Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art research techniques into different categories based on the underlying assumptions and approach adopted. Within each category we outline the basic anomaly detection technique, along with its variants and present key assumptions, to differentiate between normal and anomalous behavior. For each category, we present we also present the advantages and limitations and discuss the computational complexity of the techniques in real application domains. Finally, we outline open issues in research and challenges faced while adopting these techniques.

Deep Learning for Anomaly Detection: A Survey.

Anomaly detection has been widely studied and used in diverse applications. Building an effective anomaly detection system requires the researchers/developers to learn the complex structure from noisy data, identify the dynamic anomaly patterns and detect anomalies while lacking sufficient labels. Recent advancement in deep learning techniques has made it possible to largely improve anomaly detection performance compared to the classical approaches. This tutorial will help the audience gain a comprehensive understanding of deep learning-based anomaly detection techniques in various application domains. First, it introduces what is the anomaly detection problem, the approaches taken before the deep model era and the challenges it faced. Then it surveys the state-of-the-art deep learning models extensively and discusses the techniques used to overcome the limitations from traditional algorithms. Second to last, it studies deep model anomaly detection techniques in real world examples from LinkedIn production systems. The tutorial concludes with a discussion of future trends.

Deep Learning for Anomaly Detection

Detecting anomalies and novel events is vital to the financial services industry. These events may often be indicative of illegal activities such as fraud, risk, identity theft, network intrusion, account takeover and money laundering which may result in undesired outcomes such as disruption in service and other breakdowns. As financial environments change, digital adoption grows and the data moves at increasing speed and volume, the problem of detecting anomalies in real time at large scale becomes increasingly challenging. This is further compounded by the fact that more and more anomaly detection applications require operational decision making in real time. Several new ideas are emerging to tackle this challenge, including semi-supervised learning methods, deep learning based approaches and network/graph based solutions. These approaches must often be able to work in real time by consuming and processing large volumes of data produced in real time. The 2017 KDD Workshop on Anomaly Detection in Finance held at Halifax, Nova Scotia on Aug 14, 2017, brought together researchers and practitioners to discuss these new approaches and solutions. This half-day workshop consisted of two keynote speeches, two industry perspective talks, five full papers and eight spotlight talks. The talks and papers covered many ideas that are of general interest to the entire anomaly detection community in addition to discussing challenges specific to the financial services industry. We are happy to publish a select set of papers from the workshop in this issue of the Proceedings of Machine Learning Research. We open this Volume with a brief introduction to the applications in the financial services industry and how the papers in this Volume address the challenges for these applications.

/pdf/anomaly-detection-in-finance-editors-introduction-ousgxyxq31.pdf

Anomaly Detection in Finance: Editors’ Introduction

The structural health monitoring (SHM) data of civil infrastructure are inevitably contaminated due to sensor faults, environmental noise interference, and data transmission failures. Anomalous data severely disturb the subsequent structural modal identification, damage identification, and condition assessment. Therefore, it is critical to detect and clean SHM data before data analysis. This paper proposes a novel approach for data anomaly detection based on transfer learning, that makes full use of the similarity of the anomalous patterns across different bridges and shares the knowledge incorporated in a deep neural network to achieve high-accuracy data anomaly identification for bridge groups. In the proposed approach, first, a multivariate database for a source bridge is built by plotting and labeling the raw sequential data. Then, a convolutional neural network (CNN) for data anomaly classification is designed and trained with the database in different conditions. The original CNN with the highest accuracy is transferred to a new bridge with enhancement training using a small part of the target bridge data. To validate the performance of the proposed method, the multivariate SHM data for two real long-span bridges are employed, including the acceleration, strain, displacement, humidity, and temperature data. The results demonstrate that transfer learning leads to a better classification capacity for the case of scarce labeled training data compared with the original network.

Transfer learning-based data anomaly detection for structural health monitoring

The healthcare fraud detection field is constantly evolving and faces significant challenges, particularly when addressing imbalanced data issues. Previous studies mainly focused on traditional machine learning (ML) techniques, often struggling with imbalanced data. This problem arises in various aspects. It includes the risk of overfitting with Random Oversampling (ROS), noise introduction by the Synthetic Minority Oversampling Technique (SMOTE), and potential crucial information loss with Random Undersampling (RUS). Moreover, improving model performance, exploring hybrid resampling techniques, and enhancing evaluation metrics are crucial for achieving higher accuracy with imbalanced datasets. In this paper, we present a novel approach to tackle the issue of imbalanced datasets in healthcare fraud detection, with a specific focus on the Medicare Part B dataset. First, we carefully extract the categorical feature “Provider Type” from the dataset. This allows us to generate new, synthetic instances by randomly replicating existing types, thereby increasing the diversity within the minority class. Then, we apply a hybrid resampling method named SMOTE-ENN, which combines the Synthetic Minority Over-sampling Technique (SMOTE) with Edited Nearest Neighbors (ENN). This method aims to balance the dataset by generating synthetic samples and removing noisy data to improve the accuracy of the models. We use six machine learning (ML) models to categorize the instances. When evaluating performance, we rely on common metrics like accuracy, F1 score, recall, precision, and the AUC-ROC curve. We highlight the significance of the Area Under the Precision-Recall Curve (AUPRC) for assessing performance in imbalanced dataset scenarios. The experiments show that Decision Trees (DT) outperformed all the classifiers, achieving a score of 0.99 across all metrics.

