Exploration of Data Science Toolbox and Predictive Models to Detect and Prevent Medicare Fraud, Waste, and Abuse

The Federal Department of Health and Human Services spends approximately $830 Billion annually on Medicare of which an estimated$30 to $110 billion is some form of fraud, waste, or abuse (FWA). Despite the Federal Government’s ongoing auditing efforts, fraud, waste, and abuse is rampant and requires modern machine learning approaches to generalize and detect such patterns. New and novel machine learning algorithms offer hope to help detect fraud, waste, and abuse. The existence of publicly accessible datasets complied by The Centers for Medicare & Medicaid Services (CMS) contain vast quantities of structured data. This data, coupled with industry standardized billing codes provides many opportunities for the application of machine learning for fraud, waste, and abuse detection. This research aims to develop a new model utilizing machine learning to generalize the patterns of fraud, waste, and abuse in Medicare. This task is accomplished by linking provider and payment data with the list of excluded individuals and entities to train an Isolation Forest algorithm on previously fraudulent behavior. Results indicate anomalous instances occurring in 0.2% of all analyzed claims, demonstrating machine learning models’ predictive ability to detect FWA

Artificial Intelligence in Banking Industry: A Review on Fraud Detection, Credit Management, and Document Processing

AI is likely to alter the banking industry during the next several years. It is progressively being utilized by banks for analyzing and executing credit applications and examining vast volumes of data. This helps to avoid fraud and enables resource-heavy, repetitive procedures and client operations to be automated without any sacrifice in quality. This study reviews how the three most promising AI applications can make the banking sector robust and efficient. Specifically, we review AI fraud detection and prevention, AI credit management, and intelligent document processing. Since the majority of transactions have become digital, there is a great need for enhanced fraud detection algorithms and fraud prevention systems in banking. We argued that the conventional strategy for identifying bank fraud may be inadequate to combat complex fraudulent activity. Instead, artificial intelligence algorithms might be very useful.  Credit management is time-consuming and expensive in terms of resources. Furthermore, because of the number of phases involved, these processes need a significant amount of work involving many laborious tasks. Banks can assess new clients for credit services, calculate loan amounts and pricing, and decrease the risk of fraud by using strong AA/ML models to assess these large and varied data sets in real-time. Documents perform critical functions in the financial system and have a substantial influence on day-to-day operations. Currently, a large percentage of this data is preserved in email messages, online forms, PDFs, scanned images, and other digital formats. Using such a massive dataset is a difficult undertaking for any bank. We discuss how the artificial intelligence techniques that automatically pull critical data from all documents received by the bank, regardless of format, and feed it to the bank's existing portals/systems while maintaining consistency

Applications of Artificial Intelligence in Healthcare

Now in these days, artificial intelligence (AI) is playing a major role in healthcare. It has many applications in diagnosis, robotic surgeries, and research, powered by the growing availability of healthcare facts and brisk improvement of analytical techniques. AI is launched in such a way that it has similar knowledge as a human but is more efficient. A robot has the same expertise as a surgeon; even if it takes a longer time for surgery, its sutures, precision, and uniformity are far better than the surgeon, leading to fewer chances of failure. To make all these things possible, AI needs some sets of algorithms. In Artificial Intelligence, there are two key categories: machine learning (ML) and natural language processing (NPL), both of which are necessary to achieve practically any aim in healthcare. The goal of this study is to keep track of current advancements in science, understand technological availability, recognize the enormous power of AI in healthcare, and encourage scientists to use AI in their related fields of research. Discoveries and advancements will continue to push the AI frontier and expand the scope of its applications, with rapid developments expected in the future

Centralized and distributed learning methods for predictive health analytics

The U.S. health care system is considered costly and highly inefficient, devoting substantial resources to the treatment of acute conditions in a hospital setting rather than focusing on prevention and keeping patients out of the hospital. The potential for cost savings is large; in the U.S. more than $30 billion are spent each year on hospitalizations deemed preventable, 31% of which is attributed to heart diseases and 20% to diabetes. Motivated by this, our work focuses on developing centralized and distributed learning methods to predict future heart- or diabetes- related hospitalizations based on patient Electronic Health Records (EHRs). We explore a variety of supervised classification methods and we present a novel likelihood ratio based method (K-LRT) that predicts hospitalizations and offers interpretability by identifying the K most significant features that lead to a positive prediction for each patient. Next, assuming that the positive class consists of multiple clusters (hospitalized patients due to different reasons), while the negative class is drawn from a single cluster (non-hospitalized patients healthy in every aspect), we present an alternating optimization approach, which jointly discovers the clusters in the positive class and optimizes the classifiers that separate each positive cluster from the negative samples. We establish the convergence of the method and characterize its VC dimension. Last, we develop a decentralized cluster Primal-Dual Splitting (cPDS) method for large-scale problems, that is computationally efficient and privacy-aware. Such a distributed learning scheme is relevant for multi-institutional collaborations or peer-to-peer applications, allowing the agents to collaborate, while keeping every participant's data private. cPDS is proved to have an improved convergence rate compared to existing centralized and decentralized methods. We test all methods on real EHR data from the Boston Medical Center and compare results in terms of prediction accuracy and interpretability

Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models

IntroductionDecades of research have established the association between adverse childhood experiences (ACEs) and adult onset of chronic diseases, influenced by health behaviors and social determinants of health (SDoH). Machine Learning (ML) is a powerful tool for computing these complex associations and accurately predicting chronic health conditions.MethodsUsing the 2021 Behavioral Risk Factor Surveillance Survey, we developed several ML models—random forest, logistic regression, support vector machine, Naïve Bayes, and K-Nearest Neighbor—over data from a sample of 52,268 respondents. We predicted 13 chronic health conditions based on ACE history, health behaviors, SDoH, and demographics. We further assessed each variable’s importance in outcome prediction for model interpretability. We evaluated model performance via the Area Under the Curve (AUC) score.ResultsWith the inclusion of data on ACEs, our models outperformed or demonstrated similar accuracies to existing models in the literature that used SDoH to predict health outcomes. The most accurate models predicted diabetes, pulmonary diseases, and heart attacks. The random forest model was the most effective for diabetes (AUC = 0.784) and heart attacks (AUC = 0.732), and the logistic regression model most accurately predicted pulmonary diseases (AUC = 0.753). The strongest predictors across models were age, ever monitored blood sugar or blood pressure, count of the monitoring behaviors for blood sugar or blood pressure, BMI, time of last cholesterol check, employment status, income, count of vaccines received, health insurance status, and total ACEs. A cumulative measure of ACEs was a stronger predictor than individual ACEs.DiscussionOur models can provide an interpretable, trauma-informed framework to identify and intervene with at-risk individuals early to prevent chronic health conditions and address their inequalities in the U.S

Predictive Modelling of Retail Banking Transactions for Credit Scoring, Cross-Selling and Payment Pattern Discovery

Evaluating transactional payment behaviour offers a competitive advantage in the modern payment ecosystem, not only for confirming the presence of good credit applicants or unlocking the cross-selling potential between the respective product and service portfolios of financial institutions, but also to rule out bad credit applicants precisely in transactional payments streams. In a diagnostic test for analysing the payment behaviour, I have used a hybrid approach comprising a combination of supervised and unsupervised learning algorithms to discover behavioural patterns. Supervised learning algorithms can compute a range of credit scores and cross-sell candidates, although the applied methods only discover limited behavioural patterns across the payment streams. Moreover, the performance of the applied supervised learning algorithms varies across the different data models and their optimisation is inversely related to the pre-processed dataset. Subsequently, the research experiments conducted suggest that the Two-Class Decision Forest is an effective algorithm to determine both the cross-sell candidates and creditworthiness of their customers. In addition, a deep-learning model using neural network has been considered with a meaningful interpretation of future payment behaviour through categorised payment transactions, in particular by providing additional deep insights through graph-based visualisations. However, the research shows that unsupervised learning algorithms play a central role in evaluating the transactional payment behaviour of customers to discover associations using market basket analysis based on previous payment transactions, finding the frequent transactions categories, and developing interesting rules when each transaction category is performed on the same payment stream. Current research also reveals that the transactional payment behaviour analysis is multifaceted in the financial industry for assessing the diagnostic ability of promotion candidates and classifying bad credit applicants from among the entire customer base. The developed predictive models can also be commonly used to estimate the credit risk of any credit applicant based on his/her transactional payment behaviour profile, combined with deep insights from the categorised payment transactions analysis. The research study provides a full review of the performance characteristic results from different developed data models. Thus, the demonstrated data science approach is a possible proof of how machine learning models can be turned into cost-sensitive data models

Feature selection and personalized modeling on medical adverse outcome prediction

