Description

An onsite recording of this session will be included in the Symposium OnDemand offering. 

Empowering Patient-Centric Data Management in Healthcare Using Blockchain-based Self-Sovereign Identity and Non-Fungible Tokens

Presentation Time: 03:30 PM - 03:45 PM

Abstract Keywords: Self-care/Management/Monitoring, Interoperability and Health Information Exchange, Patient Engagement and Preferences
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics

Patient tokenization, leveraging non-fungible tokens and self-sovereign identity on blockchain technology, represents a transformative approach for secure, anonymous patient data linkage across diverse healthcare domains, including medical, dental, and beyond. This study demonstrates the feasibility of this innovative system through a case study involving over three million transactions, showcasing its potential to fundamentally reshape identity management and health information exchange in a patient-centric manner. This work showcases its transformative potential across various healthcare domains.

Speaker(s):
Yan Zhuang, Ph.D.
Indiana University

Author(s):
Zhen Hou, MS - Indiana University;

Enhancing Wearable Sensor Data Classification Through Novel Modified-Recurrent Plot-Based Image Representation and Mixup Augmentation

Presentation Time: 03:45 PM - 04:00 PM

Abstract Keywords: Deep Learning, Biomarkers, Self-care/Management/Monitoring, Chronic Care Management, Machine Learning
Primary Track: Applications
Programmatic Theme: Translational Bioinformatics

Deep learning advancements have revolutionized scalable classification in many domains including computer vision, healthcare and Natural Language Processing (NLP). However, when it comes to classification and domain adaptation based on wearables, it suffers from persistent underperformance, largely due to the scarcity of pre-trained deep learning models that are abundantly available for computer vision and NLP. This is primarily because wearable sensor data need sensor-specific preprocessing, architectural modification, and extensive data collection. We present a novel modified-recurrent plot-based image representation that seamlessly integrates both temporal and frequency domain information. We employ an efficient Fourier Transform-based frequency domain angular difference estimation scheme in conjunction with existing temporal recurrent plots. We validated proposed method in two different domains: accelerometer-based activity-recognition and real-time glucose level prediction from wearable sensors. Our findings demonstrated the method we developed not only improves accuracy at recognizing activity but also makes a big leap in glucose level prediction.

Speaker(s):
Mahmudur Rahman, PhD
University of Wisconsin-Madison

Author(s):

Voice-Activated Self-Monitoring Application (VoiS): Perspectives from People with Diabetes and Hypertension

Presentation Time: 04:00 PM - 04:15 PM

Abstract Keywords: Tracking and Self-management Systems, Mobile Health, Usability, Self-care/Management/Monitoring
Primary Track: Applications
Programmatic Theme: Consumer Health Informatics

This paper describes the development and usability test processes of the voice-activated self-monitoring (VoiS) application which is purposed to support the self-management of people with both diabetes and hypertension. VoiS is an innovative, theory-driven mobile app on a smart speaker platform to support people with coexisting diabetes and hypertension to self-monitor blood pressures, glucose levels, and health behaviors routinely and conveniently, and to improve the quality of communication with healthcare providers. The prototype of VoiS includes voice interaction with Amazon Alexa and data representation using smartphones (iOS and Android). A total of 14 people with coexisting diabetes and hypertension participated in usability testing. After completing a range of tasks individually, testers participated in group interviews. We used a survey based on the Technology Acceptance Model to measure the ease of use and perceived usefulness of VoiS. All interviews were recorded and transcribed, and then common themes were extracted. Participants found VoiS to be easy to use and useful.

Speaker(s):
Li Yang, MSM
University of Wisconsin-Milwaukee

Author(s):
Hyunkyoung Oh, PhD - University of Wisconsin Milwaukee; Li Yang, MSM - UWM School of Information Studies; Tala Abu Zahra, BSN - UWM School of Nursing; Masud Rabbani, B.Sc. - Marquette University; Shiyu Tian, MS - Marquette University; Adib Ahmed Anik, B.Sc. - Marquette University; Paramita Basak Upama, MS - Marquette University; Min Sook Park, PhD - UWM School of Information Studies; Jake Luo, PhD - UWM College of Engineering & Applied Science; Evelyn Chan, MD - Medical College of Wisconsin; Jeff Whittle, MD - Medical College of Wisconsin; Sheikh Iqbal Ahamed, PhD - Marquette University;

Integrating Remote Patient Monitoring Data into Machine Learning Models for Predicting Emergency Department Utilization

Presentation Time: 04:15 PM - 04:30 PM

Abstract Keywords: Machine Learning, Self-care/Management/Monitoring, Patient / Person Generated Health Data (Patient Reported Outcomes), Chronic Care Management, Population Health
Primary Track: Applications

