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Articles 61 - 90 of 2694

Full-Text Articles in Computer Sciences

Contributors To Pathologic Depolarization In Myotonia Congenita, Jessica Hope Myers Jan 2023

Contributors To Pathologic Depolarization In Myotonia Congenita, Jessica Hope Myers

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Myotonia congenita is an inherited skeletal muscle disorder caused by loss-of-function mutation in the CLCN1 gene. This gene encodes the ClC-1 chloride channel, which is almost exclusively expressed in skeletal muscle where it acts to stabilize the resting membrane potential. Loss of this chloride channel leads to skeletal muscle hyperexcitability, resulting in involuntary muscle action potentials (myotonic discharges) seen clinically as muscle stiffness (myotonia). Stiffness affects the limb and facial muscles, though specific muscle involvement can vary between patients. Interestingly, respiratory distress is not part of this disease despite muscles of respiration such as the diaphragm muscle also carrying this …

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Solidity Compiler Version Identification On Smart Contract Bytecode, Lakshmi Prasanna Katyayani Devasani Jan 2023

Solidity Compiler Version Identification On Smart Contract Bytecode, Lakshmi Prasanna Katyayani Devasani

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Identifying the version of the Solidity compiler used to create an Ethereum contract is a challenging task, especially when the contract bytecode is obfuscated and lacks explicit metadata. Ethereum bytecode is highly complex, as it is generated by the Solidity compiler, which translates high-level programming constructs into low-level, stack-based code. Additionally, the Solidity compiler undergoes frequent updates and modifications, resulting in continuous evolution of bytecode patterns. To address this challenge, we propose using deep learning models to analyze Ethereum bytecodes and infer the compiler version that produced them. A large number of Ethereum contracts and the corresponding compiler versions is …

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The Open Charge Point Protocol (Ocpp) Version 1.6 Cyber Range A Training And Testing Platform, David Elmo Ii Jan 2023

The Open Charge Point Protocol (Ocpp) Version 1.6 Cyber Range A Training And Testing Platform, David Elmo Ii

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The widespread expansion of Electric Vehicles (EV) throughout the world creates a requirement for charging stations. While Cybersecurity research is rapidly expanding in the field of Electric Vehicle Infrastructure, efforts are impacted by the availability of testing platforms. This paper presents a solution called the “Open Charge Point Protocol (OCPP) Cyber Range.” Its purpose is to conduct Cybersecurity research against vulnerabilities in the OCPP v1.6 protocol. The OCPP Cyber Range can be used to enable current or future research and to train operators and system managers of Electric Charge Vehicle Supply Equipment (EVSE). This paper demonstrates this solution using three …

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A Secure And Efficient Iiot Anomaly Detection Approach Using A Hybrid Deep Learning Technique, Bharath Reedy Konatham Jan 2023

A Secure And Efficient Iiot Anomaly Detection Approach Using A Hybrid Deep Learning Technique, Bharath Reedy Konatham

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The Industrial Internet of Things (IIoT) refers to a set of smart devices, i.e., actuators, detectors, smart sensors, and autonomous systems connected throughout the Internet to help achieve the purpose of various industrial applications. Unfortunately, IIoT applications are increasingly integrated into insecure physical environments leading to greater exposure to new cyber and physical system attacks. In the current IIoT security realm, effective anomaly detection is crucial for ensuring the integrity and reliability of critical infrastructure. Traditional security solutions may not apply to IIoT due to new dimensions, including extreme energy constraints in IIoT devices. Deep learning (DL) techniques like Convolutional …

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A Formal Framework For Disaster Risk Properties, Shirly Stephen, Mark Schildhauer, Kitty Currier, Pascal Hitzler, Cogan Shimizu, Krzysztof Janowicz, Dean Rehberger Jan 2023

A Formal Framework For Disaster Risk Properties, Shirly Stephen, Mark Schildhauer, Kitty Currier, Pascal Hitzler, Cogan Shimizu, Krzysztof Janowicz, Dean Rehberger

