Text mining MEDLINE to support public health

Today’s society is data rich and information driven, with access to numerous data sources available that have the potential to provide new insights into areas such as disease prevention, personalised medicine and data driven policy decisions. This paper describes and demonstrates the use of text mining tools developed to support public health institutions to complement their data with other accessible open data sources, optimize analysis and gain insight when examining policy. In particular we focus on the exploration of MEDLINE, the biggest structured open dataset of biomedical knowledge. In MEDLINE we utilize its terminology for indexing and cataloguing biomedical information – MeSH – to maximize the efficacy of the dataset

NewsMeSH: a new classifier designed to annotate health news with MeSH headings

Motivation In the age of big data, the amount of scientific information available online dwarfs the ability of current tools to support researchers in locating and securing access to the necessary materials. Well-structured open data and the smart systems that make the appropriate use of it are invaluable and can help health researchers and professionals to find the appropriate information by, e.g., configuring the monitoring of information or refining a specific query on a disease. Methods We present an automated text classifier approach based on the MEDLINE/MeSH thesaurus, trained on the manual annotation of more than 26 million expert-annotated scientific abstracts. The classifier was developed tailor-fit to the public health and health research domain experts, in the light of their specific challenges and needs. We have applied the proposed methodology on three specific health domains: the Coronavirus, Mental Health and Diabetes, considering the pertinence of the first, and the known relations with the other two health topics. Results A classifier is trained on the MEDLINE dataset that can automatically annotate text, such as scientific articles, news articles or medical reports with relevant concepts from the MeSH thesaurus. Conclusions The proposed text classifier shows promising results in the evaluation of health-related news. The application of the developed classifier enables the exploration of news and extraction of health-related insights, based on the MeSH thesaurus, through a similar workflow as in the usage of PubMed, with which most health researchers are familiar

Meaningful Big Data Integration For a Global COVID-19 Strategy

Abstract With the rapid spread of the COVID-19 pandemic, the novel Meaningful Integration of Data Analytics and Services (MIDAS) platform quickly demonstrates its value, relevance and transferability to this new global crisis. The MIDAS platform enables the connection of a large number of isolated heterogeneous data sources, and combines rich datasets including open and social data, ingesting and preparing these for the application of analytics, monitoring and research tools. These platforms will assist public health author ities in: (i) better understanding the disease and its impact; (ii) monitoring the different aspects of the evolution of the pandemic across a diverse range of groups; (iii) contributing to improved resilience against the impacts of this global crisis; and (iv) enhancing preparedness for future public health emergencies. The model of governance and ethical review, incorporated and defined within MIDAS, also addresses the complex privacy and ethical issues that the developing pandemic has highlighted, allowing oversight and scrutiny of more and richer data sources by users of the system

MedISys: A Multilingual Media Monitoring Tool for Medical Intelligence and Early Warning

Most Western countries have an institution with the task of detecting and monitoring potential threats to Public Health (PH) in their countries, i.e. chemical, biological, radiological and nuclear (CBRN) threats, which can be natural (diseases), deliberate (terrorism) or accidental. One of the daily duties of these PH bodies includes monitoring the local, national and international media for reports on disease outbreaks, on the disappearance or the release of dangerous substances, etc. This typically involves identifying and analysing relevant news articles in up to half a million news reports per day in various languages. In order to facilitate this task, the European Commission¿s Joint Research Centre (JRC) in collaboration with the EC¿s Directorate General for Health and Consumer Protection (DG SANCO) has developed the fully-automatic Medical Intelligence System MedISys, which takes away many of the routine tasks of this process and detects early warning signals that can be used as a starting point for the daily media review. MedISys gathers news reports in 42 languages from about 1,500 web portals and twenty commercial news providers, filters documents of potential interest to PH officials, categorises them, monitors trends and alerts users of an unexpected increase of articles in any of the fine-grained categories, separately for each country of the world. MedISys allows to customise the view of reports to specific languages, subjects and news sources. It furthermore allows moderators to select, group and disseminate the information to further user groups, via email, SMS, web pages and PDF reports. A challenge faced by all PH organisations is the fact that a lot of relevant information is only available in foreign languages and that employing experts in all the languages is expensive and difficult. By aggregating the information found in many different news articles in all the available languages, MedISys can automatically issue warnings to users (through graphs, email alerts, etc.) as soon as relevant information appears in any of the languages covered, and often before the information is published in the user¿s own language. MedISys is actively used by international organisations such as EC¿s DG SANCO, the European Centre for Disease Prevention and Control ECDC and the World Health Organisation WHO, by many PH bodies inside the European Union (e.g. the French Institut de Veille Sanitaire and the Spanish Instituto de Salud Carlos III), as well as by various bodies outside the EU (e.g. the US-American CDC and the Canadian Global Public Health Intelligence Network GPHIN). A public web site with restricted functionality, available at http://medusa.jrc.it/, is visited by an average of 1,000 distinct users per day. The speaker will give an overview of the challenges faced by Public Health bodies, describe the main functionality of MedISys including the features that distinguish it from other media monitoring systems, and talk about the ongoing collaboration in the G8¿s Global Health Security Action Group GHSAG with the purpose of further increasing the usefulness of the system.JRC.G.2-Support to external securit

