Browsing by Author "Jeganathan, J."

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COVIDDX: AI-based clinical decision support system for learning COVID-19 disease representations from multimodal patient data
(SciTePress, 2021) Mayya, V.; Karthik, K.; Kamath Sâ€¤, S.; Karadka, K.; Jeganathan, J.
The COVID-19 pandemic has affected the world on a global scale, infecting nearly 68 million people across the world, with over 1.5 million fatalities as of December 2020. A cost-effective early-screening strategy is crucial to prevent new outbreaks and to curtail the rapid spread. Chest X-ray images have been widely used to diagnose various lung conditions such as pneumonia, emphysema, broken ribs and cancer. In this work, we explore the utility of chest X-ray images and available expert-written diagnosis reports, for training neural network models to learn disease representations for diagnosis of COVID-19. A manually curated dataset consisting of 450 chest X-rays of COVID-19 patients and 2,000 non-COVID cases, along with their diagnosis reports were collected from reputed online sources. Convolutional neural network models were trained on this multimodal dataset, for prediction of COVID-19 induced pneumonia. A comprehensive clinical decision support system powered by ensemble deep learning models (CADNN) is designed and deployed on the web. The system also provides a relevance feedback mechanism through which it learns multimodal COVID-19 representations for supporting clinical decisions. Â© Â© 2021 by SCITEPRESS â€“ Science and Technology Publications, Lda. All rights reserved
FarSight: Long-Term Disease Prediction Using Unstructured Clinical Nursing Notes
(2020) Gangavarapu, T.; S, Krishnan, G.; Sowmya, Kamath S.; Jeganathan, J.
Accurate risk stratification using patient data is a vital task in channeling prioritized care. Most state-of-the-art models are predominantly reliant on digitized data in the form of structured Electronic Health Records (EHRs). Those models overlook the valuable patient-specific information embedded in unstructured clinical notes, which is the prevalent medium employed by caregivers to record patients' disease timeline. The availability of such patient-specific data presents an unprecedented opportunity to build intelligent systems that provide exclusive insights into patients' disease physiology. Moreover, very few works have attempted to benchmark the performance of deep neural architectures against the state-of-the-art models on publicly available datasets. This paper presents significant observations from our benchmarking experiments on the applicability of deep learning models for the clinical task of ICD-9 code group prediction. We present FarSight, a long-term aggregation mechanism intended to recognize the onset of the disease with the earliest detected symptoms. Vector space and topic modeling approaches are utilized to capture the semantic information in the patient representations. Experiments on MIMIC-III database underscored the superior performance of the proposed models built on unstructured data when compared to structured EHR based state-of-the-art model, achieving an improvement of 19.34% in AUPRC and 5.41% in AUROC. IEEE
FarSight: Long-Term Disease Prediction Using Unstructured Clinical Nursing Notes
(IEEE Computer Society, 2021) Gangavarapu, T.; S. Krishnan, G.S.; Kamath S?, S.; Jeganathan, J.
Accurate risk stratification using patient data is a vital task in channeling prioritized care. Most state-of-the-art models are predominantly reliant on digitized data in the form of structured Electronic Health Records (EHRs). Those models overlook the valuable patient-specific information embedded in unstructured clinical notes, which is the prevalent medium employed by caregivers to record patients' disease timeline. The availability of such patient-specific data presents an unprecedented opportunity to build intelligent systems that provide exclusive insights into patients' disease physiology. Moreover, very few works have attempted to benchmark the performance of deep neural architectures against the state-of-the-art models on publicly available datasets. This article presents significant observations from our benchmarking experiments on the applicability of deep learning models for the clinical task of ICD-9 code group prediction. We present FarSight, a long-term aggregation mechanism intended to recognize the onset of the disease with the earliest detected symptoms. Vector space and topic modeling approaches are utilized to capture the semantic information in the patient representations. Experiments on MIMIC-III database underscored the superior performance of the proposed models built on unstructured data when compared to structured EHR based state-of-the-art model, achieving an improvement of 19.34 percent in AUPRC and 5.41 percent in AUROC. © 2013 IEEE.
ICU Patients’ Pattern Recognition and Correlation Identification of Vital Parameters Using Optimized Machine Learning Models
(J.J. Strossmayer University of Osijek , Faculty of Electrical Engineering, Computer Science and Information Technology, 2023) Yallabandi, G.; Jeganathan, J.; Mayya, V.; Sowmya Kamath, S.
– Early detection of patient deterioration in the Intensive Care Unit (ICU) can play a crucial role in improving patient outcomes. Conventional severity scales currently used to predict patient deterioration are based on a number of factors, the majority of which consist of multiple investigations. Recent advancements in machine learning (ML) within the healthcare domain offer the potential to alleviate the burden of continuous patient monitoring. In this study, we propose an optimized ML model designed to leverage variations in vital signs observed during the final 24 hours of an ICU stay for outcome predictions. Further, we elucidate the relative contributions of distinct vital parameters to these outcomes The dataset compiled in real-time encompasses six pivotal vital parameters: systolic (0) and diastolic (1) blood pressure, pulse rate (2), respiratory rate (3), oxygen saturation (SpO2) (4), and temperature (5). Of these vital parameters, systolic blood pressure emerges as the most significant predictor associated with mortality prediction. Using a fivefold cross-validation method, several ML classifiers are used to categorize the last 24 hours of time series data after ICU admission into three groups: recovery, death, and intubation. Notably, the optimized Gradient Boosting classifier exhibited the highest performance in detecting mortality, achieving an area under the receiver-operator curve (AUC) of 0.95. Through the integration of electronic health records with this ML software, there is the promise of early notifications regarding adverse outcomes, potentially several hours before the onset of hemodynamic instability. © 2023, J.J. Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology. All rights reserved.