Computer Science and Engineering Faculty Publications

Disaster risk properties (or disaster variables) such as intensity, exposure, severity, vulnerability, resilience, and capacity are significant because they provide essential information for understanding and managing disaster risk and cascading effects. While there are an increasing number of datasets that record these properties based on different criteria, such as regional levels (e.g., community resilience at counties vs. census tracts), thematic levels (e.g., social vulnerability based on race vs. socioeconomic status), or even for different hazard types (e.g., disaster risk for earthquakes vs. hurricanes), we lack a formal model that captures the semantics of these properties, i.e., their interactions with one …

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A Pattern For Modeling Computational Observations, Cogan Shimizu, Pascal Hitzler, Charles F. Vardeman Jan 2023

A Pattern For Modeling Computational Observations, Cogan Shimizu, Pascal Hitzler, Charles F. Vardeman

Computer Science and Engineering Faculty Publications

Knowledge graphs (KG) are an established method for heterogeneous data integration and have begun powering complex software agents. However, it is important to understand where the data in the knowledge graph originates, especially within the context of synthetic research agents and other trustworthy AI systems. In this paper, we propose an ontology design pattern for tracking the provenance and context of computational observations, as well as a proposing a supporting, simplified conceptual framework for modeling abstract and concrete versions of the same underlying notion.

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Besoins Ontologiques Pour La Transformation Des Aliments, D. Dooley, M. Weber, L. Ibanescu, M. Lange, L. Chan, L. Soldatova, C. Yang, R. Warren, Cogan Shimizu, H. Mcginty, W. Hsiao Jan 2023

Besoins Ontologiques Pour La Transformation Des Aliments, D. Dooley, M. Weber, L. Ibanescu, M. Lange, L. Chan, L. Soldatova, C. Yang, R. Warren, Cogan Shimizu, H. Mcginty, W. Hsiao

Computer Science and Engineering Faculty Publications

People often value the sensual, celebratory, and health aspects of food, but behind this experience exists many other value-laden agricultural production, distribution, manufacturing, and physiological processes that support or undermine a healthy population. The complexity of such processes is evident in both every-day food preparation of recipes and in industrial food manufacturing, packaging and storage. An integrated ontology landscape does not yet exist to cover all the entities at work in this farm to fork journey. It seems necessary to construct such a vision by reusing expert-curated fit-to-purpose ontology subdomains. The challenge is to make this merger be, by analogy, …

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The Knowwheregraph Ontology: A Showcase, Cogan Shimizu, Shirly Stephen, Rui Zhu, Kitty Currier, Mark Schildhauer, Dean Rehberger, Pascal Hitzler, Krzysztof Janowicz, Colby K. Fisher, Mohammad Saeid Mahdavinejad, Antrea Christou, Adrita Barua, Abhilekha Dalal, Sanaz Saki Norouzi, Zilong Liu, Meilin Shi, Ling Cai, Gengchen Mai, Zhangyu Wang, Yuanyuan Tian Jan 2023

The Knowwheregraph Ontology: A Showcase, Cogan Shimizu, Shirly Stephen, Rui Zhu, Kitty Currier, Mark Schildhauer, Dean Rehberger, Pascal Hitzler, Krzysztof Janowicz, Colby K. Fisher, Mohammad Saeid Mahdavinejad, Antrea Christou, Adrita Barua, Abhilekha Dalal, Sanaz Saki Norouzi, Zilong Liu, Meilin Shi, Ling Cai, Gengchen Mai, Zhangyu Wang, Yuanyuan Tian

Computer Science and Engineering Faculty Publications

KnowWhereGraph is one of the largest fully publicly available spatially enabled knowledge graphs. It includes data on natural hazards (e.g., hurricanes, wildfires), climate variables (e.g., air temperature, precipitation), soil properties, crop and land-cover types, demographics, and human health, among other themes. These have been leveraged through the graph by a variety of applications to address challenges in food security and agricultural supply chains; sustainability related to soil conservation practices and farm labor; and delivery of emergency humanitarian aid following a disaster. This paper showcases the KnowWhereGraph ontology, which acts as the schema for the KnowWhereGraph. We discuss how it enables …

