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Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data.

Journal of medical Internet research
BACKGROUND: Uptake of medicinal drugs (preventive or treatment) is among the approaches used to control disease outbreaks, and therefore, it is of vital importance to be aware of the counts or frequencies of most commonly used drugs and trending topi...

Lightweight predicate extraction for patient-level cancer information and ontology development.

BMC medical informatics and decision making
BACKGROUND: Knowledge engineering for ontological knowledgebases is resource and time intensive. To alleviate these issues, especially for novices, automated tools from the natural language domain can assist in the development process of ontologies. ...

The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance.

Applied clinical informatics
OBJECTIVES: This study evaluates the accuracy and portability of a natural language processing (NLP) tool for extracting clinical findings of influenza from clinical notes across two large healthcare systems. Effectiveness is evaluated on how well NL...

Machine-Learning Algorithms to Code Public Health Spending Accounts.

Public health reports (Washington, D.C. : 1974)
OBJECTIVES: Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with...

Ontology-based automatic identification of public health-related Turkish tweets.

Computers in biology and medicine
Social media analysis, such as the analysis of tweets, is a promising research topic for tracking public health concerns including epidemics. In this paper, we present an ontology-based approach to automatically identify public health-related Turkish...

Deep Learning for Health Informatics.

IEEE journal of biomedical and health informatics
With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learn...

An unsupervised machine learning model for discovering latent infectious diseases using social media data.

Journal of biomedical informatics
INTRODUCTION: The authors of this work propose an unsupervised machine learning model that has the ability to identify real-world latent infectious diseases by mining social media data. In this study, a latent infectious disease is defined as a commu...

Tackling Missing Data in Community Health Studies Using Additive LS-SVM Classifier.

IEEE journal of biomedical and health informatics
Missing data is a common issue in community health and epidemiological studies. Direct removal of samples with missing data can lead to reduced sample size and information bias, which deteriorates the significance of the results. While data imputatio...

A knowledge base for tracking the impact of genomics on population health.

Genetics in medicine : official journal of the American College of Medical Genetics
PURPOSE: We created an online knowledge base (the Public Health Genomics Knowledge Base (PHGKB)) to provide systematically curated and updated information that bridges population-based research on genomics with clinical and public health applications...