This thesis is about the medical adverse outcome prediction and is composed of three parts, i.e. feature selection, time-to-event prediction and personalized modeling. For feature selection, we proposed a three-stage feature selection method which is an ensemble of filter, embedded and wrapper selection techniques. We combine them in a way to select a both stable and predictive set of features as well as reduce the computation burden. Datasets on two adverse outcome prediction problems, 30-day hip fracture readmission and diabetic retinopathy prognosis are derived from electronic health records and exemplified to prove the effectiveness of the proposed method. With the selected features, we investigated the application of some classical survival analysis models, namely the accelerated failure time models, Cox proportional hazard regression models and mixture cure models on adverse outcome prediction. Unlike binary classifiers, survival analysis methods consider both the status and time-to-event information and provide more flexibility when we are interested in the occurrence of adverse outcome in different time windows. Lastly, we introduced the use of personalized modeling(PM) to predict adverse outcome based on the most similar patients of each query patient. Different from the commonly used global modeling approach, PM builds prediction model on smaller but more similar patient cohort thus leading to a more individual-based prediction and customized risk factor profile. Both static and metric learning distance measures are used to identify similar patient cohort. We show that PM together with feature selection achieves better prediction performance by using only similar patients, compared with using data from all available patients in one-size-fits-all model

Robust and Adversarial Data Mining

In the domain of data mining and machine learning, researchers have made significant contributions in developing algorithms handling clustering and classification problems. We develop algorithms under assumptions that are not met by previous works. (i) In adversarial learning, which is the study of machine learning techniques deployed in non-benign environments. We design an algorithm to show how a classifier should be designed to be robust against sparse adversarial attacks. Our main insight is that sparse feature attacks are best defended by designing classifiers which use L1 regularizers. (ii) The different properties between L1 (Lasso) and L2 (Tikhonov or Ridge) regularization has been studied extensively. However, given a data set, principle to follow in terms of choosing the suitable regularizer is yet to be developed. We use mathematical properties of the two regularization methods followed by detailed experimentation to understand their impact based on four characteristics. (iii) The identification of anomalies is an inherent component of knowledge discovery. In lots of cases, the number of features of a data set can be traced to a much smaller set of features. We claim that algorithms applied in a latent space are more robust. This can lead to more accurate results, and potentially provide a natural medium to explain and describe outliers. (iv) We also apply data mining techniques on health care industry. In a lot cases, health insurance companies cover unnecessary costs carried out by healthcare providers. The potential adversarial behaviours of surgeon physicians are addressed. We describe a specific con- text of private healthcare in Australia and describe our social network based approach (applied to health insurance claims) to understand the nature of collaboration among doctors treating hospital inpatients and explore the impact of collaboration on cost and quality of care. (v) We further develop models that predict the behaviours of orthopaedic surgeons in regard to surgery type and use of prosthetic device. An important feature of these models is that they can not only predict the behaviours of surgeons but also provide explanation for the predictions

Sequential learning and shared representation for sensor-based human activity recognition

Human activity recognition based on sensor data has rapidly attracted considerable research attention due to its wide range of applications including senior monitoring, rehabilitation, and healthcare. These applications require accurate systems of human activity recognition to track and understand human behaviour. Yet, developing such accurate systems pose critical challenges and struggle to learn from temporal sequential sensor data due to the variations and complexity of human activities. The main challenges of developing human activity recognition are accuracy and robustness due to the diversity and similarity of human activities, skewed distribution of human activities, and also lack of a rich quantity of wellcurated human activity data. This thesis addresses these challenges by developing robust deep sequential learning models to boost the performance of human activity recognition and handle the imbalanced class problems as well as reduce the need for a large amount of annotated data. This thesis develops a set of new networks specifically designed for the challenges in building better HAR systems compared to the existing methods. First, this thesis proposes robust and sequential deep learning models to accurately recognise human activities and boost the performance of the human activity recognition systems against the current methods from smart home and wearable sensors collected data. The proposed methods integrate convolutional neural networks and different attention mechanisms to efficiently process human activity data and capture significant information for recognising human activities. Next, the thesis proposes methods to address the imbalanced class problems for human activity recognition systems. Joint learning of sequential deep learning algorithms, i.e., long short-term memory and convolutional neural networks is proposed to boost the performance of human activity recognition, particularly for infrequent human activities. In addition to that, also propose a data-level solution to address imbalanced class problems by extending the synthetic minority over-sampling technique (SMOTE) which we named (iSMOTE) to accurately label the generated synthetic samples. These methods have enhanced the results of the minority human activities and outperformed the current state-of-the-art methods. In this thesis, sequential deep learning networks are proposed to boost the performance of human activity recognition in addition to reducing the dependency for a rich quantity of well-curated human activity data by transfer learning techniques. A multi-domain learning network is proposed to process data from multi-domains, transfer knowledge across different but related domains of human activities and mitigate isolated learning paradigms using a shared representation. The advantage of the proposed method is firstly to reduce the need and effort for labelled data of the target domain. The proposed network uses the training data of the target domain with restricted size and the full training data of the source domain, yet provided better performance than using the full training data in a single domain setting. Secondly, the proposed method can be used for small datasets. Lastly, the proposed multidomain learning network reduces the training time by rendering a generic model for related domains compared to fitting a model for each domain separately. In addition, the thesis also proposes a self-supervised model to reduce the need for a considerable amount of annotated human activity data. The self-supervised method is pre-trained on the unlabeled data and fine-tuned on a small amount of labelled data for supervised learning. The proposed self-supervised pre-training network renders human activity representations that are semantically meaningful and provides a good initialization for supervised fine tuning. The developed network enhances the performance of human activity recognition in addition to minimizing the need for a considerable amount of labelled data. The proposed models are evaluated by multiple public and benchmark datasets of sensorbased human activities and compared with the existing state-of-the-art methods. The experimental results show that the proposed networks boost the performance of human activity recognition systems