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Data-Driven Strategies For Pain Management In Patients With Sickle Cell Disease, Swati Padhee Jan 2023

Data-Driven Strategies For Pain Management In Patients With Sickle Cell Disease, Swati Padhee

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This research explores data-driven AI techniques to extract insights from relevant medical data for pain management in patients with Sickle Cell Disease (SCD). SCD is an inherited red blood cell disorder that can cause a multitude of complications throughout an individual’s life. Most patients with SCD experience repeated, unpredictable episodes of severe pain. Arguably, the most challenging aspect of treating pain episodes in SCD is assessing and interpreting the patient’s pain intensity level due to the subjective nature of pain. In this study, we leverage multiple data-driven AI techniques to improve pain management in patients with SCD. The proposed approaches …

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A Novel Knowledge-Based Federated Deep Learning Approach For Enhancing Security And Privacy Preservation In Iot Edge Computing Applications, Tabassum Simra Jan 2023

A Novel Knowledge-Based Federated Deep Learning Approach For Enhancing Security And Privacy Preservation In Iot Edge Computing Applications, Tabassum Simra

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The Internet of Things (IoT) infrastructure encompasses smart devices and real-time sensors connected through the Internet, facilitating the exchange of large datasets among these devices. This interconnected network of IoT sensors generates a significant volume of data for processing and analysis by embedded IoT Edge Computing systems. IoT Edge Computing systems enable efficient real-time analysis and data communications. Furthermore, IoT Edge Computing emerges to enhance the overall efficiency of IoT applications, making them adept at handling the dynamic demands of AI-based and large data-driven applications. The integration of IoT Edge Computing introduces several unique research challenges. Unfortunately, IoT Edge Computing …

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Code Execution Capability As A Metric For Machine Learning–Assisted Software Vulnerability Detection Models, Daniel Grahn, Lingwei Chen, Junjie Zhang Jan 2023

Code Execution Capability As A Metric For Machine Learning–Assisted Software Vulnerability Detection Models, Daniel Grahn, Lingwei Chen, Junjie Zhang

Computer Science and Engineering Faculty Publications

In this paper, we consider how the ability to learn Code Execution Tasks affects a model’s accuracy on software vulnerability detection (SVD) benchmark datasets. We initially find that models can achieve near state-of-the-art accuracy on SVD benchmarks regardless of their ability to learn Code Execution Tasks. However, these models fail to generalize well across SVD benchmarks. The results indicate a bias in the datasets that allows models to predict non- SVD signals. Under the theory that different collection methods will reduce biases, we investigate combining the SVD datasets. When trained on combined datasets, SVD accuracy is reduced but correlation with …

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A Pattern For Representing Scientific Taxonomies, Shirly Stephen, Cogan Shimizu, Mark Schildhauer, Rui Zhu, Krzysztof Janowicz, Pascal Hitzler Jan 2023

A Pattern For Representing Scientific Taxonomies, Shirly Stephen, Cogan Shimizu, Mark Schildhauer, Rui Zhu, Krzysztof Janowicz, Pascal Hitzler

Computer Science and Engineering Faculty Publications

Standard taxonomies that are meant to serve as reference specifications of specific scientific domains are developed through extensive review by scientists and experts. For data integration and interoperability needs within Knowledge Graphs (KGs), these taxonomies must be translated into formal ontologies. In this paper we present an ontology design pattern for modeling a scientific taxonomy as an ontology. The focus of the pattern is to 1) capture temporal dynamics of concepts as taxonomies evolve, 2) model the provenance of concepts to add context and enable governance, 3) assist the translation of taxonomic relations to ontological relations appropriately that will empower …

