Peter Washington
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View article: Compact subsets of autism screening items predict clinical diagnoses with a machine learning analysis of the QCHAT-10
Compact subsets of autism screening items predict clinical diagnoses with a machine learning analysis of the QCHAT-10 Open
Early identification improves life outcomes for individuals with autism. This study addresses a central question: do compact subsets of the most predictive QCHAT-10 items, when fed into machine learning (ML) models trained to reproduce the…
View article: Digital information-seeking behaviors among cancer survivors: associations with sociodemographic determinants, cancer history, and perceived health
Digital information-seeking behaviors among cancer survivors: associations with sociodemographic determinants, cancer history, and perceived health Open
Purpose This study aimed to examine general and digital information-seeking behaviors among U.S. cancer survivors and assess how these behaviors are patterned by sociodemographic and clinical factors. The study addressed a key gap in prior…
View article: Multimodal LLM vs. Human-Measured Features for AI Predictions of Autism in Home Videos
Multimodal LLM vs. Human-Measured Features for AI Predictions of Autism in Home Videos Open
Autism diagnosis remains a critical healthcare challenge, with current assessments contributing to average diagnostic ages of 5 and extending to 8 in underserved populations. With the FDA approval of CanvasDx in 2021, the paradigm of human…
View article: mHealth technologies in research studying cardiovascular health in cancer: A systematic review
mHealth technologies in research studying cardiovascular health in cancer: A systematic review Open
Cancer survivors face an increased risk of cardiovascular disease (CVD) due to treatment-related toxicity, lifestyle factors, and comorbidities. Addressing CV health is crucial for improving quality of life and long-term outcomes. The Amer…
View article: Quantifying device type and handedness biases in a remote Parkinson’s disease AI-powered assessment
Quantifying device type and handedness biases in a remote Parkinson’s disease AI-powered assessment Open
We investigate issues pertaining to algorithmic fairness and digital health equity within the context of using machine learning to predict Parkinson’s Disease (PD) with data recorded from structured assessments of finger and hand movements…
View article: Aiding Large Language Models Using Clinical Scoresheets for Neurobehavioral Diagnostic Classification From Text: Algorithm Development and Validation
Aiding Large Language Models Using Clinical Scoresheets for Neurobehavioral Diagnostic Classification From Text: Algorithm Development and Validation Open
Background Large language models (LLMs) have demonstrated the ability to perform complex tasks traditionally requiring human intelligence. However, their use in automated diagnostics for psychiatry and behavioral sciences remains under-stu…
View article: Personalization of AI Using Personal Foundation Models Can Lead to More Precise Digital Therapeutics
Personalization of AI Using Personal Foundation Models Can Lead to More Precise Digital Therapeutics Open
Digital health interventions often use machine learning (ML) models to make predictions of repeated adverse health events. For example, models may be used to analyze patient data to identify patterns that can anticipate the likelihood of d…
View article: Associations Between Social Determinants of Health and Adherence in Mobile-Based Ecological Momentary Assessment: Scoping Review
Associations Between Social Determinants of Health and Adherence in Mobile-Based Ecological Momentary Assessment: Scoping Review Open
Background Ecological momentary assessment (EMA) involves repeated prompts to capture real-time self-reported health outcomes and behaviors via mobile devices. With the rise of mobile health (mHealth) technologies, EMA has been applied acr…
View article: Current challenges and opportunities in active and passive data collection for mobile health sensing: a scoping review
Current challenges and opportunities in active and passive data collection for mobile health sensing: a scoping review Open
Objective Mobile and ubiquitous devices enable health data collection “in a free-living environment” to support applications such as remote patient monitoring and adaptive digital interventions using machine learning (ML). Despite their po…
View article: Comparing the Accuracy of Different Wearable Activity Monitors in Patients With Lung Cancer and Providing Initial Recommendations: Protocol for a Pilot Validation Study
Comparing the Accuracy of Different Wearable Activity Monitors in Patients With Lung Cancer and Providing Initial Recommendations: Protocol for a Pilot Validation Study Open
Background Wearable activity monitors (WAMs) provide insights into physical activity (PA) and are widely used in behavioral interventions and cancer survivorship research. However, validation studies of wearable devices in populations with…
View article: Aiding Large Language Models Using Clinical Scoresheets for Neurobehavioral Diagnostic Classification From Text: Algorithm Development and Validation (Preprint)
Aiding Large Language Models Using Clinical Scoresheets for Neurobehavioral Diagnostic Classification From Text: Algorithm Development and Validation (Preprint) Open
BACKGROUND Large language models (LLMs) have demonstrated the ability to perform complex tasks traditionally requiring human intelligence. However, their use in automated diagnostics for psychiatry and behavioral sciences remains under-st…
View article: Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review
Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review Open
The development of digital instruments for mental health monitoring using biosensor data from wearable devices can enable remote, longitudinal, and objective quantitative benchmarks. To survey developments and trends in this field, we cond…
View article: Quantifying Device Type and Handedness Biases in a Remote Parkinson’s Disease AI-Powered Assessment
Quantifying Device Type and Handedness Biases in a Remote Parkinson’s Disease AI-Powered Assessment Open
Early detection of Parkinson’s Disease (PD) can enable early access to care, improving patient outcomes. We investigate the use of machine learning to predict PD using data recorded from a web application measuring structured mouse and key…
View article: Analysis of Physical Activity Using Wearable Health Technology in US Adults Enrolled in the All of Us Research Program: Multiyear Observational Study
