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.
Supervised learning improves disease outbreak detection accuracy.
Mobile apps and machine learning improve malaria prevention and treatment.
Accurate real-time monitoring systems of influenza outbreaks help public health officials make informed decisions that may help save lives. We show that information extracted from cloud-based electronic health records databases, in combination with machine learning techniques and historical epidemiological information,…
Framework extracts symptoms from EHRs for rapid disease outbreak detection.
Improved bio-surveillance through automated document classification.
Flusion combines multiple data sources to improve flu forecasts.
Study shows diverse data types improve SARS-COV-2 case surge predictions.
Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this "Bibliome", the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that ha…
New method tackles anomaly detection in video surveillance using continual learning.
Study finds macroeconomic indicators predict health workforce and infrastructure measures.
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…
Domestic Violence (DV) is considered as big social issue and there exists a strong relationship between DV and health impacts of the public. Existing research studies have focused on social media to track and analyse real world events like emerging trends, natural disasters, user sentiment analysis, political opinions,…
In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these new big data streams. At the same time, many challenging proble…
Identification of the influential clinical symptoms and laboratory features that help in the diagnosis of dengue fever in early phase of the illness would aid in designing effective public health management and virological surveillance strategies. Keeping this as our main objective we develop in this paper, a new compu…
New model maps malaria prevalence across Kenya's changing administrative boundaries.
Social media based digital epidemiology has the potential to support faster response and deeper understanding of public health related threats. This study proposes a new framework to analyze unstructured health related textual data via Twitter users' post (tweets) to characterize the negative health sentiments and non-…
Study analyzes misinformation on social media during COVID-19.
CASTNet forecasts opioid overdoses using crime patterns.
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…
Exploiting the large amount of available data for addressing relevant social problems has been one of the key challenges in data mining. Such efforts have been recently named "data science for social good" and attracted the attention of several researchers and institutions. We give a contribution in this objective in t…
Simulation-based inference aids in predicting disease dynamics for health policy.
Study uses machine learning to analyze Twitter sentiments about COVID-19.
We present three case studies of organizations using a data science competition to answer a pressing question. The first is in education where a nonprofit that creates smart school budgets wanted to automatically tag budget line items. The second is in public health, where a low-cost, nonprofit women's health care prov…
Study uses CNNs to estimate BMI from photos.
TMLE improves unbiased estimation in public health studies.
Paper introduces score embedding for Twitter sentiment analysis of health care issues.
Method fuses low and high-resolution data for better health estimates.
The global financial crisis, beginning in 2008, took an historic toll on national economies around the world. Following equity market crashes, unemployment rates rose significantly in many countries: Italy was among those. What will be the impact of such large shocks on Italian healthcare finances? An empirical model f…
Repository tackles fake health news in cancer research.
Hidden Markov Models classify cough events with high accuracy.
Paper proposes a new model for fine-grained review classification using weak supervision.
This review explores methods for detecting Adverse Drug Events in EHRs.
This study seeks to validate a search protocol of ill health-related terms using Twitter data which can later be used to understand if, and how, Twitter can reveal information on the current health situation. We extracted conversations related to health and disease postings on Twitter using a set of pre-defined keyword…
Graph-based framework predicts ADR signals from clinical data.
Chagas disease is a neglected disease, and information about its geographical spread is very scarse. We analyze here mobility and calling patterns in order to identify potential risk zones for the disease, by using public health information and mobile phone records. Geolocalized call records are rich in social and mobi…
Robust model detects outliers in spatiotemporal epidemic data.
Deep learning improves skin cancer detection.
System predicts HIV patients at risk of dropping out of care.
A lack of information exists about the health issues of lesbian, gay, bisexual, transgender, and queer (LGBTQ) people who are often excluded from national demographic assessments, health studies, and clinical trials. As a result, medical experts and researchers lack a holistic understanding of the health disparities fa…
Syndromic surveillance detects and monitors individual and population health indicators through sources such as emergency department records. Automated classification of these records can improve outbreak detection speed and diagnosis accuracy. Current syndromic systems rely on hand-coded keyword-based methods to parse…
The paper tackles robust classification trees for distribution shifts, improving accuracy in public health and social work.
Study finds billing codes at IPO boost digital health companies' financial performance.
The CHAMPION study clusters multi-dimensional accelerometer data to understand health links.
We consider a binary unsupervised classification problem where each observation is associated with an unobserved label that we want to retrieve. More precisely, we assume that there are two groups of observation: normal and abnormal. The `normal' observations are coming from a known distribution whereas the distributio…
Study reveals hidden infections and infection dynamics from early data.
Satellite images predict U.S. county mortality rates.
The occurrence of drug-drug-interactions (DDI) from multiple drug dispensations is a serious problem, both for individuals and health-care systems, since patients with complications due to DDI are likely to reenter the system at a costlier level. We present a large-scale longitudinal study (18 months) of the DDI phenom…
Study historical cholera epidemics and simulate long-term mortality impacts.