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The Expertise Ontology: Modeling Expertise In The Context Of Emergency Management, Shirly Stephen, Mark Schildhauer, Ling Cai, Yuanyuan Tian, Kitty Currier, Cogan Shimizu, Krzysztof Janowicz, Pascal Hitzler, Anna Lopez-Carr, Andrew Schroeder, Zilong Liu, Rui Zhu, Dean Rehberger, Colby K. Fisher, Gengchen Mai Jan 2023

The Expertise Ontology: Modeling Expertise In The Context Of Emergency Management, Shirly Stephen, Mark Schildhauer, Ling Cai, Yuanyuan Tian, Kitty Currier, Cogan Shimizu, Krzysztof Janowicz, Pascal Hitzler, Anna Lopez-Carr, Andrew Schroeder, Zilong Liu, Rui Zhu, Dean Rehberger, Colby K. Fisher, Gengchen Mai

Computer Science and Engineering Faculty Publications

It is crucial for emergency management organizations to have rapid access to relevant experts who can advise and assist following a disaster. To improve expert-mining and recommendation capabilities, creating a knowledge graph that links experts to their corresponding topics of expertise and other sources of relevant information is a natural choice to capture an integrated network of people and a rich taxonomy of expertise. In this paper, we present an ontology for modeling experts, their expertise topics and relations between them, and their spatiotemporal scoping. We go on to discuss the primary conceptual components and how they can be instantiated, …

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Accelerating Precision Station Keeping For Automated Aircraft, James D. Anderson Jan 2023

Accelerating Precision Station Keeping For Automated Aircraft, James D. Anderson

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Automated vehicles pose challenges in various research domains, including robotics, machine learning, computer vision, public safety, system certification, and beyond. These vehicles autonomously handle navigation and locomotion, often requiring minimal user interaction, and can operate on land, in water, or in the air. In the context of aircraft, one specific application is Automated Aerial Refueling (AAR). Traditional aerial refueling involves a "tanker" aircraft using a mechanism, such as a rigid boom arm or a flexible hose, to transfer fuel to another aircraft designated as the "receiver". For AAR, the boom arm may be maneuvered automatically, or in certain instances the …

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Comparative Adjudication Of Noisy And Subjective Data Annotation Disagreements For Deep Learning, Scott David Williams Jan 2023

Comparative Adjudication Of Noisy And Subjective Data Annotation Disagreements For Deep Learning, Scott David Williams

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Obtaining accurate inferences from deep neural networks is difficult when models are trained on instances with conflicting labels. Algorithmic recognition of online hate speech illustrates this. No human annotator is perfectly reliable, so multiple annotators evaluate and label online posts in a corpus. Labeling scheme limitations, differences in annotators' beliefs, and limits to annotators' honesty and carefulness cause some labels to disagree. Consequently, decisive and accurate inferences become less likely. Some practical applications such as social research can tolerate some indecisiveness. However, an online platform using an indecisive classifier for automated content moderation could create more problems than it solves. …

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Enhancing Graph Convolutional Network With Label Propagation And Residual For Malware Detection, Aravinda Sai Gundubogula Jan 2023

Enhancing Graph Convolutional Network With Label Propagation And Residual For Malware Detection, Aravinda Sai Gundubogula

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Malware detection is a critical task in ensuring the security of computer systems. Due to a surge in malware and the malware program sophistication, machine learning methods have been developed to perform such a task with great success. To further learn structural semantics, Graph Neural Networks abbreviated as GNNs have emerged as a recent practice for malware detection by modeling the relationships between various components of a program as a graph, which deliver promising detection performance improvement. However, this line of research attends to individual programs while overlooking program interactions; also, these GNNs tend to perform feature aggregation from neighbors …

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Effective Systems For Insider Threat Detection, Muhanned Qasim Jabbar Alslaiman Jan 2023

Effective Systems For Insider Threat Detection, Muhanned Qasim Jabbar Alslaiman

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Insider threats to information security have become a burden for organizations. Understanding insider activities leads to an effective improvement in identifying insider attacks and limits their threats. This dissertation presents three systems to detect insider threats effectively. The aim is to reduce the false negative rate (FNR), provide better dataset use, and reduce dimensionality and zero padding effects. The systems developed utilize deep learning techniques and are evaluated using the CERT 4.2 dataset. The dataset is analyzed and reformed so that each row represents a variable length sample of user activities. Two data representations are implemented to model extracted features …

