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.
New algorithm forecasts health indicators for better equipment lifespan prediction.
Study finds macroeconomic indicators predict health workforce and infrastructure measures.
AdaCare learns health status from biomarkers across multiple time scales.
Study shows racial bias in health data, which can be reduced with simple techniques.
Method fuses low and high-resolution data for better health estimates.
Imaging fluorescent disease biomarkers in tissues and skin is a non-invasive method to screen for health conditions. We report an automated process that combines intraoral fluorescent porphyrin biomarker imaging, clinical examinations and machine learning for correlation of systemic health conditions with periodontal d…
Research predicts healthcare index movements using historical OHLC data.
Generative models create personalized patient health simulations.
Online health communities such as the online breast cancer forum enable patients (i.e., users) to interact and help each other within various subforums, which are subsections of the main forum devoted to specific health topics. The changing nature of the users' activities in different subforums can be strong indicators…
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…
Study finds billing codes at IPO boost digital health companies' financial performance.
Binary PheNorm extends phenotype labeling for EHRs using binary silver labels.
The paper reviews methods for estimating individual treatment effects using non-parametric regression models.
Respiratory infections and chronic respiratory diseases impose a heavy health burden worldwide. Coughing is one of the most common symptoms of many such infections, and can be indicative of flare-ups of chronic respiratory diseases. Whether at a clinical or public health level, the capacity to identify bouts of coughin…
Framework extracts symptoms from EHRs for rapid disease outbreak detection.
Risk-based active learning improves SHM decision-making.
CovidCare uses EMR data to predict patient outcomes in emerging epidemics.
Study analyzes misinformation on social media during COVID-19.
Feature selection, which searches for the most representative features in observed data, is critical for health data analysis. Unlike feature extraction, such as PCA and autoencoder based methods, feature selection preserves interpretability, meaning that the selected features provide direct information about certain h…
Paper proposes sharing models instead of data for smart health predictions.
More than two thirds of mental health problems have their onset during childhood or adolescence. Identifying children at risk for mental illness later in life and predicting the type of illness is not easy. We set out to develop a platform to define subtypes of childhood social-emotional development using longitudinal,…
Gaussian Process Regression improves damage assessment in structural health monitoring.
Neural network predicts cardiovascular events from EHRs with high accuracy.
Using particle system methodologies we study the propagation of financial distress in a network of firms facing credit risk. We investigate the phenomenon of a credit crisis and quantify the losses that a bank may suffer in a large credit portfolio. Applying a large deviation principle we compute the limiting distribut…
The study uses stock market indicators to forecast COVID-19 cases.
Deep learning improves skin cancer detection.
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…
VBphenoR uses variational Bayes for EHR-based patient phenotyping.
Computational Drug Repositioning (CDR) is the task of discovering potential new indications for existing drugs by mining large-scale heterogeneous drug-related data sources. Leveraging the patient-level temporal ordering information between numeric physiological measurements and various drug prescriptions provided in E…
Study uses machine learning to predict stroke risk in China with improved accuracy.
This paper considers the problem of estimating an unknown high dimensional signal from noisy linear measurements, {when} the signal is assumed to possess a \emph{group-sparse} structure in a {known,} fixed dictionary. We consider signals generated according to a natural probabilistic model, and establish new conditions…
Recently it's been shown that neural networks can use images of human faces to accurately predict Body Mass Index (BMI), a widely used health indicator. In this paper we demonstrate that a neural network performing BMI inference is indeed vulnerable to test-time adversarial attacks. This extends test-time adversarial a…
Paper proposes ARPHMM for fault detection and prognosis in aero-engines.
Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data - the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and gen…
Prediction of disease onset from patient survey and lifestyle data is quickly becoming an important tool for diagnosing a disease before it progresses. In this study, data from the National Health and Nutrition Examination Survey (NHANES) questionnaire is used to predict the onset of type II diabetes. An ensemble model…
Study evaluates how changes in mobility affect COVID-19 case rates.
Novel framework detects CKD in diabetic patients using sparse EHR representations.
Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identification of the source of the problem that produced the anomaly is also essential. This is particularly the case in aircraft engine health monitoring where detecting early signs of failure (anomalies) and helping the engine …
MIM adds indicator variables to improve model performance on incomplete data.
New methods reduce bias in machine learning predictions for causal inference without extra data.
Companies do not operate in a vacuum. As companies move towards an increasingly specialized production function and their reach is becoming truly global, their aptitude in managing and shaping their inter-organizational network is a determining factor in measuring their health. Current models of company financial healt…
Repository tackles fake health news in cancer research.
Prototypical examples that best summarizes and compactly represents an underlying complex data distribution communicate meaningful insights to humans in domains where simple explanations are hard to extract. In this paper we present algorithms with strong theoretical guarantees to mine these data sets and select protot…
Study uses machine learning to predict future health from various health data types.
Dynamic treatment recommendation systems based on large-scale electronic health records (EHRs) become a key to successfully improve practical clinical outcomes. Prior relevant studies recommend treatments either use supervised learning (e.g. matching the indicator signal which denotes doctor prescriptions), or reinforc…
Study user engagement in mobile health apps for health workers in resource-poor settings.
Survey of EEG market and machine learning applications.
LHIEM model predicts health, income, and employment over years.