Enhancing Medicare Fraud Detection through Machine Learning: Addressing Class Imbalance with SMOTE-ENN

Abstract 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 relevant to a given diagnosis and patient profile, resulting in unnecessary and unwarranted treatments and medical payments. This study aims to identify such unwarranted procedures from millions of healthcare claims. In the absence of labeled examples of unwarranted procedures, the study focused on the application of unsupervised machine learning techniques. Methods Experiments were conducted with deep autoencoders to find claims containing anomalous procedure codes indicative of FWA, and were compared against a baseline density-based clustering model. Diagnoses, procedures, and demographic data associated with healthcare claims were used as features for the models. A dataset of one hundred thousand claims sampled from a larger claims database is used to initially train and tune the models, followed by experimentations on a dataset with thirty-three million claims. Experimental results show that the autoencoder model, when trained with a novel feature-weighted loss function, outperforms the density-based clustering approach in finding potential outlier procedure codes. Results Given the unsupervised nature of our experiments, model performance was evaluated using a synthetic outlier test dataset, and a manually annotated outlier test dataset. Precision, recall and F1-scores on the synthetic outlier test dataset for the autoencoder model trained on one hundred thousand claims were 0.87, 1.0 and 0.93, respectively, while the results for these metrics on the manually annotated outlier test dataset were 0.36, 0.86 and 0.51, respectively. The model performance on the manually annotated outlier test dataset improved further when trained on the larger thirty-three million claims dataset with precision, recall and F1-scores of 0.48, 0.90 and 0.63, respectively. Conclusions This study demonstrates the feasibility of leveraging unsupervised, deep-learning methods to identify potential procedure overutilization from healthcare claims. 

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

Abstract Hospital readmissions rate is reportedly high and has caused huge financial burden on health care systems in many countries. It is viewed as an important indicator of health care providers’ quality of care. We examine the use of machine learning-based survival analysis to assess quality of care risk in hospital readmissions. This study applies various survival models to explore the risk of hospital readmissions given patient demographics and their respective hospital discharges extracted from a health care claims dataset. We explore advanced feature representation techniques such as BioBERT and Node2Vec to encode high-dimensional diagnosis code features. To our knowledge, this study is the first to apply deep-learning based survival-analysis models for predicting hospital readmission risk agnostic of specific medical conditions and a fixed window for readmission. We found that modeling the time from discharge date to readmission date as a Weibull distribution as in the SparseDeepWeiSurv model yields the best discriminative power and calibration. In addition, embedding representations of the diagnosis codes do not contribute to improvement in model performance. We find dependency of each model’s performance on the time point at which it is evaluated. This time dependency of the models’ performance on the health care claims data may necessitate a different choice of model in quality of care issue detection at different points in time. We show the effectiveness of deep-learning based survival-analysis models in estimating the quality of care risk in hospital readmissions. 

/pdf/leveraging-deep-survival-models-to-predict-quality-of-care-v1v3b3gz.pdf

Leveraging deep survival models to predict quality of care risk in diverse hospital readmissions

Fraud, waste, and abuse (FWA) in healthcare claims pose challenges to the accessibility of healthcare, adds a burden to health expenditures, and diminishes the quality of care. There is a wide variety of fraudulent behavior among providers, which necessitates lengthy investigative efforts. Timely identification of providers who share traits with other high risk or potentially fraudulent providers is of great importance to help curb FWA and expedite investigations. In this paper, we experiment with a graph topology built on healthcare claim-level features and provider risk indicators. Graph neural networks are used to compute provider node embeddings from this graph. For each provider in the graph we compute pairwise similarity using vector similarity methods, and create a rank order of providers who are similar to a given provider. We compute rank-based performance metrics on the predicted rankings and compare them to subject matter specialist rankings to show the effectiveness of our models in discerning providers with similar risk profiles versus those who are dissimilar.

Identification of Providers with Similar Risk Profiles in Healthcare Claims Using Graphs

Fraud in healthcare services and claims poses a significant threat to healthcare expenditure, accessibility to health services, and quality of care of members. One important type of member-provider collusion is where members travel unreason-able distances seeking healthcare services. Such activities could be indicators of “pill mills”, doctor shopping, or referral kickback schemes. Previous research on the identification of suspicious travel distances have focused mostly on the billed amount and considered select diagnosis conditions and travel distances at zip code or county levels. Compared to these studies, our proposed framework focuses on claims across various diagnoses and takes into account population densities of members' zip codes, and provider densities for various specialties, among other features, which are critical to the prediction of travel distances. We exper-iment with two approaches - i) a regression model paired with a statistical anomalous distance detector, and ii) a neural network-based model paired with a likelihood estimator for anomalous distance detection. The evaluation of these models on a manually annotated dataset shows that the second approach outperforms the first one in identifying anomalous travel distances.

How far is too far? Identifying suspicious travel patterns in healthcare claims using machine learning

Excessive treatment or testing of patients is considered one of the most ubiquitous and persistent forms of waste and abuse in healthcare. Some estimates show excessive treatment to be as high as 8% of all medical insurance provider expenditures. It is very difficult to identify an extraneous or unnecessary procedure or drug because there is such a wide variety of diagnoses and an equally large number of treatment options. Our goal in this paper was to show how RBMs can be utilized effectively to ferret out abnormal treatments where the prescribed treatment for a given diagnosis is not strictly followed. To test our hypothesis we generated 200,000 different injuries and injected 10% of the injuries with unnecessary treatments to reflect estimated industry prevalence levels. Using testing and training sets we found that Restricted Boltzmann Machines (RBMs) were able to reach AUCs of .95, lifts at 9.5 and recalls at 50%. Implementing our approach on real-world client datasets have shown performances levels that approach simulation performances despite additional noise.

/pdf/deep-learning-to-detect-medical-treatment-fraud-89go0fjlvt.pdf

Deep Learning to Detect Medical Treatment Fraud.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Daniel Lasaga

Author Tools

Chat about Author