10 research outputs found
Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications
Understanding public discourse on emergency use of unproven therapeutics is
crucial for monitoring safe use and combating misinformation. We developed a
natural language processing-based pipeline to comprehend public perceptions of
and stances on coronavirus disease 2019 (COVID-19)-related drugs on Twitter
over time. This retrospective study included 609,189 US-based tweets from
January 29, 2020, to November 30, 2021, about four drugs that garnered
significant public attention during the COVID-19 pandemic: (1)
Hydroxychloroquine and Ivermectin, therapies with anecdotal evidence; and (2)
Molnupiravir and Remdesivir, FDA-approved treatments for eligible patients.
Time-trend analysis was employed to understand popularity trends and related
events. Content and demographic analyses were conducted to explore potential
rationales behind people's stances on each drug. Time-trend analysis indicated
that Hydroxychloroquine and Ivermectin were discussed more than Molnupiravir
and Remdesivir, particularly during COVID-19 surges. Hydroxychloroquine and
Ivermectin discussions were highly politicized, related to conspiracy theories,
hearsay, and celebrity influences. The distribution of stances between the two
major US political parties was significantly different (P < .001); Republicans
were more likely to support Hydroxychloroquine (55%) and Ivermectin (30%) than
Democrats. People with healthcare backgrounds tended to oppose
Hydroxychloroquine (7%) more than the general population, while the general
population was more likely to support Ivermectin (14%). Our study found that
social media users have varying perceptions and stances on off-label versus
FDA-authorized drug use at different stages of COVID-19. This indicates that
health systems, regulatory agencies, and policymakers should design tailored
strategies to monitor and reduce misinformation to promote safe drug use.Comment: Full paper published in JAMI
Streamlining Social Media Information Retrieval for Public Health Research with Deep Learning
The utilization of social media in epidemic surveillance has been well
established. Nonetheless, bias is often introduced when pre-defined lexicons
are used to retrieve relevant corpus. This study introduces a framework aimed
at curating extensive dictionaries of medical colloquialisms and Unified
Medical Language System (UMLS) concepts. The framework comprises three modules:
a BERT-based Named Entity Recognition (NER) model that identifies medical
entities from social media content, a deep-learning powered normalization
module that standardizes the extracted entities, and a semi-supervised
clustering module that assigns the most probable UMLS concept to each
standardized entity. We applied this framework to COVID-19-related tweets from
February 1, 2020, to April 30, 2022, generating a symptom dictionary (available
at https://github.com/ningkko/UMLS_colloquialism/) composed of 9,249
standardized entities mapped to 876 UMLS concepts and 38,175 colloquial
expressions. This framework demonstrates encouraging potential in addressing
the constraints of keyword matching information retrieval in social media-based
public health research.Comment: Accepted to ICHI 2023 (The 11th IEEE International Conference on
Healthcare Informatics) as a poster presentatio
Application of artificial intelligence in ophthalmic plastic surgery
The advancement of computers and data explosion have ushered in the third wave of artificial intelligence(AI). AI is an interdisciplinary field that encompasses new ideas, new theories, and new technologies, etc. AI has brought convenience to ophthalmology application and promoted its intelligent, precise, and minimally invasive development. At present, AI has been widely applied in various fields of ophthalmology, especially in oculoplastic surgery. AI has made rapid progress in image detection, facial recognition, etc., and its performance and accuracy have even surpassed humans in some aspects. This article reviews the relevant research and applications of AI in oculoplastic surgery, including ptosis, single eyelid, pouch, eyelid mass, and exophthalmos, and discusses the challenges and opportunities faced by AI in oculoplastic surgery, and provides prospects for its future development, aiming to provide new ideas for the development of AI in oculoplastic surgery
African bushpigs exhibit porous species boundaries and appeared in Madagascar concurrently with human arrival
Several African mammals exhibit a phylogeographic pattern where closely related taxa are split between West/Central and East/Southern Africa, but their evolutionary relationships and histories remain controversial. Bushpigs (Potamochoerus larvatus) and red river hogs (P. porcus) are recognised as separate species due to morphological distinctions, a perceived lack of interbreeding at contact, and putatively old divergence times, but historically, they were considered conspecific. Moreover, the presence of Malagasy bushpigs as the sole large terrestrial mammal shared with the African mainland raises intriguing questions about its origin and arrival in Madagascar. Analyses of 67 whole genomes revealed a genetic continuum between the two species, with putative signatures of historical gene flow, variable FST values, and a recent divergence time (<500,000 years). Thus, our study challenges key arguments for splitting Potamochoerus into two species and suggests their speciation might be incomplete. Our findings also indicate that Malagasy bushpigs diverged from southern African populations and underwent a limited bottleneck 1000-5000 years ago, concurrent with human arrival in Madagascar. These results shed light on the evolutionary history of an iconic and widespread African mammal and provide insight into the longstanding biogeographic puzzle surrounding the bushpig's presence in Madagascar
Research on Technological Innovation as Seen through the Chinese Looking Glass
The rapid development of the Chinese economy during the 1990's has intensified research on technological innovation. Recent policy emphasis on innovation as a path to sustainable economic growth will only accelerate work on this important topic. The work by individuals and groups at various research institutes and universities has mainly been published in leading Chinese scholarly journals. In this paper, we develop a framework and do a substantive review of the literature to characterize the state of knowledge about technological innovation in China, with special emphasis on: 1) conceptual contributions, 2) empirical results, and 3) connections between innovation and performance