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Fuzzing Php Interpreters By Automatically Generating Samples, Jacob S. Baumgarte Jan 2023

Fuzzing Php Interpreters By Automatically Generating Samples, Jacob S. Baumgarte

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Modern web development has grown increasingly reliant on scripting languages such as PHP. The complexities of an interpreted language means it is very difficult to account for every use case as unusual interactions can cause unintended side effects. Automatically generating test input to detect bugs or fuzzing, has proven to be an effective technique for JavaScript engines. By extending this concept to PHP, existing vulnerabilities that have since gone undetected can be brought to light. While PHP fuzzers exist, they are limited to testing a small quantity of test seeds per second. In this thesis, we propose a solution for …

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Unsupervised-Based Distributed Machine Learning For Efficient Data Clustering And Prediction, Vishnu Vardhan Baligodugula Jan 2023

Unsupervised-Based Distributed Machine Learning For Efficient Data Clustering And Prediction, Vishnu Vardhan Baligodugula

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Machine learning techniques utilize training data samples to help understand, predict, classify, and make valuable decisions for different applications such as medicine, email filtering, speech recognition, agriculture, and computer vision, where it is challenging or unfeasible to produce traditional algorithms to accomplish the needed tasks. Unsupervised ML-based approaches have emerged for building groups of data samples known as data clusters for driving necessary decisions about these data samples and helping solve challenges in critical applications. Data clustering is used in multiple fields, including health, finance, social networks, education, and science. Sequential processing of clustering algorithms, like the K-Means, Minibatch K-Means, …

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Anomaly Detection In Multi-Seasonal Time Series Data, Ashton Taylor Williams Jan 2023

Anomaly Detection In Multi-Seasonal Time Series Data, Ashton Taylor Williams

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Most of today’s time series data contain anomalies and multiple seasonalities, and accurate anomaly detection in these data is critical to almost any type of business. However, most mainstream forecasting models used for anomaly detection can only incorporate one or no seasonal component into their forecasts and cannot capture every known seasonal pattern in time series data. In this thesis, we propose a new multi-seasonal forecasting model for anomaly detection in time series data that extends the popular Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Our model, named multi-SARIMA, utilizes a time series dataset’s multiple pre-determined seasonal trends to increase …

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Path-Safe: Enabling Dynamic Mandatory Access Controls Using Security Tokens, James P. Maclennan Jan 2023

Path-Safe: Enabling Dynamic Mandatory Access Controls Using Security Tokens, James P. Maclennan

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Deploying Mandatory Access Controls (MAC) is a popular way to provide host protection against malware. Unfortunately, current implementations lack the flexibility to adapt to emergent malware threats and are known for being difficult to configure. A core tenet of MAC security systems is that the policies they are deployed with are immutable from the host while they are active. This work looks at deploying a MAC system that leverages using encrypted security tokens to allow for redeploying policy configurations in real-time without the need to stop a running process. This is instrumental in developing an adaptive framework for security systems …

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Data-Driven Strategies For Disease Management In Patients Admitted For Heart Failure, Ankita Agarwal Jan 2023

Data-Driven Strategies For Disease Management In Patients Admitted For Heart Failure, Ankita Agarwal

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Heart failure is a syndrome which effects a patient’s quality of life adversely. It can be caused by different underlying conditions or abnormalities and involves both cardiovascular and non-cardiovascular comorbidities. Heart failure cannot be cured but a patient’s quality of life can be improved by effective treatment through medicines and surgery, and lifestyle management. As effective treatment of heart failure incurs cost for the patients and resource allocation for the hospitals, predicting length of stay of these patients during each hospitalization becomes important. Heart failure can be classified into two types: left sided heart failure and right sided heart failure. …

