Arvind Pillai
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View article: Psychometric properties and validity of a Mobile Patient Health Questionnaire–9 (MPHQ-9) for ecological momentary assessment in depressed adults.
Psychometric properties and validity of a Mobile Patient Health Questionnaire–9 (MPHQ-9) for ecological momentary assessment in depressed adults. Open
Ecological momentary assessment is well-suited for capturing rapid symptom dynamics, and it is increasingly used to measure depression symptoms. However, few depression measures are validated for ecological momentary assessment use in the …
View article: Acute suicidal ideation in context: highlighting sentiment-based markers through the diary entries of a clinically depressed sample
Acute suicidal ideation in context: highlighting sentiment-based markers through the diary entries of a clinically depressed sample Open
This work provides an accessible exploratory framework that capitalizes on the benefits of dense EMA sampling and NLP to profile and quantify acute SI trajectories. The use of the MPHQ's item 9 to quantify SI is an important limitation as …
View article: Digital biomarkers of avoidance and their relationship with depression and anxiety symptoms
Digital biomarkers of avoidance and their relationship with depression and anxiety symptoms Open
Anxiety and depression are highly comorbid, warranting a need to understand transdiagnostic mechanisms contributing to the high rates of comorbidity. Prior work has implicated avoidance as a transdiagnostic mechanism; however, this has pri…
View article: Psychometric Properties and Validity of a Mobile Patient Health Questionnaire-9 (MPHQ-9) for Ecological Momentary Assessment in Depressed Adults
Psychometric Properties and Validity of a Mobile Patient Health Questionnaire-9 (MPHQ-9) for Ecological Momentary Assessment in Depressed Adults Open
Ecological momentary assessment (EMA) offers advantages over retrospective questionnaires by reducing recall bias and capturing rapid symptom dynamics, and it is increasingly used to measure depression symptoms. However, few depression sym…
View article: Psychometric Properties and Validity of a Mobile Patient Health Questionnaire-9 (MPHQ-9) for Ecological Momentary Assessment in Depressed Adults
Psychometric Properties and Validity of a Mobile Patient Health Questionnaire-9 (MPHQ-9) for Ecological Momentary Assessment in Depressed Adults Open
Ecological momentary assessment (EMA) offers advantages over retrospective questionnaires by reducing recall bias and capturing rapid symptom dynamics, and it is increasingly used to measure depression symptoms. However, few depression sym…
View article: Semantic signals in self-reference: The detection and prediction of depressive symptoms from the daily diary entries of a sample with major depressive disorder.
Semantic signals in self-reference: The detection and prediction of depressive symptoms from the daily diary entries of a sample with major depressive disorder. Open
Individuals with major depressive disorder (MDD) experience fewer positive and more negative emotions and use fewer positive words to describe themselves. Natural language processing techniques have been used to predict depression, with pr…
View article: Predicting weekly instability in depressive symptoms among individuals diagnosed with Major Depressive Disorder using deep learning and passively-collected movement data
Predicting weekly instability in depressive symptoms among individuals diagnosed with Major Depressive Disorder using deep learning and passively-collected movement data Open
Major Depressive Disorder (MDD) is a prevalent mental health disorder often identified by persistentlow mood, and a lack of motivation and energy. Persons with MDD often experience largefluctuations in their symptoms over hours and days, w…
View article: Time2Lang: Bridging Time-Series Foundation Models and Large Language Models for Health Sensing Beyond Prompting
Time2Lang: Bridging Time-Series Foundation Models and Large Language Models for Health Sensing Beyond Prompting Open
Large language models (LLMs) show promise for health applications when combined with behavioral sensing data. Traditional approaches convert sensor data into text prompts, but this process is prone to errors, computationally expensive, and…
View article: MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences
MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences Open
Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape explores a novel approach to AI-powered journaling by integrating…
View article: Assessing the Psychometric Properties and Validity of a Mobile Patient Health Questionnaire-9 (MPHQ-9) for Ecological Momentary Assessment
Assessing the Psychometric Properties and Validity of a Mobile Patient Health Questionnaire-9 (MPHQ-9) for Ecological Momentary Assessment Open
