3,328 research outputs found
Near-real-time Earthquake-induced Fatality Estimation using Crowdsourced Data and Large-Language Models
When a damaging earthquake occurs, immediate information about casualties is
critical for time-sensitive decision-making by emergency response and aid
agencies in the first hours and days. Systems such as Prompt Assessment of
Global Earthquakes for Response (PAGER) by the U.S. Geological Survey (USGS)
were developed to provide a forecast within about 30 minutes of any significant
earthquake globally. Traditional systems for estimating human loss in disasters
often depend on manually collected early casualty reports from global media, a
process that's labor-intensive and slow with notable time delays. Recently,
some systems have employed keyword matching and topic modeling to extract
relevant information from social media. However, these methods struggle with
the complex semantics in multilingual texts and the challenge of interpreting
ever-changing, often conflicting reports of death and injury numbers from
various unverified sources on social media platforms. In this work, we
introduce an end-to-end framework to significantly improve the timeliness and
accuracy of global earthquake-induced human loss forecasting using
multi-lingual, crowdsourced social media. Our framework integrates (1) a
hierarchical casualty extraction model built upon large language models, prompt
design, and few-shot learning to retrieve quantitative human loss claims from
social media, (2) a physical constraint-aware, dynamic-truth discovery model
that discovers the truthful human loss from massive noisy and potentially
conflicting human loss claims, and (3) a Bayesian updating loss projection
model that dynamically updates the final loss estimation using discovered
truths. We test the framework in real-time on a series of global earthquake
events in 2021 and 2022 and show that our framework streamlines casualty data
retrieval, achieving speed and accuracy comparable to manual methods by USGS.Comment: 10 pages, 8 figure
Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media
With the rise of social media, millions of people are routinely expressing
their moods, feelings, and daily struggles with mental health issues on social
media platforms like Twitter. Unlike traditional observational cohort studies
conducted through questionnaires and self-reported surveys, we explore the
reliable detection of clinical depression from tweets obtained unobtrusively.
Based on the analysis of tweets crawled from users with self-reported
depressive symptoms in their Twitter profiles, we demonstrate the potential for
detecting clinical depression symptoms which emulate the PHQ-9 questionnaire
clinicians use today. Our study uses a semi-supervised statistical model to
evaluate how the duration of these symptoms and their expression on Twitter (in
terms of word usage patterns and topical preferences) align with the medical
findings reported via the PHQ-9. Our proactive and automatic screening tool is
able to identify clinical depressive symptoms with an accuracy of 68% and
precision of 72%.Comment: 8 pages, Advances in Social Networks Analysis and Mining (ASONAM),
2017 IEEE/ACM International Conferenc
A Generalizable Deep Learning System for Cardiac MRI
Cardiac MRI allows for a comprehensive assessment of myocardial structure,
function, and tissue characteristics. Here we describe a foundational vision
system for cardiac MRI, capable of representing the breadth of human
cardiovascular disease and health. Our deep learning model is trained via
self-supervised contrastive learning, by which visual concepts in cine-sequence
cardiac MRI scans are learned from the raw text of the accompanying radiology
reports. We train and evaluate our model on data from four large academic
clinical institutions in the United States. We additionally showcase the
performance of our models on the UK BioBank, and two additional publicly
available external datasets. We explore emergent zero-shot capabilities of our
system, and demonstrate remarkable performance across a range of tasks;
including the problem of left ventricular ejection fraction regression, and the
diagnosis of 35 different conditions such as cardiac amyloidosis and
hypertrophic cardiomyopathy. We show that our deep learning system is capable
of not only understanding the staggering complexity of human cardiovascular
disease, but can be directed towards clinical problems of interest yielding
impressive, clinical grade diagnostic accuracy with a fraction of the training
data typically required for such tasks.Comment: 21 page main manuscript, 4 figures. Supplementary Appendix and code
will be made available on publicatio
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