approach." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 4 (July 31, 2022): 1–34. http://dx.doi.org/10.1145/3501401.

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Fake news stories can polarize society, particularly during political events. They undermine confidence in the media in general. Current NLP systems are still lacking the ability to properly interpret and classify Arabic fake news. Given the high stakes involved, determining truth in social media has recently become an emerging research that is attracting tremendous attention. Our literature review indicates that applying the state-of-the-art approaches on news content address some challenges in detecting fake news’ characteristics, which needs auxiliary information to make a clear determination. Moreover, the ‘Social-context-based’ and ‘propagation-based’ approaches can be either an alternative or complementary strategy to content-based approaches. The main goal of our research is to develop a model capable of automatically detecting truth given an Arabic news or claim. In particular, we propose a deep neural network approach that can classify fake and real news claims by exploiting ‘Convolutional Neuron Networks’. Our approach attempts to solve the problem from the fact checking perspective, where the fact-checking task involves predicting whether a given news text claim is factually authentic or fake. We opt to use an Arabic balanced corpus to build our model because it unifies stance detection, stance rationale, relevant document retrieval and fact-checking. The model is trained on different well selected attributes. An extensive evaluation has been conducted to demonstrate the ability of the fact-checking task in detecting the Arabic fake news. Our model outperforms the performance of the state-of-the-art approaches when applied to the same Arabic dataset with the highest accuracy of 91%.
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Dissertations / Theses on the topic "Claim detection"

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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. The second methodology uses a different approach to construct a corpus of contradictions which does not rely on human annotation. This methodology is proposed to overcome the limitations of the manual annotation approach. Moreover, this thesis proposes a pipeline to detect contradictions in abstracts. The pipeline takes a question and a list of research abstracts which may contain answers to it. The output of the pipeline is a list of sentences extracted from abstracts which answer the question, where each sentence is annotated with an assertion value with respect to the question. Claims which feature opposing assertion values are considered as potentially contradictory claims. The research demonstrates that automating the detection of contradictory claims in research abstracts is a feasible problem.
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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 challenges with complex analytical techniques and the evolving nature of insurance fraud contribute to the on-going challenge of detecting fraud successfully. Insurance organisations face financial instability from a global recession, tighter regulatory requirements and consolidation of the industry, which implore the need for a practical and effective fraud strategy. Given the volumes of structured and unstructured data available in data warehouses of insurance organisations, it would be sensible for an effective fraud strategy to take into account data-driven methods and incorporate analytical techniques into an overall fraud risk assessment system. Having said that, the complexity of the analytical techniques, coupled with the effort required to prepare the data to support it, should be carefully considered as some studies found that less complex algorithms produce equal or better results. Furthermore, an over reliance on analytical models can underestimate the underlying risk, as observed with credit risk at financial institutions during the financial crisis. An attractive property of the structure of the probabilistic weights-of-evidence (WOE) formulation for risk scorecard construction is its ability to handle data issues like missing values, outliers and rare cases. It is also transparent and flexible in allowing the re-adjustment of the bins based on expert knowledge or other business considerations. The approach proposed in the study is to construct fraud risk scorecards at entity level that incorporate sets of intrinsic and relational risk factors to support a robust fraud risk assessment. The study investigates the application of an integrated Suspicious Activity Assessment System (SAAS) empirically using real-world South African insurance data. The first case study uses a data sample of short-term insurance claims data and the second a data sample of life insurance claims data. Both case studies show promising results. The contributions of the study are summarised as follows: The study identified several challenges with the use of an analytical approach to fraud detection within the context of the South African insurance industry. The study proposes the development of fraud risk scorecards based on WOE measures for diagnostic fraud detection, within the context of the South African insurance industry, and the consideration of alternative algorithms to determine split points. To improve the discriminatory performance of the fraud risk scorecards, the study evaluated the use of analytical techniques, such as text mining, to identify risk factors. In order to identify risk factors from large sets of data, the study suggests the careful consideration of both the types of information as well as the types of statistical techniques in a fraud detection system. The types of information refer to the categories of input data available for analysis, translated into risk factors, and the types of statistical techniques refer to the constraints and assumptions of the underlying statistical techniques. In addition, the study advocates the use of an entity-focused approach to fraud detection, given that fraudulent activity typically occurs at an entity or group of entities level.
PhD, Operational Research, North-West University, Vaal Triangle Campus, 2011
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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 1996. To achieve the goal of this research, parametric and nonparametric methods are used to determine what variables play a major role in detecting fraudulent claims. These methods include logistic regression, the LASSO (least absolute shrinkage and selection operator) method, and Random Forests. This research concluded that a non-parametric Random Forests model classified fraudulent claims with the highest accuracy and best balance between sensitivity and specificity. Variable selection and importance are also implemented to improve the performance at which fraudulent claims are accurately classified.