Ecological momentary assessment (EMA) offers advantages over retrospective questionnaires by reducing recall bias and capturing rapid symptom dynamics, and it is increasingly used to measure depression symptoms. However, few depression sym…
View article: Psychometric Properties and Validity of a Mobile Patient Health Questionnaire-9 (MPHQ-9) for Ecological Momentary Assessment in Depressed Adults
Psychometric Properties and Validity of a Mobile Patient Health Questionnaire-9 (MPHQ-9) for Ecological Momentary Assessment in Depressed Adults Open
Ecological momentary assessment (EMA) offers advantages over retrospective questionnaires by reducing recall bias and capturing rapid symptom dynamics, and it is increasingly used to measure depression symptoms. However, few depression sym…
View article: Psychometric Properties and Validity of a Mobile Patient Health Questionnaire-9 (MPHQ-9) for Ecological Momentary Assessment in Depressed Adults
Psychometric Properties and Validity of a Mobile Patient Health Questionnaire-9 (MPHQ-9) for Ecological Momentary Assessment in Depressed Adults Open
Ecological momentary assessment (EMA) offers advantages over retrospective questionnaires by reducing recall bias and capturing rapid symptom dynamics, and it is increasingly used to measure depression symptoms. However, few depression sym…
View article: Acute Suicidal Ideation in Context: Highlighting Sentiment-based Markers through the Diary Entries of a Clinically Depressed Sample
Acute Suicidal Ideation in Context: Highlighting Sentiment-based Markers through the Diary Entries of a Clinically Depressed Sample Open
Background: Despite major strides in conceptualizing and modeling the multifaceted nature of suicidal thought and behavior (STB) over the past few decades, the overall predictability of STB has not improved. This may be partly due to the d…
View article: MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences
MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences Open
Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape pioneers a novel approach to AI-powered journaling by integrating…
View article: Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape App
Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape App Open
MindScape aims to study the benefits of integrating time series behavioral patterns (e.g., conversational engagement, sleep, location) with Large Language Models (LLMs) to create a new form of contextual AI journaling, promoting self-refle…
View article: MoodCapture: Depression Detection using In-the-Wild Smartphone Images
MoodCapture: Depression Detection using In-the-Wild Smartphone Images Open
MoodCapture presents a novel approach that assesses depression based on images automatically captured from the front-facing camera of smartphones as people go about their daily lives. We collect over 125,000 photos in the wild from N=177 p…
View article: Capturing the College Experience
Capturing the College Experience Open
Understanding the dynamics of mental health among undergraduate students across the college years is of critical importance, particularly during a global pandemic. In our study, we track two cohorts of first-year students at Dartmouth Coll…
View article: Depressive symptoms as a heterogeneous and constantly evolving dynamical system: Idiographic depressive symptom networks of rapid symptom changes among persons with major depressive disorder.
Depressive symptoms as a heterogeneous and constantly evolving dynamical system: Idiographic depressive symptom networks of rapid symptom changes among persons with major depressive disorder. Open
Major depressive disorder (MDD) is conceptualized by individual symptoms occurring most of the day for at least two weeks. Despite this operationalization, MDD is highly variable with persons showing greater variation within and across day…
View article: Investigating Generalizability of Speech-based Suicidal Ideation Detection Using Mobile Phones
Investigating Generalizability of Speech-based Suicidal Ideation Detection Using Mobile Phones Open
Speech-based diaries from mobile phones can capture paralinguistic patterns that help detect mental illness symptoms such as suicidal ideation. However, previous studies have primarily evaluated machine learning models on a single dataset,…
View article: Social Isolation and Serious Mental Illness: The Role of Context-Aware Mobile Interventions
Social Isolation and Serious Mental Illness: The Role of Context-Aware Mobile Interventions Open
Social isolation is a common problem faced by individuals with serious mental illness (SMI), and current intervention approaches have limited effectiveness. This paper presents a blended intervention approach, called mobile Social Interact…
View article: Rare Life Event Detection via Mobile Sensing Using Multi-Task Learning