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Understanding And Enhancing The Efficiency And Efficacy Of Machine Learning-Assisted Software Vulnerability Detection, Daniel J. Grahn Jan 2023

Understanding And Enhancing The Efficiency And Efficacy Of Machine Learning-Assisted Software Vulnerability Detection, Daniel J. Grahn

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As our world has become dependent upon software for nearly every aspect of modern society, software security has followed as an essential feature. The first line of defense against vulnerabilities is secure coding. While today’s programmers are carefully taught secure coding best practices, they can make mistakes or intentionally introduce vulnerable code. The traditional backstop to human errors and insider threats is the adoption of automated security analysis tools. These analysis tools have limitations. Static analysis suffers from high false positive rates that may cause annoyance and complacency among developers. Dynamic analysis can be difficult to set up and very …

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Drug Abuse Ontology To Harness Web-Based Data For Substance Use Epidemiology Research, Usha Lokala, Francois Lamy, Raminta Daniulaityte, Manas Gaur, Amelie Gyrard, Krishnaprasad Thirunarayan, Ugur Kursuncu, Amit Sheth Dec 2022

Drug Abuse Ontology To Harness Web-Based Data For Substance Use Epidemiology Research, Usha Lokala, Francois Lamy, Raminta Daniulaityte, Manas Gaur, Amelie Gyrard, Krishnaprasad Thirunarayan, Ugur Kursuncu, Amit Sheth

Computer Science and Engineering Faculty Publications

Background: Web-based resources and social media platforms play an increasingly important role in health-related knowledge and experience sharing. There is a growing interest in the use of these novel data sources for epidemiological surveillance of substance use behaviors and trends. Objective: The key aims were to describe the development and application of the drug abuse ontology (DAO) as a framework for analyzing web-based and social media data to inform public health and substance use research in the following areas: determining user knowledge, attitudes, and behaviors related to nonmedical use of buprenorphine and illicitly manufactured opioids through the analysis of web …

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Machine Learning For Angiography-Based Blood Flow Velocity Prediction, Swati Padhee, Mark Johnson, Hang Yi, Tanvi Banerjee, Zifeng Yang Nov 2022

Machine Learning For Angiography-Based Blood Flow Velocity Prediction, Swati Padhee, Mark Johnson, Hang Yi, Tanvi Banerjee, Zifeng Yang

Computer Science and Engineering Faculty Publications

Computational fluid dynamics (CFD) is widely employed to predict hemodynamic characteristics in arterial models, while not friendly to clinical applications due to the complexity of numerical simulations. Alternatively, this work proposed a framework to estimate hemodynamics in vessels based on angiography images using machine learning (ML) algorithms. First, the iodine contrast perfusion in blood was mimicked by a flow of dye diffusing into water in the experimentally validated CFD modeling. The generated projective images from simulations imitated the counterpart of light passing through the flow field as an analogy of X-ray imaging. Thus, the CFD simulation provides both the ground …

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Machine Learning For Aiding Blood Flow Velocity Estimation Based On Angiography, Swati Padhee, Mark Johnson, Hang Yi, Tanvi Banerjee, Zifeng Yang Oct 2022

Machine Learning For Aiding Blood Flow Velocity Estimation Based On Angiography, Swati Padhee, Mark Johnson, Hang Yi, Tanvi Banerjee, Zifeng Yang

Computer Science and Engineering Faculty Publications

Computational fluid dynamics (CFD) is widely employed to predict hemodynamic characteristics in arterial models, while not friendly to clinical applications due to the complexity of numerical simulations. Alternatively, this work proposed a framework to estimate hemodynamics in vessels based on angiography images using machine learning (ML) algorithms. First, the iodine contrast perfusion in blood was mimicked by a flow of dye diffusing into water in the experimentally validated CFD modeling. The generated projective images from simulations imitated the counterpart of light passing through the flow field as an analogy of X-ray imaging. Thus, the CFD simulation provides both the ground …

