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Article ; Online: Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering.

Ghaderi, Hamid / Foreman, Brandon / Reddy, Chandan K / Subbian, Vignesh

ArXiv

2024

Abstract: Traumatic Brain Injury (TBI) presents a broad spectrum of clinical presentations and outcomes due to its inherent heterogeneity, leading to diverse recovery trajectories and varied therapeutic responses. While many studies have delved into TBI ... ...

Abstract	Traumatic Brain Injury (TBI) presents a broad spectrum of clinical presentations and outcomes due to its inherent heterogeneity, leading to diverse recovery trajectories and varied therapeutic responses. While many studies have delved into TBI phenotyping for distinct patient populations, identifying TBI phenotypes that consistently generalize across various settings and populations remains a critical research gap. Our research addresses this by employing multivariate time-series clustering to unveil TBI's dynamic intricates. Utilizing a self-supervised learning-based approach to clustering multivariate time-Series data with missing values (SLAC-Time), we analyzed both the research-centric TRACK-TBI and the real-world MIMIC-IV datasets. Remarkably, the optimal hyperparameters of SLAC-Time and the ideal number of clusters remained consistent across these datasets, underscoring SLAC-Time's stability across heterogeneous datasets. Our analysis revealed three generalizable TBI phenotypes (α, β, and γ), each exhibiting distinct non-temporal features during emergency department visits, and temporal feature profiles throughout ICU stays. Specifically, phenotype α represents mild TBI with a remarkably consistent clinical presentation. In contrast, phenotype β signifies severe TBI with diverse clinical manifestations, and phenotype γ represents a moderate TBI profile in terms of severity and clinical diversity. Age is a significant determinant of TBI outcomes, with older cohorts recording higher mortality rates. Importantly, while certain features varied by age, the core characteristics of TBI manifestations tied to each phenotype remain consistent across diverse populations.
Language	English
Publishing date	2024-01-15
Publishing country	United States
Document type	Preprint
ISSN	2331-8422
ISSN (online)	2331-8422
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Complex Logical Reasoning over Knowledge Graphs using Large Language Models

Choudhary, Nurendra / Reddy, Chandan K.

2023

Abstract: Reasoning over knowledge graphs (KGs) is a challenging task that requires a deep understanding of the complex relationships between entities and the underlying logic of their relations. Current approaches rely on learning geometries to embed entities in ... ...

Abstract	Reasoning over knowledge graphs (KGs) is a challenging task that requires a deep understanding of the complex relationships between entities and the underlying logic of their relations. Current approaches rely on learning geometries to embed entities in vector space for logical query operations, but they suffer from subpar performance on complex queries and dataset-specific representations. In this paper, we propose a novel decoupled approach, Language-guided Abstract Reasoning over Knowledge graphs (LARK), that formulates complex KG reasoning as a combination of contextual KG search and logical query reasoning, to leverage the strengths of graph extraction algorithms and large language models (LLM), respectively. Our experiments demonstrate that the proposed approach outperforms state-of-the-art KG reasoning methods on standard benchmark datasets across several logical query constructs, with significant performance gain for queries of higher complexity. Furthermore, we show that the performance of our approach improves proportionally to the increase in size of the underlying LLM, enabling the integration of the latest advancements in LLMs for logical reasoning over KGs. Our work presents a new direction for addressing the challenges of complex KG reasoning and paves the way for future research in this area. Comment: Code available at https://github.com/Akirato/LLM-KG-Reasoning
Keywords	Computer Science - Logic in Computer Science ; Computer Science - Artificial Intelligence ; Computer Science - Information Retrieval ; F.4.1 ; H.3.3 ; I.1.1
Subject code	004
Publishing date	2023-05-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Self-Supervised Graph Learning With Hyperbolic Embedding for Temporal Health Event Prediction.

Lu, Chang / Reddy, Chandan K / Ning, Yue

IEEE transactions on cybernetics

2023 Volume 53, Issue 4, Page(s) 2124–2136

Abstract: Electronic health records (EHRs) have been heavily used in modern healthcare systems for recording patients' admission information to health facilities. Many data-driven approaches employ temporal features in EHR for predicting specific diseases, ... ...

