Browsing by Author "Bhimireddy, Ananth"

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    Blood Glucose Level Prediction as Time-Series Modeling using Sequence-to-Sequence Neural Networks
    (CEUR Workshop Proceedings, 2020-08) Bhimireddy, Ananth; Sinha, Priyanshu; Oluwalade, Bolu; Gichoya, Judy Wawira; Purkayastha, Saptarshi; BioHealth Informatics, School of Informatics and Computing
    The management of blood glucose levels is critical in the care of Type 1 diabetes subjects. In extremes, high or low levels of blood glucose are fatal. To avoid such adverse events, there is the development and adoption of wearable technologies that continuously monitor blood glucose and administer insulin. This technology allows subjects to easily track their blood glucose levels with early intervention without the need for hospital visits. The data collected from these sensors is an excellent candidate for the application of machine learning algorithms to learn patterns and predict future values of blood glucose levels. In this study, we developed artificial neural network algorithms based on the OhioT1DM training dataset that contains data on 12 subjects. The dataset contains features such as subject identifiers, continuous glucose monitoring data obtained in 5 minutes intervals, insulin infusion rate, etc. We developed individual models, including LSTM, BiLSTM, Convolutional LSTMs, TCN, and sequence-to-sequence models. We also developed transfer learning models based on the most important features of the data, as identified by a gradient boosting algorithm. These models were evaluated on the OhioT1DM test dataset that contains 6 unique subject’s data. The model with the lowest RMSE values for the 30- and 60-minutes was selected as the best performing model. Our result shows that sequence-to-sequence BiLSTM performed better than the other models. This work demonstrates the potential of artificial neural networks algorithms in the management of Type 1 diabetes.
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    PLHI-MC10: A dataset of exercise activities captured through a triple synchronous medically-approved sensor
    (Elsevier, 2021-10) Mahajan, Yohan; Bhimireddy, Ananth; Abid, Areeba; Gichoya, Judy W.; Purkayastha, Saptarshi; BioHealth Informatics, School of Informatics and Computing
    Most human activity recognition datasets that are publicly available have data captured by using either smartphones or smartwatches, which are usually placed on the waist or the wrist, respectively. These devices obtain one set of acceleration and angular velocity in the x-, y-, and z-axis from the accelerometer and the gyroscope planted in these devices. The PLHI-MC10 dataset contains data obtained by using 3 BioStamp nPoint® sensors from 7 physically healthy adult test subjects performing different exercise activities. These sensors are the state-of-the-art biomedical sensors manufactured by MC10. Each of the three sensors was attached to the subject externally on three muscles-Extensor Digitorum (Posterior Forearm), Gastrocnemius (Calf), and Pectoralis (Chest)-giving us three sets of 3 axial acceleration, two sets of 3 axial angular velocities, and 1 set of voltage values from the heart. Using three different sensors instead of a single sensor improves precision. It helps distinguish between human activities as it simultaneously captures the movement and contractions of various muscles from separate parts of the human body. Each test subject performed five activities (stairs, jogging, skipping, lifting kettlebell, basketball throws) in a supervised environment. The data is cleaned, filtered, and synced.
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    Using ImageBERT to improve performance of multi-class Chest Xray classification
    (2020-07-02) Purkayastha, Saptarshi; Bhimireddy, Ananth; Sinha, Priyanshu; Gichoya, Judy W.
    Pulmonary edema is a medical condition that is often related to life-threatening heart-related complications. Several recent studies have demonstrated that machine learning models using deep learning (DL) methods are able to identify anomalies on chest X-rays (CXR) as well as trained radiologists. Yet, there are limited/no studies that have integrated these models in clinical radiology workflows. The objective of this project is to identify state-of-the-art DL algorithms and integrate the classification results into the radiology workflow, more specifically in a DICOM Viewer, so that radiologists can use it as a clinical decision support. Our proof-of-concept (POC) is to detect the presence/absence of edema in chest radiographs obtained from the CheXpert dataset. We implemented the state-of-the-art deep learning methods for image classification -ResNet50, VGG16 and Inception v4 using the FastAI library and PyTorch on 77,408 CXR which have classified the presence/absence of edema in the images with an accuracy of 65%, 70% and 65% respectively on a test dataset of about 202 images. The CXR were converted to DICOM format using the img2dcm utility of DICOM ToolKit (DCMTK), and later uploaded to the Orthanc PACS, which was connected to the OHIF Viewer. This is the first study that has integrated the machine learning outcomes into the clinical workflow in order to improve the decision-making process by implementing object detection and instance segmentation algorithms.