Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Analytical, Diagnostic and Therapeutic Techniques and Equipment (3)
- Biomedical Engineering and Bioengineering (3)
- Engineering (3)
- Business (2)
- Business Analytics (2)
-
- Life Sciences (2)
- Physical Sciences and Mathematics (2)
- Applied Statistics (1)
- Bioimaging and Biomedical Optics (1)
- Bioinformatics (1)
- Biomedical Devices and Instrumentation (1)
- Biomedical Informatics (1)
- Data Science (1)
- Diagnosis (1)
- Diseases (1)
- Electrical and Computer Engineering (1)
- Emergency Medicine (1)
- Endocrinology, Diabetes, and Metabolism (1)
- Medical Specialties (1)
- Multivariate Analysis (1)
- Other Analytical, Diagnostic and Therapeutic Techniques and Equipment (1)
- Other Public Health (1)
- Preventive Medicine (1)
- Public Health (1)
- Science and Technology Studies (1)
- Social and Behavioral Sciences (1)
- Statistical Models (1)
- Statistics and Probability (1)
- Technology and Innovation (1)
- File Type
Articles 1 - 9 of 9
Full-Text Articles in Medicine and Health Sciences
Multimode Point Spectroscopy For Food Authentication, Sayed Asaduzzaman, Nicholas Mackinnon, Hossein Kashani Zadeh
Multimode Point Spectroscopy For Food Authentication, Sayed Asaduzzaman, Nicholas Mackinnon, Hossein Kashani Zadeh
SDSU Data Science Symposium
Enhancing food quality measurement is a necessity to guarantee food safety and adherence to health regulations. Current methods involve lab testing which are time-consuming, costly, destructive and require skilled workers. Spectroscopy has the potential to overcome these challenges. This study employs a multi-mode point spectroscopy method to distinguish food products according to their spectral characteristics,. The system records fluorescence, excited at 365 and 405 nm, visible-near infrared (Vis-NIR) and short-wave infrared (SWIR) spectra. The three main subjects of the study are olive oil, milk, and honey. Samples were kept in a transparent cell culture pot, and Gray and White Spectralon …
Session 12: Active Learning To Minimize The Possible Risk From Future Epidemics, Kc Santosh
Session 12: Active Learning To Minimize The Possible Risk From Future Epidemics, Kc Santosh
SDSU Data Science Symposium
In medical imaging informatics, for any future epidemics (e.g., Covid-19), deep learning (DL) models are of no use as they require a large dataset as they take months and even years to collect enough data (with annotations). In such a context, active learning (or human/expert-in-the-loop) is the must, where a machine can learn from the first day with minimum possible labeled data. In unsupervised learning, we propose to build pre-trained DL models that iteratively learn independently over time, where human/expert intervenes only when it makes mistakes and for only a limited data. In our work, deep features are used to …
2d Respiratory Sound Analysis To Detect Lung Abnormalities, Rafia Sharmin Alice, Kc Santosh
2d Respiratory Sound Analysis To Detect Lung Abnormalities, Rafia Sharmin Alice, Kc Santosh
SDSU Data Science Symposium
Abstract. In this paper, we analyze deep visual features from 2D data representation(s) of the respiratory sound to detect evidence of lung abnormalities. The primary motivation behind this is that visual cues are more important in decision-making than raw data (lung sound). Early detection and prompt treatments are essential for any future possible respiratory disorders, and respiratory sound is proven to be one of the biomarkers. In contrast to state-of-the-art approaches, we aim at understanding/analyzing visual features using our Convolutional Neural Networks (CNN) tailored Deep Learning Models, where we consider all possible 2D data such as Spectrogram, Mel-frequency Cepstral Coefficients …
Generalized Estimating Equations (Gee) Approach For Clustered Binary Data With Application To Covid-19 Treatment., Sadixa Sanjel
Generalized Estimating Equations (Gee) Approach For Clustered Binary Data With Application To Covid-19 Treatment., Sadixa Sanjel
SDSU Data Science Symposium
Clustered binary data frequently occur in epidemiology and other applied fields such as clinical trial studies, where observations within the respective samples are correlated. In such situations, the standard logistic regression method is not valid as logistic regression requires the observations to be independent of one other. This situation arises when treating COVD-19 patients. Patients from certain clusters, such as geographic areas or the same family, are highly correlated, and we need to fit the model using the GEE approach. In this paper, Standard Logistic Regression (LS), Generalized Linear Models (GENMOD), and GEE procedures have been utilized for comparison purposes. …