Abstract	Electronic health records (EHRs) have been heavily used in modern healthcare systems for recording patients' admission information to health facilities. Many data-driven approaches employ temporal features in EHR for predicting specific diseases, readmission times, and diagnoses of patients. However, most existing predictive models cannot fully utilize EHR data, due to an inherent lack of labels in supervised training for some temporal events. Moreover, it is hard for the existing methods to simultaneously provide generic and personalized interpretability. To address these challenges, we propose Sherbet, a self-supervised graph learning framework with hyperbolic embeddings for temporal health event prediction. We first propose a hyperbolic embedding method with information flow to pretrain medical code representations in a hierarchical structure. We incorporate these pretrained representations into a graph neural network (GNN) to detect disease complications and design a multilevel attention method to compute the contributions of particular diseases and admissions, thus enhancing personalized interpretability. We present a new hierarchy-enhanced historical prediction proxy task in our self-supervised learning framework to fully utilize EHR data and exploit medical domain knowledge. We conduct a comprehensive set of experiments on widely used publicly available EHR datasets to verify the effectiveness of our model. Our results demonstrate the proposed model's strengths in both predictive tasks and interpretable abilities.
MeSH term(s)	Humans ; Electronic Health Records ; Neural Networks, Computer
Language	English
Publishing date	2023-03-16
Publishing country	United States
Document type	Journal Article
ISSN	2168-2275
ISSN (online)	2168-2275
DOI	10.1109/TCYB.2021.3109881
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data.

Ghaderi, Hamid / Foreman, Brandon / Nayebi, Amin / Tipirneni, Sindhu / Reddy, Chandan K / Subbian, Vignesh

AMIA ... Annual Symposium proceedings. AMIA Symposium

2024 Volume 2023, Page(s) 379–388

Abstract: Determining clinically relevant physiological states from multivariate time-series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure. ...

Abstract	Determining clinically relevant physiological states from multivariate time-series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure. Utilizing non-temporal clustering or data imputation and aggregation techniques may lead to loss of valuable information and biased analyses. In our study, we apply the SLAC-Time algorithm, an innovative self-supervision-based approach that maintains data integrity by avoiding imputation or aggregation, offering a more useful representation of acute patient states. By using SLAC-Time to cluster data in a large research dataset, we identified three distinct TBI physiological states and their specific feature profiles. We employed various clustering evaluation metrics and incorporated input from a clinical domain expert to validate and interpret the identified physiological states. Further, we discovered how specific clinical events and interventions can influence patient states and state transitions.
MeSH term(s)	Humans ; Brain Injuries, Traumatic/diagnosis ; Algorithms ; Cluster Analysis ; Time Factors ; Benchmarking
Language	English
Publishing date	2024-01-11
Publishing country	United States
Document type	Journal Article
ISSN	1942-597X
ISSN (online)	1942-597X
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: CodeAttack

Jha, Akshita / Reddy, Chandan K.

Code-Based Adversarial Attacks for Pre-trained Programming Language Models

2022

Abstract: Pre-trained programming language (PL) models (such as CodeT5, CodeBERT, GraphCodeBERT, etc.,) have the potential to automate software engineering tasks involving code understanding and code generation. However, these models operate in the natural channel ...

Abstract	Pre-trained programming language (PL) models (such as CodeT5, CodeBERT, GraphCodeBERT, etc.,) have the potential to automate software engineering tasks involving code understanding and code generation. However, these models operate in the natural channel of code, i.e., they are primarily concerned with the human understanding of the code. They are not robust to changes in the input and thus, are potentially susceptible to adversarial attacks in the natural channel. We propose, CodeAttack, a simple yet effective black-box attack model that uses code structure to generate effective, efficient, and imperceptible adversarial code samples and demonstrates the vulnerabilities of the state-of-the-art PL models to code-specific adversarial attacks. We evaluate the transferability of CodeAttack on several code-code (translation and repair) and code-NL (summarization) tasks across different programming languages. CodeAttack outperforms state-of-the-art adversarial NLP attack models to achieve the best overall drop in performance while being more efficient, imperceptible, consistent, and fluent. The code can be found at https://github.com/reddy-lab-code-research/CodeAttack. Comment: AAAI Conference on Artificial Intelligence (AAAI) 2023
Keywords	Computer Science - Computation and Language ; Computer Science - Cryptography and Security
Subject code	005
Publishing date	2022-05-31
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Towards Scalable Hyperbolic Neural Networks using Taylor Series Approximations

Choudhary, Nurendra / Reddy, Chandan K.

