Unsupervised Anomaly Detection of Healthcare Providers Using Generative Adversarial Networks

Naidoo, Krishnan; Marivate, Vukosi

doi:10.1007/978-3-030-44999-5_35

Cited by 13 publications

(8 citation statements)

References 16 publications

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“…Precision and recall scores that are high demonstrate the effectiveness of these models in recognizing fraudulent activities (Chalapathy & Chawla, 2019). In healthcare fraud detection, high precision values are crucial for minimizing false positives and reducing the burden on investigators (Naidoo & Marivate, 2020). High recall values reflect the models' ability to capture a significant amount of actual fraud cases, contributing to fraud prevention and financial savings.…”

Section: Model Performance and Effectivenessmentioning

confidence: 99%

Importance of Machine Learning Models in Healthcare Fraud Detection

Lekkala

2023

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With the advent of technology and the improvements in AI, many healthcare institutions are struggling with the threat of fraud. As such, Healthcare fraud poses a significant threat to the healthcare industry, as it has led to numerous financial losses. In addition, there have been cases of compromised patient care due to the fraudsters being so advanced in their systems. The purpose of this research is to investigate the pivotal role of machine learning models and how they can be used to address the challenge of fraud. Many professionals have stated that machine learning models can enhance the accuracy and fairness of healthcare fraud detection. The ideas stem from the ability to leverage a diverse dataset of healthcare transactions, including claims and billing records. Other ideas include patient demographics, where a range of machine learning algorithms, like (Random et al.) and deep learning models (CNN, RNN), are significant in evaluating the performance of the technology. The results from this research show that machine learning models are better when compared to traditional approaches. These models can achieve high precision and recall scores. The models exhibit robustness, and they are able to show an ability to adapt to variations in fraud patterns. Therefore, machine learning models offer a promising avenue for healthcare organizations to combat fraud.

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Section: Model Performance and Effectivenessmentioning

confidence: 99%

Importance of Machine Learning Models in Healthcare Fraud Detection

Lekkala

2023

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“…The study in reference 104 suggests an unsupervised framework using GANs to identify healthcare fraud by detecting anomalies in healthcare provider data sets. The GAN‐AD model demonstrates good performance in classification using logistic regression and extreme gradient boosting models, and shapley additive explanations (SHAP) analysis confirms the explanation of predictors for anomalous healthcare providers.…”

Section: Approaches For Personalized Health Monitoringmentioning

confidence: 99%

Deep learning for personalized health monitoring and prediction: A review

Damaševičius,

Jagatheesaperumal,

Kandala

et al. 2024

Computational Intelligence

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Personalized health monitoring and prediction are indispensable in advancing healthcare delivery, particularly amidst the escalating prevalence of chronic illnesses and the aging population. Deep learning (DL) stands out as a promising avenue for crafting personalized health monitoring systems adept at forecasting health outcomes with precision and efficiency. As personal health data becomes increasingly accessible, DL‐based methodologies offer a compelling strategy for enhancing healthcare provision through accurate and timely prognostications of health conditions. This article offers a comprehensive examination of recent advancements in employing DL for personalized health monitoring and prediction. It summarizes a diverse range of DL architectures and their practical implementations across various realms, such as wearable technologies, electronic health records (EHRs), and data accumulated from social media platforms. Moreover, it elucidates the obstacles encountered and outlines future directions in leveraging DL for personalized health monitoring, thereby furnishing invaluable insights into the immense potential of DL in this domain.

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“…These methods are present in numerous domains and research fields. These can be found in industrial machinery failure [28][29][30], credit card fraud [31][32][33], image processing [34,35], medical and public health [36][37][38], network intrusion [39][40][41][42], and others [43][44][45][46][47]. We focused on One-Class Classification (OCC) [17] methods to understand whether we could improve the results of the best classification algorithm.…”

Section: Computational Techniquesmentioning

confidence: 99%

Anomaly Detection on Natural Language Processing to Improve Predictions on Tourist Preferences

et al. 2022

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Argumentation-based dialogue models have shown to be appropriate for decision contexts in which it is intended to overcome the lack of interaction between decision-makers, either because they are dispersed, they are too many, or they are simply not even known. However, to support decision processes with argumentation-based dialogue models, it is necessary to have knowledge of certain aspects that are specific to each decision-maker, such as preferences, interests, and limitations, among others. Failure to obtain this knowledge could ruin the model’s success. In this work, we sought to facilitate the information acquisition process by studying strategies to automatically predict the tourists’ preferences (ratings) in relation to points of interest based on their reviews. We explored different Machine Learning methods to predict users’ ratings. We used Natural Language Processing strategies to predict whether a review is positive or negative and the rating assigned by users on a scale of 1 to 5. We then applied supervised methods such as Logistic Regression, Random Forest, Decision Trees, K-Nearest Neighbors, and Recurrent Neural Networks to determine whether a tourist likes/dislikes a given point of interest. We also used a distinctive approach in this field through unsupervised techniques for anomaly detection problems. The goal was to improve the supervised model in identifying only those tourists who truly like or dislike a particular point of interest, in which the main objective is not to identify everyone, but fundamentally not to fail those who are identified in those conditions. The experiments carried out showed that the developed models could predict with high accuracy whether a review is positive or negative but have some difficulty in accurately predicting the rating assigned by users. Unsupervised method Local Outlier Factor improved the results, reducing Logistic Regression false positives with an associated cost of increasing false negatives.

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Unsupervised Anomaly Detection of Healthcare Providers Using Generative Adversarial Networks

Cited by 13 publications

References 16 publications

Importance of Machine Learning Models in Healthcare Fraud Detection

Importance of Machine Learning Models in Healthcare Fraud Detection

Deep learning for personalized health monitoring and prediction: A review

Anomaly Detection on Natural Language Processing to Improve Predictions on Tourist Preferences

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