A scoping review of the use of Twitter for public health research.

Public health practitioners and researchers have used traditional medical databases to study and understand public health for a long time. Recently, social media data, particularly Twitter, has seen some use for public health purposes. Every large technological development in history has had an impact on the behaviour of society.

An Exploration of Deep-Learning Based Phenotypic Analysis to Detect Spike Regions in Field Conditions for UK Bread Wheat.

Wheat is one of the major crops in the world, with a global demand expected to reach 850 million tons by 2050 that is clearly outpacing current supply. The continual pressure to sustain wheat yield due to the world's growing population under fluctuating climate conditions requires breeders to increase yield and yield stability across environments. We are working to integrate deep learning into field-based phenotypic analysis to assist breeders in this endeavour.

Twitter mining using semi-supervised classification for relevance filtering in syndromic surveillance.

We investigate the use of Twitter data to deliver signals for syndromic surveillance in order to assess its ability to augment existing syndromic surveillance efforts and give a better understanding of symptomatic people who do not seek healthcare advice directly. We focus on a specific syndrome-asthma/difficulty breathing. We outline data collection using the Twitter streaming API as well as analysis and pre-processing of the collected data.

Building Data-Driven Pathways From Routinely Collected Hospital Data: A Case Study on Prostate Cancer.

Background: Routinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden patterns, and improve services and cost. The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood.

Predictors of orthostatic hypotension in patients attending a transient ischaemic attack clinic: database study.

Background: Orthostatic hypotension (OH) is common amongst the older population and is associated with morbidity and mortality. We sought to investigate predictors of OH to assist the clinician in identifying patients at risk.

Methods And Results: Database of 2696 patients attending a transient ischaemic attack (TIA) clinic between January 2006 and May 2009 was examined.

Performance of the ASSIGN cardiovascular disease risk score on a UK cohort of patients from general practice.

Objective: To evaluate the performance of ASSIGN against the Framingham equations for predicting 10 year risk of cardiovascular disease in a UK cohort of patients from general practice and to make the evaluation comparable to an independent evaluation of QRISK on the same cohort.

Design: Prospective open cohort study. Setting 288 practices from England and Wales contributing to The Health Improvement Network (THIN) database.

Classification techniques with minimal labelling effort and application to medical reports.

There are a number of approaches to classify text documents. Here, we use Partially Supervised Classification (PSC) and argue that it is an effective and efficient approach for real-world problems. PSC uses a two-step strategy to cut down on the labelling effort.

Determining a unique defining DNA sequence for yeast species using hashing techniques.

Motivation: Yeasts are often still identified with physiological growth tests, which are both time consuming and unsuitable for detection of a mixture of organisms. Hence, there is a need for molecular methods to identify yeast species.

Results: A hashing technique has been developed to search for unique DNA sequences in 702 26S rRNA genes.