The integration of Remote Patient Monitoring (RPM) data into risk stratification models has emerged as a promising approach for improving healthcare delivery and patient outcomes. In this work, we explore the integration of RPM features – including at home monitoring of body weight, blood pressure, and blood oxygen – into a machine learning model that uses EHR data to predict the likelihood of emergency department (ED) visits or unplanned inpatient admissions within the next 30 days. Through exploratory data analysis, feature engineering, model training, and evaluation of a dataset with 913 patients, we found that RPM data has signal to predict unplanned utilization, and combining RPM data with EHR data improves the predictive power of the model, compared with either data source alone. We discuss the transformative potential of RPM data to augment predictive analytics capabilities in care management settings.

Speaker(s):
Ashika Farzana, MS
Geisinger

Author(s):
Ashika Farzana, MS - Geisinger; Satish Kalepalli, MS - Geisinger; Grant DeLong; Vishal Mehra, MD, PhD - Geisinger; Emily Fry, MHA - Geisinger; David Vawdrey, PhD - Geisinger; Elliot Mitchell, PhD - Geisinger;

Detection of Short-Form Video Addiction with Wearable Sensors via Temporally-Coherent Domain Adaptation

Presentation Time: 04:30 PM - 04:45 PM

Abstract Keywords: Mobile Health, Deep Learning, Ubiquitous Computing and Sensors
Primary Track: Applications
Programmatic Theme: Translational Bioinformatics

Short-form Video Addiction (SVA), a novel digital addiction of the modern world, proliferates among young adults and is not formally diagnosable. SVA detection from resulting bio-signals is crucial to prevent its adverse impacts. Existing formal methods involve large and expensive neuro-imaging devices in laboratory setups that are intrusive and not feasible to use in daily life. A possible non-intrusive solution can be using wearable sensors which is challenging due to the resulting noisy and faint signals. To address this problem, we investigate multi-modal wearable sensing technology to detect SVA in a non-intrusive fashion. However, fusing multi-modal sensors effectively presents different challenges due to the presence of signal heterogeneity. In this study, we propose a novel multi-modal temporally coherent domain adaptation method to effectively detect SVA using Electroencephalogram (EEG) and Electrodermal Activity (EDA) sensors. We also investigate the nature and properties of SVA with the help of different components of EEG and EDA signals. We evaluate our proposed method for SVA detection and fatigue assessment tasks. Experimental evaluation posits the proposed model's superior performance (10% accuracy) over state-of-art domain adaptation models.

Speaker(s):
Mahmudur Rahman, PhD
University of Wisconsin-Madison

Author(s):
Mahmudur Rahman, PhD - University of Wisconsin-Madison; Atqiya Munawara Mahi, MS - University of Massachusetts Lowell; Sharmin Sultana, Ph.D. Student - University of Massachusetts Lowell; Matthew Churpek, MD, MPH, PhD - University of Wisconsin-Madison; Mohammad Arif Ul Alam, Assistant Professor/PhD - University of Massachusetts Lowell;

“I worry we’ll blow right by it” Barriers to Uptake of the STRATIFY CDSS for ED Discharge in Acute Heart Failure

Presentation Time: 04:45 PM - 05:00 PM

Abstract Keywords: Clinical Decision Support, Usability, Qualitative Methods, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Clinical Informatics

We recently implemented a clinical decision support system (CDSS) to identify patients in the emergency department (ED) with acute heart failure that may be safe for discharge instead of the typical costly hospitalization. Despite user-centered-design initial tool uptake was low. To explore barriers to use we interviewed 10 ED clinicians with a case-simulation. Usability issues around tool launch, instead of the tool itself, along with low familiarity of evidence supporting the CDSS drove low uptake.

Speaker(s):
Matthew Christensen, MD
Vanderbilt University Medical Center

Author(s):
Matthew Christensen, MD - Vanderbilt University Medical Center; Shilo Anders, PhD - Vanderbilt University Medical Center; Carrie Reale, MSN, RN-BC - Vanderbilt University Medical Center; Tim Coffman, BS - Vanderbilt University Medical Center; Hala Alaw, BS - Vanderbilt University Medical Center; Janos Mathe, PhD - Vanderbilt University Medical Center; Dan Albert - Vanderbilt University Medical Center; Allison McCoy, PhD, ACHIP, FACMI, FAMIA - Vanderbilt University Medical Center; Anna Sachs, MPH - Vanderbilt University Medical Center; Dandan Liu, PhD - Vanderbilt University Medical Center; Sunil Kripalani, MD, MSc - Vanderbilt University Medical Center; Laurie Novak, PhD - Vanderbilt University Medical Center Dept of Biomedical Informatics;