449 citations · 520 across the 22 of their papers we have counts for
26 papers
ChatGPT for Us: Preserving Data Privacy in ChatGPT via Dialogue Text Ambiguation to Expand Mental Health Care Delivery
Anaelia Ovalle, Mehrab Beikzadeh, Parshan Teimouri +2
Large language models have been useful in expanding mental health care delivery. ChatGPT, in particular, has gained popularity for its ability to generate human-like dialogue. Howe…
A Self-supervised Framework for Improved Data-Driven Monitoring of Stress via Multi-modal Passive Sensing
Shayan Fazeli, Lionel Levine, Mehrab Beikzadeh +4
Recent advances in remote health monitoring systems have significantly benefited patients and played a crucial role in improving their quality of life. However, while physiological…
Auditing Algorithmic Fairness in Machine Learning for Health with Severity-Based LOGAN
Anaelia Ovalle, Sunipa Dev, Jieyu Zhao +2
Auditing machine learning-based (ML) healthcare tools for bias is critical to preventing patient harm, especially in communities that disproportionately face health inequities. Gen…
Beyond Labels: Visual Representations for Bone Marrow Cell Morphology Recognition
Shayan Fazeli, Alireza Samiei, Thomas D. Lee +1
Analyzing and inspecting bone marrow cell cytomorphology is a critical but highly complex and time-consuming component of hematopathology diagnosis. Recent advancements in artifici…
Contrastive Mixup: Self- and Semi-Supervised learning for Tabular Domain
Sajad Darabi, Shayan Fazeli, Ali Pazoki +2
Recent literature in self-supervised has demonstrated significant progress in closing the gap between supervised and unsupervised methods in the image and text domains. These metho…
A Framework for Neural Topic Modeling of Text Corpora
Shayan Fazeli, Majid Sarrafzadeh
Topic Modeling refers to the problem of discovering the main topics that have occurred in corpora of textual data, with solutions finding crucial applications in numerous fields. I…
Unsupervised Acute Intracranial Hemorrhage Segmentation with Mixture Models
Kimmo Kärkkäinen, Shayan Fazeli, Majid Sarrafzadeh
Intracranial hemorrhage occurs when blood vessels rupture or leak within the brain tissue or elsewhere inside the skull. It can be caused by physical trauma or by various medical c…
COVID-19 and Big Data: Multi-faceted Analysis for Spatio-temporal Understanding of the Pandemic with Social Media Conversations
Shayan Fazeli, Davina Zamanzadeh, Anaelia Ovalle +3
COVID-19 has been devastating the world since the end of 2019 and has continued to play a significant role in major national and worldwide events, and consequently, the news. In it…
Real-Time Decentralized knowledge Transfer at the Edge
Orpaz Goldstein, Mohammad Kachuee, Derek Shiell +1
The proliferation of edge networks creates islands of learning agents working on local streams of data. Transferring knowledge between these agents in real-time without exposing pr…
Anxiety Detection Leveraging Mobile Passive Sensing
Lionel Levine, Migyeong Gwak, Kimmo Karkkainen +5
Anxiety disorders are the most common class of psychiatric problems affecting both children and adults. However, tools to effectively monitor and manage anxiety are lacking, and co…
Statistical Analytics and Regional Representation Learning for COVID-19 Pandemic Understanding
Shayan Fazeli, Babak Moatamed, Majid Sarrafzadeh
The rapid spread of the novel coronavirus (COVID-19) has severely impacted almost all countries around the world. It not only has caused a tremendous burden on health-care provider…
Transfer Learning for Activity Recognition in Mobile Health
Yuchao Ma, Andrew T. Campbell, Diane J. Cook +6
While activity recognition from inertial sensors holds potential for mobile health, differences in sensing platforms and user movement patterns cause performance degradation. Aimin…
A Flexible and Intelligent Framework for Remote Health Monitoring Dashboards
Shayan Fazeli, Majid Sarrafzadeh
Developing and maintaining monitoring panels is undoubtedly the main task in the remote patient monitoring (RPM) systems. Due to the significant variations in desired functionaliti…
Hierarchical Target-Attentive Diagnosis Prediction in Heterogeneous Information Networks
Anahita Hosseini, Tyler Davis, Majid Sarrafzadeh
We introduce HTAD, a novel model for diagnosis prediction using Electronic Health Records (EHR) represented as Heterogeneous Information Networks. Recent studies on modeling EHR ha…
Cost-Sensitive Feature-Value Acquisition Using Feature Relevance
Kimmo Kärkkäinen, Mohammad Kachuee, Orpaz Goldstein +1
In many real-world machine learning problems, feature values are not readily available. To make predictions, some of the missing features have to be acquired, which can incur a cos…
Group-Connected Multilayer Perceptron Networks
Mohammad Kachuee, Sajad Darabi, Shayan Fazeli +1
Despite the success of deep learning in domains such as image, voice, and graphs, there has been little progress in deep representation learning for domains without a known structu…
Unsupervised Representation for EHR Signals and Codes as Patient Status Vector
Sajad Darabi, Mohammad Kachuee, Majid Sarrafzadeh
Effective modeling of electronic health records presents many challenges as they contain large amounts of irregularity most of which are due to the varying procedures and diagnosis…
Target-Focused Feature Selection Using a Bayesian Approach
Orpaz Goldstein, Mohammad Kachuee, Kimmo Karkkainen +1
In many real-world scenarios where data is high dimensional, test time acquisition of features is a non-trivial task due to costs associated with feature acquisition and evaluating…
TAPER: Time-Aware Patient EHR Representation
Sajad Darabi, Mohammad Kachuee, Shayan Fazeli +1
Effective representation learning of electronic health records is a challenging task and is becoming more important as the availability of such data is becoming pervasive. The data…
Generative Imputation and Stochastic Prediction
Mohammad Kachuee, Kimmo Karkkainen, Orpaz Goldstein +2
In many machine learning applications, we are faced with incomplete datasets. In the literature, missing data imputation techniques have been mostly concerned with filling missing…
Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams
Mohammad Kachuee, Orpaz Goldstein, Kimmo Karkkainen +2
In many real-world learning scenarios, features are only acquirable at a cost constrained under a budget. In this paper, we propose a novel approach for cost-sensitive feature acqu…
Cost-Sensitive Diagnosis and Learning Leveraging Public Health Data
Mohammad Kachuee, Kimmo Karkkainen, Orpaz Goldstein +2
Traditionally, machine learning algorithms rely on the assumption that all features of a given dataset are available for free. However, there are many concerns such as monetary dat…
Unsupervised Prediction of Negative Health Events Ahead of Time
Anahita Hosseini, Majid Sarrafzadeh
The emergence of continuous health monitoring and the availability of an enormous amount of time series data has provided a great opportunity for the advancement of personal health…
Dynamic Feature Acquisition Using Denoising Autoencoders
Mohammad Kachuee, Sajad Darabi, Babak Moatamed +1
In real-world scenarios, different features have different acquisition costs at test-time which necessitates cost-aware methods to optimize the cost and performance trade-off. This…