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.
Study assesses health plan risk measures for Solvency Capital Requirement.
Framework improves health by planning actionable treatment processes.
New AI model optimizes personalized care for elderly residents.
The distribution of health care payments to insurance plans has substantial consequences for social policy. Risk adjustment formulas predict spending in health insurance markets in order to provide fair benefits and health care coverage for all enrollees, regardless of their health status. Unfortunately, current risk a…
Machine learning models outperform traditional actuarial methods in predicting health insurance costs.
Managing patients with chronic diseases is a major and growing healthcare challenge in several countries. A chronic condition, such as diabetes, is an illness that lasts a long time and does not go away, and often leads to the patient's health gradually getting worse. While recent works involve raw electronic health re…
Risk adjustment has become an increasingly important tool in healthcare. It has been extensively applied to payment adjustment for health plans to reflect the expected cost of providing coverage for members. Risk adjustment models are typically estimated using linear regression, which does not fully exploit the informa…
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…
Estimates causal effect of managed care plans on NYC Medicaid spending.
One of the main issues affecting the Italian NHS is the healthcare deficit: according to current agreements between the Italian State and its Regions, public funding of regional NHS is now limited to the amount of regional deficit and is subject to previous assessment of strict adherence to constraint on regional healt…
This study compares community detection algorithms for delineating health service areas.
VisitHGNN predicts visit probabilities between neighborhoods and POIs using graph neural networks.
In this paper we introduce score embedding, a neural network based model to learn interpretable vector representations for words. Score embedding is a supervised method that takes advantage of the labeled training data and the neural network architecture to learn interpretable representations for words. Health care has…
In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (…
EHR-MPC optimizes sepsis treatment using digital twins and inference-time control.
Model predicts wound and episode-level readmission risk and time to re-admit.
Social media provide a platform for users to express their opinions and share information. Understanding public health opinions on social media, such as Twitter, offers a unique approach to characterizing common health issues such as diabetes, diet, exercise, and obesity (DDEO), however, collecting and analyzing a larg…
In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven health monitoring systems is gaining in popularity due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as Deep Learning. The main purpose of this pa…
Deep Claim predicts payer responses from claims data using deep learning.
New bandit model for healthcare intervention planning.
Ability for accurate hospital case cost modelling and prediction is critical for efficient health care financial management and budgetary planning. A variety of regression machine learning algorithms are known to be effective for health care cost predictions. The purpose of this experiment was to build an Azure Machine…
Early detection of Alzheimer's disease (AD) and identification of potential risk/beneficial factors are important for planning and administering timely interventions or preventive measures. In this paper, we learn a disease model for AD that combines genotypic and phenotypic profiles, and cognitive health metrics of pa…
Study uses machine learning to analyze Twitter sentiments about COVID-19.
Maps of infectious disease---charting spatial variations in the force of infection, degree of endemicity, and the burden on human health---provide an essential evidence base to support planning towards global health targets. Contemporary disease mapping efforts have embraced statistical modelling approaches to properly…
Holdout set improves risk score accuracy without biasing predictions.
Develops a two-stage conformal prediction method for Parkinson's disease medication needs.
Study optimizes inspection and monitoring of deteriorating structures using POMDPs.
New model outperforms traditional disease models in forecasting COVID-19.
Antimicrobial resistance is an important public health concern that has implications in the practice of medicine worldwide. Accurately predicting resistance phenotypes from genome sequences shows great promise in promoting better use of antimicrobial agents, by determining which antibiotics are likely to be effective i…
Machine learning (ML), artificial intelligence (AI) and other modern statistical methods are providing new opportunities to operationalize previously untapped and rapidly growing sources of data for patient benefit. Whilst there is a lot of promising research currently being undertaken, the literature as a whole lacks:…
The study uses stock market indicators to forecast COVID-19 cases.
AI enhances personalized drug development and decision-making in pharma.
Repository tackles fake health news in cancer research.
Study finds macroeconomic indicators predict health workforce and infrastructure measures.
New method designs fairer transport plans with uncertainty.
Improves RL planning by proposing sub-goals hierarchically.
Study uses machine learning to predict future health from various health data types.
Study user engagement in mobile health apps for health workers in resource-poor settings.
Aims to integrate AI and modelling for patient health forecasting.
New algorithm forecasts health indicators for better equipment lifespan prediction.
LHIEM model predicts health, income, and employment over years.
We aim to reduce the burden of programming and deploying autonomous systems to work in concert with people in time-critical domains, such as military field operations and disaster response. Deployment plans for these operations are frequently negotiated on-the-fly by teams of human planners. A human operator then trans…
Automatically assesses the quality of online health articles.
Study integrates reliability constraints into generation planning models.
One primary task of population health analysis is the identification of risk factors that, for some subpopulation, have a significant association with some health condition. Examples include finding lifestyle factors associated with chronic diseases and finding genetic mutations associated with diseases in precision he…
Mobile apps and machine learning improve malaria prevention and treatment.
New approach improves black-box planning efficiency by discovering focused macros.
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…