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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 high fuel economy did not necessarily correlate to a smaller environmental impact, but rather the biggest contribution to the environmental impact of automobiles is in their end-of-life disposal. My green baloney detection kit will be an adaptation of Carl Sagan's original baloney detection kit, which is a series of probes which serve as a pillar for detecting fallacious arguments or claims. My enquiries show that the Dust to Dust report does not pass the green baloney detection kit and with it nontechnical environmentally conscious automotive consumers can determine that the claims made by CNW are not scientifically sound and so their decisions should be based on those claims.
by Irina Mateko Azu.
S.B.
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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 geometry that optimally trades image quality for system size. The goal of this work has been to develop analysis tools to help simulate and evaluate novel system geometries. To test the tools we have developed, three geometries have been considered in the thesis, namely, parallel projections, clam-shell and parallel plate geometries. The parallel projections geometry is commonly used in reconstruction of images by filtered back projection technique. A clam-shell structure consists of two semi-cylindrical braces that fold together over the patient's body and connect at the top. A parallel plate structure uses two fixed flat or curved plates on either side of the patient's body and image from fixed sources/detectors that are gated on and off so as to step the X-ray field through the body. The parallel plate geometry has been found to be the least reliable of the three geometries investigated, with the parallel projections geometry being the most reliable. For the targeted application, the clam-shell geometry seems to be the solution with more chances to succeed in the short term. We implemented the Van Cittert iterative technique for the reconstruction of images from projections. The thesis discusses a number of variations on the algorithm, such as the use of the Conjugate Gradient Method, several choices for the initial guess, and the incorporation of a priori information to handle the reconstruction of images with metal inserts.
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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 approach supervised learning problems on structured data by extending the R-convolution concept used in graph kernels. We represent a graph (or, more in general, a relational structure) as a hierarchy of objects and we define how to unroll a template neural network on it. This approach is able to outperform state-of-the-art methods on large social networks datasets, while at the same time being competitive on small chemobiological datasets. We also introduce a lossless compression algorithm for the hierarchical decompositions that improves the temporal complexity of our approach by exploiting symmetries in the input data. Another contribution of this thesis is an application of the aforementioned framework to the context-dependent claim detection task. Claim detection is the assessment of whether a sentence contains a claim, i.e. the thesis, or conclusion, of an argument; in particular we focus on context-dependent claims, where the context (i.e. the topic of the argument) is a determining factor in classifying a sentence. We show how our framework is able to take advantage of contextual information in a straightforward way and we present some preliminary results that indicates how this approach is viable on real world datasets. A third contribution is a machine learning approach to aortic size normalcy assesment. The definition of normalcy is crucial when dealing with thoracic aortas, as a dilatation of its diameter often precedes serious disease. We build a new estimator based on OC-SVM fitted on a cohort of 1024 healty individuals aging from 5 to 89 years, and we compare its results to those obtained on the same set of subjects by an approach based on linear regression. As a further novelty, we also build a second estimator that combines the diameters measured at multiple levels in order to assess the normalcy of the overall shape of the aorta.
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Guimaraes, Amanda De Azevedo. "Digital transformation in the insurance industry: applications of artificial intelligence in fraud detection." Master's thesis, 2020. http://hdl.handle.net/10362/108422.

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The insurance industry has always been a crucial part of the economy and society’s progress worldwide. However, it is currently facing an unprecedented scenario composed of high risks and opportunities. This study aims to explain and analyze the process of digitalization in this sector and what are the available applications of artificial intelligence for fraud detection in claim management.It also comprehends a discussion about Brazil, with recommendations that were validated with local professionals from major players in the industry. Hence, the methodological approach chosen for this study wasa combination of the qualitative method, essentially based on the review and analysis of academic literature and reports, with important interviews.Lastly, it was concluded that most insurance companies are still at the beginning of the digitalization process, seeking a better understanding of its landscape. Consequently, A.I.applications are slowly being implemented by some large insurance companies.
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Books on the topic "Claim detection"

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Caldwell, Laura. Claim of innocence. Don Mills, Ont: Mira Books, 2011.

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Joseph, Hansen. Death claims. Harpenden [England]: No Exit Press, 1996.

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Joseph, Hansen. Death claims. Los Angeles, Calif: Alyson Books, 2001.