Rare Life Event Detection via Mobile Sensing Using Multi-Task Learning Open
Rare life events significantly impact mental health, and their detection in behavioral studies is a crucial step towards health-based interventions. We envision that mobile sensing data can be used to detect these anomalies. However, the h…
View article: Semantic signals in self-reference: The detection and prediction of depressive symptoms from the daily diary entries of a sample with major depressive disorder
Semantic signals in self-reference: The detection and prediction of depressive symptoms from the daily diary entries of a sample with major depressive disorder Open
Individuals with major depressive disorder (MDD) experience fewer positive and more negative emotions and use fewer positive words to describe themselves. Natural language processing (NLP) techniques have been used to predict depression, w…
View article: Depressive Symptoms as a Heterogeneous and Constantly Evolving Dynamical System: Idiographic Depressive Symptom Networks of Rapid Symptom Changes among Persons with Major Depressive Disorder
Depressive Symptoms as a Heterogeneous and Constantly Evolving Dynamical System: Idiographic Depressive Symptom Networks of Rapid Symptom Changes among Persons with Major Depressive Disorder Open
Major depressive disorder (MDD) is the leading cause of global disease burden. Diagnostically, major depressive episodes are conceptualized as a series of individual symptoms occurring most of the day for at least two weeks. Despite this o…
View article: First-Gen Lens
First-Gen Lens Open
The transition from high school to college is a taxing time for young adults. New students arriving on campus navigate a myriad of challenges centered around adapting to new living situations, financial needs, academic pressures and social…
View article: Accurate Step Count with Generalized and Personalized Deep Learning on Accelerometer Data
Accurate Step Count with Generalized and Personalized Deep Learning on Accelerometer Data Open
Physical activity (PA) is globally recognized as a pillar of general health. Step count, as one measure of PA, is a well known predictor of long-term morbidity and mortality. Despite its popularity in consumer devices, a lack of methodolog…
View article: Machine Learning Enabled Non-invasive Diagnosis of Nonalcoholic Fatty Liver Disease and Assessment of Abdominal Fat from MRI Data
Machine Learning Enabled Non-invasive Diagnosis of Nonalcoholic Fatty Liver Disease and Assessment of Abdominal Fat from MRI Data Open
Nonalcoholic fatty liver disease (NAFLD) is the most rapidly growing contributor to chronic liver disease worldwide with high disease burden and suffers from limitations in diagnosis. Inspired by recent advances in machine learning digital…
View article: Low Incidence of COVID-19 Infection in Patients with Acute Myeloid Leukemia Undergoing Reduced Intensity/Venetoclax Based Treatment: Initial Results of the PACE Prospective Clinical Study from the UK Trials Acceleration Program
Low Incidence of COVID-19 Infection in Patients with Acute Myeloid Leukemia Undergoing Reduced Intensity/Venetoclax Based Treatment: Initial Results of the PACE Prospective Clinical Study from the UK Trials Acceleration Program Open
The impact of Coronavirus disease 2019 (COVID-19) on outcomes in patients with cancer remains unclear. Acute Myeloid Leukemia (AML)/high-risk myelodysplasia (MDS) are common hematological malignancies resulting in profound immunosuppressio…
View article: Effective expression analysis using gene interaction matrices and convolutional neural networks
Effective expression analysis using gene interaction matrices and convolutional neural networks Open
Artificial intelligence recently experienced a renaissance with the advancement of convolutional neural networks (CNNs). CNNs require spatially meaningful matrices ( e.g ., image data) with recurring patterns, limiting its applicability to…
View article: Personalized Step Counting Using Wearable Sensors: A Domain Adapted LSTM\n Network Approach
Personalized Step Counting Using Wearable Sensors: A Domain Adapted LSTM\n Network Approach Open
Activity monitors are widely used to measure various physical activities (PA)\nas an indicator of mobility, fitness and general health. Similarly, real-time\nmonitoring of longitudinal trends in step count has significant clinical\npotenti…
View article: Personalized Step Counting Using Wearable Sensors: A Domain Adapted LSTM Network Approach
Personalized Step Counting Using Wearable Sensors: A Domain Adapted LSTM Network Approach Open
Activity monitors are widely used to measure various physical activities (PA) as an indicator of mobility, fitness and general health. Similarly, real-time monitoring of longitudinal trends in step count has significant clinical potential …