Use of Machine Learning and Natural Language Processing to Enhance Traffic Safety Analysis

Despite significant advances in vehicle technologies, safety data collection and analysis, and engineering advancements, tens of thousands of Americans die every year in motor vehicle crashes. Alarmingly, the trend of fatal and serious injury crashes appears to be heading in the wrong direction. In 2021, the actual rate of fatalities exceeded the predicted rate. This worrisome trend prompts and necessitates the development of advanced and holistic approaches to determining the causes of a crash (particularly fatal and major injuries). These approaches range from analyzing problems from multiple perspectives, utilizing available data sources, and employing the most suitable tools and technologies within and outside traffic safety domain.The primary source for traffic safety analysis is the structure (also called tabular) data collected from crash reports. However, structure data may be insufficient because of missing information, incomplete sequence of events, misclassified crash types, among many issues. Crash narratives, a form of free text recorded by police officers to describe the unique aspects and circumstances of a crash, are commonly used by safety professionals to supplement structure data fields. Due to its unstructured nature, engineers have to manually review every crash narrative. Thanks to the rapid development in natural language processing (NLP) and machine learning (ML) techniques, text mining and analytics has become a popular tool to accelerate information extraction and analysis for unstructured text data. The primary objective of this dissertation is to discover and develop necessary tools, techniques, and algorithms to facilitate traffic safety analysis using crash narratives. The objectives are accomplished in three areas: enhancing data quality by recovering missed crashes through text classification, uncovering complex characteristics of collision generation through information extraction and pattern recognition, and facilitating crash narrative analysis by developing a web-based tool. At first, a variety of NoisyOR classifiers were developed to identify and investigate work zone (WZ), distracted (DD), and inattentive (ID) crashes. In addition, various machine learning (ML) models, including multinomial naive bayes (MNB), logistic regression (LGR), support vector machine (SVM), k-nearest neighbor (K-NN), random forest (RF), and gated recurrent unit (GRU), were developed and compared with NoisyOR. The comparison shows that NoisyOR is simple, computationally efficient, theoretically sound, and has one of the best model performances. Furthermore, a novel neural network architecture named Sentence-based Hierarchical Attention Network (SHAN) was developed to classify crashes and its performance exceeds that of NoisyOR, GRU, Hierarchical Attention Network (HAN), and other ML models. SHAN handled noisy or irrelevant parts of narratives effectively and the model results can be visualized by attention weight. Because a crash often comprises a series of actions and events, breaking the chain of events could prevent a crash from reaching its most dangerous stage. With the objectives of creating crash sequences, discovering pattern of crash events, and finding missing events, the Part-of-Speech tagging (PT), Pattern Matching with POS Tagging (PMPT), Dependency Parser (DP), and Hybrid Generalized (HGEN) algorithms were developed and thoroughly tested using crash narratives. The top performer, HGEN, uses predefined events and event-related action words from crash narratives to find new events not captured in the data fields. Besides, the association analysis unravels the complex interrelations between events within a crash. Finally, the crash information extraction, analysis, and classification tool (CIEACT), a simple and flexible online web tool, was developed to analyze crash narratives using text mining techniques. The tool uses a Python-based Django Web Framework, HTML, and a relational database (PostgreSQL) that enables concurrent model development and analysis. The tool has built-in classifiers by default or can train a model in real time given the data. The interface is user friendly and the results can be displayed in a tabular format or on an interactive map. The tool also provides an option for users to download the word with their probability scores and the results in csv files. The advantages and limitations of each proposed methodology were discussed, and several future research directions were outlined. In summary, the methodologies and tools developed as part of the dissertation can assist transportation engineers and safety professionals in extracting valuable information from narratives, recovering missed crashes, classifying a new crash, and expediting their review process on a large scale. Thus, this research can be used by transportation agencies to analyze crash records, identify appropriate safety solutions, and inform policy making to improve highway safety of our transportation system