2022

Abstract: Hyperbolic networks have shown prominent improvements over their Euclidean counterparts in several areas involving hierarchical datasets in various domains such as computer vision, graph analysis, and natural language processing. However, their adoption ... ...

Abstract	Hyperbolic networks have shown prominent improvements over their Euclidean counterparts in several areas involving hierarchical datasets in various domains such as computer vision, graph analysis, and natural language processing. However, their adoption in practice remains restricted due to (i) non-scalability on accelerated deep learning hardware, (ii) vanishing gradients due to the closure of hyperbolic space, and (iii) information loss due to frequent mapping between local tangent space and fully hyperbolic space. To tackle these issues, we propose the approximation of hyperbolic operators using Taylor series expansions, which allows us to reformulate the computationally expensive tangent and cosine hyperbolic functions into their polynomial equivariants which are more efficient. This allows us to retain the benefits of preserving the hierarchical anatomy of the hyperbolic space, while maintaining the scalability over current accelerated deep learning infrastructure. The polynomial formulation also enables us to utilize the advancements in Euclidean networks such as gradient clipping and ReLU activation to avoid vanishing gradients and remove errors due to frequent switching between tangent space and hyperbolic space. Our empirical evaluation on standard benchmarks in the domain of graph analysis and computer vision shows that our polynomial formulation is as scalable as Euclidean architectures, both in terms of memory and time complexity, while providing results as effective as hyperbolic models. Moreover, our formulation also shows a considerable improvement over its baselines due to our solution to vanishing gradients and information loss. Comment: Preprint under review
Keywords	Computer Science - Machine Learning ; I.2.4 ; B.8.2
Subject code	514
Publishing date	2022-06-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering

Ghaderi, Hamid / Foreman, Brandon / Reddy, Chandan K. / Subbian, Vignesh

2024

Abstract	Traumatic Brain Injury (TBI) presents a broad spectrum of clinical presentations and outcomes due to its inherent heterogeneity, leading to diverse recovery trajectories and varied therapeutic responses. While many studies have delved into TBI phenotyping for distinct patient populations, identifying TBI phenotypes that consistently generalize across various settings and populations remains a critical research gap. Our research addresses this by employing multivariate time-series clustering to unveil TBI's dynamic intricates. Utilizing a self-supervised learning-based approach to clustering multivariate time-Series data with missing values (SLAC-Time), we analyzed both the research-centric TRACK-TBI and the real-world MIMIC-IV datasets. Remarkably, the optimal hyperparameters of SLAC-Time and the ideal number of clusters remained consistent across these datasets, underscoring SLAC-Time's stability across heterogeneous datasets. Our analysis revealed three generalizable TBI phenotypes ({\alpha}, \b{eta}, and {\gamma}), each exhibiting distinct non-temporal features during emergency department visits, and temporal feature profiles throughout ICU stays. Specifically, phenotype {\alpha} represents mild TBI with a remarkably consistent clinical presentation. In contrast, phenotype \b{eta} signifies severe TBI with diverse clinical manifestations, and phenotype {\gamma} represents a moderate TBI profile in terms of severity and clinical diversity. Age is a significant determinant of TBI outcomes, with older cohorts recording higher mortality rates. Importantly, while certain features varied by age, the core characteristics of TBI manifestations tied to each phenotype remain consistent across diverse populations. Comment: 25 pages, 10 figures, 4 tables, submitted to Computers in Biology and Medicine
Keywords	Computer Science - Machine Learning ; Quantitative Biology - Quantitative Methods ; Statistics - Applications
Subject code	006
Publishing date	2024-01-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Hyperbolic Graph Neural Networks at Scale

Choudhary, Nurendra / Rao, Nikhil / Reddy, Chandan K.

A Meta Learning Approach

2023

Abstract: The progress in hyperbolic neural networks (HNNs) research is hindered by their absence of inductive bias mechanisms, which are essential for generalizing to new tasks and facilitating scalable learning over large datasets. In this paper, we aim to ... ...