General Adversarial Networks In Tumor-Related Research: A Review And Agenda For Moving Forward, Andrew James Behrens, Cherie Noteboom
General Adversarial Networks In Tumor-Related Research: A Review And Agenda For Moving Forward, Andrew James Behrens, Cherie Noteboom
SDSU Data Science Symposium
Recent advances in Generative Adversarial Networks (GANs) have led to many new variants and uses of GANs. The latest advancements have allowed researchers and practitioners to apply this technique to tumor-related problems with limited data. One of the trends in this problem domain is to develop different variants of GANs suited explicitly to particular problems. The variants of GANs are numerous but share a common characteristic of expanding the dataset by creating synthetic data from the original dataset. This paper aims to develop a research agenda through a systematic literature review that investigates practitioners' and researchers' emerging issues and current …
A Comparative Study Of Machine Learning Approaches For Human Activity Recognition, Loknath Ambati, Omar El-Gayar
A Comparative Study Of Machine Learning Approaches For Human Activity Recognition, Loknath Ambati, Omar El-Gayar
SDSU Data Science Symposium
The goal of this project is to study the performance of Machine Learning (ML) techniques used in Human Activity Recognition (HAR). Specifically, we aim to 1) evaluate and benchmark the performance of various ML techniques used for HAR against established ML performance metrics using multiple datasets, and 2) map the characteristics of various HAR datasets to appropriate ML techniques. From a theoretical perspective, the research will shed light into the strengths and weaknesses of various ML techniques that can provide insights into future research aimed at improving these techniques for HAR. From a practical perspective, the research provides guidance into …
Foreign Object Detection And Localization In Chest X-Rays Using Deep Learning, Amul Neupane, Kc Santosh
Foreign Object Detection And Localization In Chest X-Rays Using Deep Learning, Amul Neupane, Kc Santosh
SDSU Data Science Symposium
Pulmonary abnormalities, such as Tuberculosis (TB), Asthma and/or Chronic obstructive are global threats. Nearly 1.6 million died from TB alone according to the WHO (World Health Organization) report 2019. Computer scientists together with medical experts have designed and reported automated screening systems for chest X-ray (CXR) images. However, most of the research works did not consider detecting foreign objects, such as buttons, coins, ring, pins, bone pieces and other medical devices (e.g. pacemaker) all together that can hinder the performance of automatic screening system. The circle-like foreign objects, such as coins are often confused with nodules, which is one of …
Predicting Unplanned Medical Visits Among Patients With Diabetes Using Machine Learning, Arielle Selya, Eric L. Johnson
Predicting Unplanned Medical Visits Among Patients With Diabetes Using Machine Learning, Arielle Selya, Eric L. Johnson
SDSU Data Science Symposium
Diabetes poses a variety of medical complications to patients, resulting in a high rate of unplanned medical visits, which are costly to patients and healthcare providers alike. However, unplanned medical visits by their nature are very difficult to predict. The current project draws upon electronic health records (EMR’s) of adult patients with diabetes who received care at Sanford Health between 2014 and 2017. Various machine learning methods were used to predict which patients have had an unplanned medical visit based on a variety of EMR variables (age, BMI, blood pressure, # of prescriptions, # of diagnoses on problem list, A1C, …
Minque: An R Package For Analyzing Various Linear Mixed Models, Jixiang Wu
Minque: An R Package For Analyzing Various Linear Mixed Models, Jixiang Wu
SDSU Data Science Symposium
Linear mixed model (LMM) approaches offer much more flexibility comparing ANOVA (analysis of variance) based methods. There are three commonly used LMM approaches: maximum likelihood, restricted maximum likelihood, and minimum norm quadratic unbiased estimation. These three approaches, however, sometimes could also lead low testing power compared to ANOVA methods. Integration of resampling techniques like jackknife could help improve testing power based on both our simulation studies. In this presentation, I will introduce a R package, minque, which integrates LMM approaches and resampling techniques and demonstrate the use of this packages in various linear mixed model analyses.