Text mining open datasets to support public health

Today’s society is data rich and information driven, with access to numerous data sources available that have the potential to provide new insights into areas such as disease prevention, personalised medicine and data driven policy decisions. This paper describes and demonstrates the use of text mining tools developed to support public health institutions to complement their data with other accessible open data sources, optimize analysis and gain insight when examining policy. In particular we focus on the exploration of MEDLINE, the biggest structured open dataset of biomedical knowledge. In MEDLINE we utilize its terminology for indexing and cataloguing biomedical information – MeSH – to maximize the efficacy of the dataset

Mining MEDLINE for the visualisation of a global perspective on biomedical knowledge

There is an ever increasing number of data sources that potentially could be used to gain new insights into areas such as disease prevention, policy formulation/evaluation and personalised medicine, but these are not optimised for use within an analytics type user interface. The MIDAS project was funded under a call for ‘Big Data supporting Public Health policies’ to develop a big data platform that facilitates the utilisation of healthcare data beyond existing isolated systems, making that data amenable to enrichment with open and social data. This aligns closely with a number of themes in Knowledge Discovery in Databases (KDD) in that the platform enables the integration of heterogeneous data sources, providing privacy-preserving analytics, forecasting tools and visualisation modules to deliver actionable information. Policy makers as a result will have the capability to perform data-driven evaluations of the efficiency and effectiveness of proposed policies in terms of expenditure, delivery, wellbeing, and health and socio-economic inequalities, thus improving current policy formulation, delivery risk stratification and evaluation. This H2020 project has a total of 15 partners from 5 EU countries as well as Arizona State University (ASU). The partners are Universities, SMEs and health departments in governmental institutions

Media Monitoring of Public Health Threats with MedISys

The Medical Information System (MedISys) is a fully automatic event-based surveillance system that monitors reporting on infectious diseases in man and animals, chemical, biological, radiological and nuclear (CBRN) threats, plant health and food & feed contaminations on the internet. The system retrieves news articles from specialised official and unofficial medical sites, general news media and selected blogs, categorizes all incoming articles according to pre-defined multilingual categories, identifies known names such as organizations, persons and locations, extracts events, clusters news articles and calculates statistics to detect emerging threats. Users can screen the categorized articles and display world maps highlighting event locations together with statistics on the reporting of health threats, countries and combinations thereof. Articles can be further filtered by language, news source, and country. Analysts can use a collaborative tool called NewsDesk to further refine the selection of automatically retrieved articles and create reports or deliver notifications via e-mail or SMS.JRC.G.2-Global security and crisis managemen

Combining Information about Epidemic Threats from Multiple Sources

This paper describes an on-going effort to combine Information Retrieval (IR) and Information Extraction (IE) technologies, to leverage the benefits provided by both approaches to add value for the end-user, as compared with IR or IE in isolation. The main aim of the combined system is to pool together information from multiple sources to improve the quality of results. On one hand, multiple mentions of the same event or related events should be presented in a coherent fashion. On the other hand, grouping related events should improve the system’s confidence in the discovered facts. We describe our approach and the results achieved in the project to date.

Natural Hazards: Changing Media Environments and the Efficient Use of ICT for Disaster Communication

The growing importance of mass media in the ‘information society’, combined with society’s increased dependence on electronic modes of information is important to the perception, regulation and management of risk at a local, national and international level. However, media organisations have their own logic and goals that are not necessarily compatible with the logic and goals of disaster planning and assistance agencies. Using a detailed study of the media coverage of floods in Switzerland from 1910 to 2005, we will illustrate the salient features of disaster reporting and how these relate to issues of risk perception and risk prevention behaviour in the public sphere. The findings are used to discuss the traditional media’s shortcomings for the goal of risk reduction, the public’s information seeking behaviour, and the opportunities and limitations arising from the emergence of digital, internet-based information and communication technologies (ICT) for disaster communication