Abstract	The progress in hyperbolic neural networks (HNNs) research is hindered by their absence of inductive bias mechanisms, which are essential for generalizing to new tasks and facilitating scalable learning over large datasets. In this paper, we aim to alleviate these issues by learning generalizable inductive biases from the nodes' local subgraph and transfer them for faster learning over new subgraphs with a disjoint set of nodes, edges, and labels in a few-shot setting. We introduce a novel method, Hyperbolic GRAph Meta Learner (H-GRAM), that, for the tasks of node classification and link prediction, learns transferable information from a set of support local subgraphs in the form of hyperbolic meta gradients and label hyperbolic protonets to enable faster learning over a query set of new tasks dealing with disjoint subgraphs. Furthermore, we show that an extension of our meta-learning framework also mitigates the scalability challenges seen in HNNs faced by existing approaches. Our comparative analysis shows that H-GRAM effectively learns and transfers information in multiple challenging few-shot settings compared to other state-of-the-art baselines. Additionally, we demonstrate that, unlike standard HNNs, our approach is able to scale over large graph datasets and improve performance over its Euclidean counterparts. Comment: Accepted to NeurIPS 2023. 14 pages of main paper, 5 pages of supplementary
Keywords	Computer Science - Machine Learning ; Computer Science - Social and Information Networks
Subject code	006
Publishing date	2023-10-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting

Liu, Zibo / Shojaee, Parshin / Reddy, Chandan K

2023

Abstract: There is a recent surge in the development of spatio-temporal forecasting models in the transportation domain. Long-range traffic forecasting, however, remains a challenging task due to the intricate and extensive spatio-temporal correlations observed in ...

Abstract	There is a recent surge in the development of spatio-temporal forecasting models in the transportation domain. Long-range traffic forecasting, however, remains a challenging task due to the intricate and extensive spatio-temporal correlations observed in traffic networks. Current works primarily rely on road networks with graph structures and learn representations using graph neural networks (GNNs), but this approach suffers from over-smoothing problem in deep architectures. To tackle this problem, recent methods introduced the combination of GNNs with residual connections or neural ordinary differential equations (ODE). However, current graph ODE models face two key limitations in feature extraction: (1) they lean towards global temporal patterns, overlooking local patterns that are important for unexpected events; and (2) they lack dynamic semantic edges in their architectural design. In this paper, we propose a novel architecture called Graph-based Multi-ODE Neural Networks (GRAM-ODE) which is designed with multiple connective ODE-GNN modules to learn better representations by capturing different views of complex local and global dynamic spatio-temporal dependencies. We also add some techniques like shared weights and divergence constraints into the intermediate layers of distinct ODE-GNN modules to further improve their communication towards the forecasting task. Our extensive set of experiments conducted on six real-world datasets demonstrate the superior performance of GRAM-ODE compared with state-of-the-art baselines as well as the contribution of different components to the overall performance. The code is available at https://github.com/zbliu98/GRAM-ODE Comment: Published in Transactions on Machine Learning Research, 2023
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2023-05-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Self-supervised Transformer for Multivariate Clinical Time-Series with Missing Values

Tipirneni, Sindhu / Reddy, Chandan K.

2021

Abstract: Multivariate time-series (MVTS) data are frequently observed in critical care settings and are typically characterized by excessive missingness and irregular time intervals. Existing approaches for learning representations in this domain handle such ... ...

Abstract	Multivariate time-series (MVTS) data are frequently observed in critical care settings and are typically characterized by excessive missingness and irregular time intervals. Existing approaches for learning representations in this domain handle such issues by either aggregation or imputation of values, which in-turn suppresses the fine-grained information and adds undesirable noise/overhead into the machine learning model. To tackle this challenge, we propose STraTS (Self-supervised Transformer for TimeSeries) model which bypasses these pitfalls by treating time-series as a set of observation triplets instead of using the traditional dense matrix representation. It employs a novel Continuous Value Embedding (CVE) technique to encode continuous time and variable values without the need for discretization. It is composed of a Transformer component with Multi-head attention layers which enables it to learn contextual triplet embeddings while avoiding problems of recurrence and vanishing gradients that occur in recurrent architectures. Many healthcare datasets also suffer from the limited availability of labeled data. Our model utilizes self-supervision by leveraging unlabeled data to learn better representations by performing time-series forecasting as a self-supervision task. Experiments on real-world multivariate clinical time-series benchmark datasets show that STraTS shows better prediction performance than state-of-the-art methods for mortality prediction, especially when labeled data is limited. Finally, we also present an interpretable version of STraTS which can identify important measurements in the time-series data.
Keywords	Computer Science - Machine Learning ; I.2.1 ; I.2.6
Subject code	006
Publishing date	2021-07-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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