A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
HealthSyn generates synthetic user behavior data for health interventions.
Develops a personalized reinforcement learning algorithm for dyadic health interventions.
Mobile apps and machine learning improve malaria prevention and treatment.
Develops algorithms to balance personalization and statistical power in mobile health studies.
AI improves precision health through adaptive interventions.
A recommendation framework helps users choose healthcare interventions.
RoME optimizes mobile health interventions by modeling user and time-specific effects.
Due to the recent advancements in wearables and sensing technology, health scientists are increasingly developing mobile health (mHealth) interventions. In mHealth interventions, mobile devices are used to deliver treatment to individuals as they go about their daily lives. These treatments are generally designed to im…
Unified RL survey for healthcare AI interventions.
Study user engagement in mobile health apps for health workers in resource-poor settings.
Meta-learning method for estimating time-varying mHealth intervention effects.
Increasing technological sophistication and widespread use of smartphones and wearable devices provide opportunities for innovative and highly personalized health interventions. A Just-In-Time Adaptive Intervention (JITAI) uses real-time data collection and communication capabilities of modern mobile devices to deliver…
Bayesian ATM improves stability and efficiency in mobile health interventions.
BFTS uses Bayesian Additive Regression Trees for improved personalized mobile health interventions.
Bayesian networks learn sub-population differences from data.
Italy and the Eurozone are heading in the year 2012 into a financial depression of unprecedented magnitude, with a forthcoming multitude of often contradictory public economic and financial stability emergency interventions whose ultimate endogenous and exogenous effects on public and private health spending and on the…
The opioid epidemic in the United States claims over 40,000 lives per year, and it is estimated that well over two million Americans have an opioid use disorder. Over-prescription and misuse of prescription opioids play an important role in the epidemic. Individuals who are prescribed opioids, and who are diagnosed wit…
GAMBITTS uses GenAI for adaptive interventions, improving decision-making.
Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network t…
Holdout set improves risk score accuracy without biasing predictions.
Although there are millions of transgender people in the world, a lack of information exists about their health issues. This issue has consequences for the medical field, which only has a nascent understanding of how to identify and meet this population's health-related needs. Social media sites like Twitter provide ne…
Agent decides when to measure latent states in RL to improve efficiency.
New bandit model for healthcare intervention planning.
The field of mobile health aims to leverage recent advances in wearable on-body sensing technology and smart phone computing capabilities to develop systems that can monitor health states and deliver just-in-time adaptive interventions. However, existing work has largely focused on analyzing collected data in the off-l…
Simulation-based inference aids in predicting disease dynamics for health policy.
New method improves mHealth user engagement using Thompson sampling for count data.
The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best healthcare possible for each patient. Mobile technologies have an important role to play in this vision as they offer a means to monitor a patient's health status in real-time an…
Deep learning predicts opioid use disorder risk in patients.
Deep learning using neural networks has provided advances in image style transfer, merging the content of one image (e.g., a photo) with the style of another (e.g., a painting). Our research shows this concept can be extended to analyse the design of streetscapes in relation to health and wellbeing outcomes. An Austral…
The aim of clinical effectiveness research using repositories of electronic health records is to identify what health interventions 'work best' in real-world settings. Since there are several reasons why the net benefit of intervention may differ across patients, current comparative effectiveness literature focuses on …
New approach tackles non-Markovian behavior in maternal health programs.
Extends Thompson sampling for RL with fewer episodes.
Review of methods enabling causal predictions under hypothetical interventions.
Study shows diverse data types improve SARS-COV-2 case surge predictions.
The emergence of an ageing population is a significant public health concern. This has led to an increase in the number of people living with progressive neurodegenerative disorders like dementia. Consequently, the strain this is places on health and social care services means providing 24-hour monitoring is not sustai…
Proposes DRIG for robust predictions using noise interventions.
In mobile health (mHealth), reinforcement learning algorithms that adapt to one's context without learning personalized policies might fail to distinguish between the needs of individuals. Yet the high amount of noise due to the in situ delivery of mHealth interventions can cripple the ability of an algorithm to learn …
Generative model predicts vehicle faults up to 1000 hours in advance.
Diabetes is a major public health problem in the United States, affecting roughly 30 million people. Diabetes complications, along with the mental health comorbidities that often co-occur with them, are major drivers of high healthcare costs, poor outcomes, and reduced treatment adherence in diabetes. Here, we evaluate…
Framework identifies comorbidities for frequent ED and inpatient visits.
Unified framework for estimating indirect effects in observational studies with unmeasured confounding.
An app-based mHealth intervention uses reinforcement learning to send effective reminder notifications.
In many mobile health interventions, treatments should only be delivered in a particular context, for example when a user is currently stressed, walking or sedentary. Even in an optimal context, concerns about user burden can restrict which treatments are sent. To diffuse the treatment delivery over times when a user i…
There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals' mood and wellbei…
Proposes pT-Learning for optimal dynamic treatment regimes in mHealth.
It is not clear how to target patients who are most likely to benefit from digital care management programs ex-ante, a shortcoming of current risk score based approaches. This study focuses on defining impactability by identifying those patients most likely to benefit from technology enabled care management, delivered …
Adapts causal inference for high-dimensional treatments like text strings.
Deep learning models have exhibited superior performance in predictive tasks with the explosively increasing Electronic Health Records (EHR). However, due to the lack of transparency, behaviors of deep learning models are difficult to interpret. Without trustworthiness, deep learning models will not be able to assist i…