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Toward Mental Effort Measurement Using Electrodermal Activity Features, William Romine, Noah Schroeder, Tanvi Banerjee, Josephine Graft Sep 2022

Toward Mental Effort Measurement Using Electrodermal Activity Features, William Romine, Noah Schroeder, Tanvi Banerjee, Josephine Graft

Computer Science and Engineering Faculty Publications

The ability to monitor mental effort during a task using a wearable sensor may improve productivity for both work and study. The use of the electrodermal activity (EDA) signal for tracking mental effort is an emerging area of research. Through analysis of over 92 h of data collected with the Empatica E4 on a single participant across 91 different activities, we report on the efficacy of using EDA features getting at signal intensity, signal dispersion, and peak intensity for prediction of the participant's self-reported mental effort. We implemented the logistic regression algorithm as an interpretable machine learning approach and found …

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Leveraging Natural Learning Processing To Uncover Themes In Clinical Notes Of Patients Admitted For Heart Failure, Ankita Agarwal, Krishnaprasad Thirunarayan, William Romine, Amanuel Alambo, Mia Cajita, Tanvi Banerjee Sep 2022

Leveraging Natural Learning Processing To Uncover Themes In Clinical Notes Of Patients Admitted For Heart Failure, Ankita Agarwal, Krishnaprasad Thirunarayan, William Romine, Amanuel Alambo, Mia Cajita, Tanvi Banerjee

Computer Science and Engineering Faculty Publications

Heart failure occurs when the heart is not able to pump blood and oxygen to support other organs in the body as it should. Treatments include medications and sometimes hospitalization. Patients with heart failure can have both cardiovascular as well as non-cardiovascular comorbidities. Clinical notes of patients with heart failure can be analyzed to gain insight into the topics discussed in these notes and the major comorbidities in these patients. In this regard, we apply machine learning techniques, such as topic modeling, to identify the major themes found in the clinical notes specific to the procedures performed on 1,200 patients …

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Improving The Factual Accuracy Of Abstractive Clinical Text Summarization Using Multi-Objective Optimization, Amanuel Alambo, Tanvi Banerjee, Krishnaprasad Thirunarayan, Mia Cajita Jul 2022

Improving The Factual Accuracy Of Abstractive Clinical Text Summarization Using Multi-Objective Optimization, Amanuel Alambo, Tanvi Banerjee, Krishnaprasad Thirunarayan, Mia Cajita

Computer Science and Engineering Faculty Publications

While there has been recent progress in abstractive summarization as applied to different domains including news articles, scientific articles, and blog posts, the application of these techniques to clinical text summarization has been limited. This is primarily due to the lack of large-scale training data and the messy/unstructured nature of clinical notes as opposed to other domains where massive training data come in structured or semi -structured form. Further, one of the least explored and critical components of clinical text summarization is factual accuracy of clinical summaries. This is specifically crucial in the healthcare domain, cardiology in particular, where an …

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Improving Pain Assessment Using Vital Signs And Pain Medication For Patients With Sickle Cell Disease: Retrospective Study, Swati Padhee, Gary K. Nave Jr, Tanvi Banerjee, Daniel M. Abrams, Nirmish Shah Jun 2022

Improving Pain Assessment Using Vital Signs And Pain Medication For Patients With Sickle Cell Disease: Retrospective Study, Swati Padhee, Gary K. Nave Jr, Tanvi Banerjee, Daniel M. Abrams, Nirmish Shah

Computer Science and Engineering Faculty Publications

Background: Sickle cell disease (SCD) is the most common inherited blood disorder affecting millions of people worldwide. Most patients with SCD experience repeated, unpredictable episodes of severe pain. These pain episodes are the leading cause of emergency department visits among patients with SCD and may last for several weeks. Arguably, the most challenging aspect of treating pain episodes in SCD is assessing and interpreting a patient's pain intensity level. Objective: This study aims to learn deep feature representations of subjective pain trajectories using objective physiological signals collected from electronic health records. Methods: This study used electronic health record data collected …

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