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

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Kiker, Douglas. Murder on Clam Pond. New York: Random House, 1986.

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

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

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Phelan, Twist. Family claims: A Pinnacle Peak mystery. Scottsdale, AZ: 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. Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey, 2009.

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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. Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey, 2009.

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Book chapters on the topic "Claim detection"

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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, 197–209. Singapore: 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, 555–59. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67670-4_38.

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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, 513–27. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00178-0_35.

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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, 1–17. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61841-4_1.

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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, 275–88. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80599-9_25.

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Mohan, Thanusree, and K. Praveen. "Fraud Detection in Medical Insurance Claim with Privacy Preserving Data Publishing in TLS-N Using Blockchain." In Communications in Computer and Information Science, 211–20. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9939-8_19.

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(Mary) Tai, Hsueh-Yung. "Applications of Big Data and Artificial Intelligence." In Digital Health Care in Taiwan, 207–17. Cham: 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 resources to explore the possibilities of diverse AI applications. Its big data have been applied to create an AI-based COVID-19 detection platform used by medical centers. Within it, high-risk patients’ X-ray images can be detected automatically and then an alert message is sent to doctors, thus preventing nosocomial COVID-19 infections.Besides a convenient digital claims system, the NHIA also provides contracted institutions with useful reminders, references, and graphic functions with figures and/or tables to help the quality of their self-management.
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Azam, Kazi Sultana Farhana, Farhin Farhad Riya, and Shah Tuhin Ahmed. "Leaf Detection Using Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and Classifying with SVM Utilizing Claim Dataset." In Intelligent Data Communication Technologies and Internet of Things, 313–23. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9509-7_27.

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Smith, Robert B. "Will Claims Workers Dislike a Fraud Detector?" In Multilevel Modeling of Social Problems, 225–56. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9855-9_9.

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Diez, P. F., A. Garcés Correa, and E. Laciar Leber. "SSVEP Detection Using Adaptive Filters." In V Latin American Congress on Biomedical Engineering CLAIB 2011 May 16-21, 2011, Habana, Cuba, 1154–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-21198-0_293.

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Conference papers on the topic "Claim detection"

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Levy, Ran, Shai Gretz, Benjamin Sznajder, Shay Hummel, Ranit Aharonov, and Noam Slonim. "Unsupervised corpus–wide claim detection." In Proceedings of the 4th Workshop on Argument Mining. Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/w17-5110.

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Woloszyn, Vinicius, Joseph Kobti, and Vera Schmitt. "Towards Automatic Green Claim Detection." In FIRE 2021: Forum for Information Retrieval Evaluation. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3503162.3503163.

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Cheema, Gullal Singh, Sherzod Hakimov, Abdul Sittar, Eric Müller-Budack, Christian Otto, and Ralph Ewerth. "MM-Claims: A Dataset for Multimodal Claim Detection in Social Media." In Findings of the Association for Computational Linguistics: NAACL 2022. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.findings-naacl.72.

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Wührl, Amelie, and Roman Klinger. "Claim Detection in Biomedical Twitter Posts." In Proceedings of the 20th Workshop on Biomedical Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.bionlp-1.15.

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Wright, Dustin, and Isabelle Augenstein. "Claim Check-Worthiness Detection as Positive Unlabelled Learning." In Findings of the Association for Computational Linguistics: EMNLP 2020. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.findings-emnlp.43.

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Vyas, Sandip, and Shilpa Serasiya. "Fraud Detection in Insurance Claim System: A Review." In 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS). IEEE, 2022. http://dx.doi.org/10.1109/icais53314.2022.9742984.

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Urunkar, Abhijeet, Amruta Khot, Rashmi Bhat, and Nandinee Mudegol. "Fraud Detection and Analysis for Insurance Claim using Machine Learning." In 2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES). IEEE, 2022. http://dx.doi.org/10.1109/spices52834.2022.9774071.

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Blokker, Nico, Erenay Dayanik, Gabriella Lapesa, and Sebastian Padó. "Swimming with the Tide? Positional Claim Detection across Political Text Types." In Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.nlpcss-1.3.

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Lin, Hongzhan, Jing Ma, Mingfei Cheng, Zhiwei Yang, Liangliang Chen, and Guang Chen. "Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks." In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.emnlp-main.786.

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Vyas, Sandip, Shilpa Serasiya, and Archana Vyas. "Combined Approach of ML and Blockchain for Fraudulent Detection in Insurance Claim." In 2022 International Conference on Edge Computing and Applications (ICECAA). IEEE, 2022. http://dx.doi.org/10.1109/icecaa55415.2022.9936353.

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