Analysis of Physical Activity Using Wearable Health Technology in US Adults Enrolled in the All of Us Research Program: Multiyear Observational Study Open
Background To date, no studies have examined adherence to the 2018 Physical Activity Guidelines for Americans (PAGA) in real-world longitudinal settings using objectively measured activity monitoring data. This study addresses this gap by …
View article: Systematic Review of the Influence of Sociocultural Determinants of Health on Mobile-based Ecological Momentary Assessment Studies (Preprint)
Systematic Review of the Influence of Sociocultural Determinants of Health on Mobile-based Ecological Momentary Assessment Studies (Preprint) Open
BACKGROUND Ecological momentary assessment (EMA) involves repeated, real-time prompts for self-reported behaviors in the real world. Due to the widespread availability of mobile devices, mobile-based EMA is a research method commonly used…
View article: Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing
Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing Open
Substance use disorders affect 17.3% of Americans. Digital health solutions that use machine learning to detect substance use from wearable biosignal data can eventually pave the way for real-time digital interventions. However, difficulti…
View article: Challenges in the Differential Classification of Individual Diagnoses from Co-Occurring Autism and ADHD Using Survey Data
Challenges in the Differential Classification of Individual Diagnoses from Co-Occurring Autism and ADHD Using Survey Data Open
Autism and Attention-Deficit Hyperactivity Disorder (ADHD) are two of the most commonly observed neurodevelopmental conditions in childhood. Providing a specific computational assessment to distinguish between the two can prove difficult a…
View article: Evaluating Multicultural Autism Screening for Toddlers Using Machine Learning on the QCHAT-10
Evaluating Multicultural Autism Screening for Toddlers Using Machine Learning on the QCHAT-10 Open
Early identification and intervention often leads to improved life outcomes for individuals with Autism Spectrum Disorder (ASD). However, traditional diagnostic methods are time-consuming, frequently delaying treatment. This study examines…
View article: Challenges in the Differential Classification of Individual Diagnoses from Co-Occurring Autism and ADHD Using Survey Data
Challenges in the Differential Classification of Individual Diagnoses from Co-Occurring Autism and ADHD Using Survey Data Open
Autism and Attention-Deficit Hyperactivity Disorder (ADHD) are two of the most commonly observed neurodevelopmental conditions in childhood. Providing a specific computational assessment to distinguish between the two can prove difficult a…
View article: Public Health Using Social Network Analysis During the COVID-19 Era: A Systematic Review
Public Health Using Social Network Analysis During the COVID-19 Era: A Systematic Review Open
Social network analysis (SNA), or the application of network analysis techniques to social media data, is an increasingly prominent approach used in computational public health research. We conducted a systematic review to investigate tren…
View article: Deep Learning Prediction of Parkinson’s Disease using Remotely Collected Structured Mouse Trace Data
Deep Learning Prediction of Parkinson’s Disease using Remotely Collected Structured Mouse Trace Data Open
Parkinson’s Disease (PD) is the second most common neurodegenerative disorder globally, and current screening methods often rely on subjective evaluations. We developed deep learning-based classification models using structured mouse trace…
View article: Design Guidelines for Improving Mobile Sensing Data Collection: Prospective Mixed Methods Study
Design Guidelines for Improving Mobile Sensing Data Collection: Prospective Mixed Methods Study Open
Background Machine learning models often use passively recorded sensor data streams as inputs to train machine learning models that predict outcomes captured through ecological momentary assessments (EMA). Despite the growth of mobile data…
View article: Ensemble Modeling of Multiple Physical Indicators to Dynamically Phenotype Autism Spectrum Disorder
Ensemble Modeling of Multiple Physical Indicators to Dynamically Phenotype Autism Spectrum Disorder Open
Early detection of autism, a neurodevelopmental disorder marked by social communication challenges, is crucial for timely intervention. Recent advancements have utilized naturalistic home videos captured via the mobile application GuessWha…
View article: Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts
Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts Open
We aim to evaluate the efficacy of traditional machine learning and large language models (LLMs) in classifying anxiety and depression from long conversational transcripts. We fine-tune both established transformer models (BERT, RoBERTa, L…
View article: Addendum: Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study
Addendum: Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study Open
[This corrects the article DOI: 10.2196/52660.].
View article: Fair and Accessible Parkinson’s Disease Screening using a Machine Learning-Powered Web Platform: Research Protocol and Preliminary Results
Fair and Accessible Parkinson’s Disease Screening using a Machine Learning-Powered Web Platform: Research Protocol and Preliminary Results Open
Digital technologies offer unprecedented opportunities to screen for conditions like Parkinson’s Disease (PD) in a scalable and accessible manner. With the widespread adoption of smartphones and computers, the general public is constantly …
View article: Multimodal deep learning for dementia classification using text and audio
Multimodal deep learning for dementia classification using text and audio Open
Dementia is a progressive neurological disorder that affects the daily lives of older adults, impacting their verbal communication and cognitive function. Early diagnosis is important to enhance the lifespan and quality of life for affecte…
View article: Ethics of the Use of Social Media as Training Data for AI Models Used for Digital Phenotyping
Ethics of the Use of Social Media as Training Data for AI Models Used for Digital Phenotyping Open
Digital phenotyping, or personal sensing, is a field of research that seeks to quantify traits and characteristics of people using digital technologies, usually for health care purposes. In this commentary, we discuss emerging ethical issu…
View article: Addendum: Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study (Preprint)
Addendum: Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study (Preprint) Open
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