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. The advent of the internet and social media is no different. Social media creates public streams of communication, and scientists are starting to understand that such data can provide some level of access into the people's opinions and situations. As such, this paper aims to review and synthesize the literature on Twitter applications for public health, highlighting current research and products in practice. A scoping review methodology was employed and four leading health, computer science and cross-disciplinary databases were searched. A total of 755 articles were retreived, 92 of which met the criteria for review. From the reviewed literature, six domains for the application of Twitter to public health were identified: (i) Surveillance; (ii) Event Detection; (iii) Pharmacovigilance; (iv) Forecasting; (v) Disease Tracking; and (vi) Geographic Identification. From our review, we were able to obtain a clear picture of the use of Twitter for public health. We gained insights into interesting observations such as how the popularity of different domains changed with time, the diseases and conditions studied and the different approaches to understanding each disease, which algorithms and techniques were popular with each domain, and more.

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. Even with keyword-based data collection, many of the tweets collected are not be relevant because they represent chatter, or talk of awareness instead of an individual suffering a particular condition. In light of this, we set out to identify relevant tweets to collect a strong and reliable signal. For this, we investigate text classification techniques, and in particular we focus on semi-supervised classification techniques since they enable us to use more of the Twitter data collected while only doing very minimal labelling. In this paper, we propose a semi-supervised approach to symptomatic tweet classification and relevance filtering. We also propose alternative techniques to popular deep learning approaches. Additionally, we highlight the use of emojis and other special features capturing the tweet's tone to improve the classification performance. Our results show that negative emojis and those that denote laughter provide the best classification performance in conjunction with a simple word-level n-gram approach. We obtain good performance in classifying symptomatic tweets with both supervised and semi-supervised algorithms and found that the proposed semi-supervised algorithms preserve more of the relevant tweets and may be advantageous in the context of a weak signal. Finally, we found some correlation (r = 0.414, p = 0.0004) between the Twitter signal generated with the semi-supervised system and data from consultations for related health conditions.

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. We have utilised wheat images collected by distributed CropQuant phenotyping workstations deployed for multiyear field experiments of UK bread wheat varieties. Based on these image series, we have developed a deep-learning based analysis pipeline to segment spike regions from complicated backgrounds. As a first step towards robust measurement of key yield traits in the field, we present a promising approach that employ Fully Convolutional Network (FCN) to perform semantic segmentation of images to segment wheat spike regions. We also demonstrate the benefits of transfer learning through the use of parameters obtained from other image datasets. We found that the FCN architecture had achieved a Mean classification Accuracy (MA) >82% on validation data and >76% on test data and Mean Intersection over Union value (MIoU) >73% on validation data and and >64% on test datasets. Through this phenomics research, we trust our attempt is likely to form a sound foundation for extracting key yield-related traits such as spikes per unit area and spikelet number per spike, which can be used to assist yield-focused wheat breeding objectives in near future.

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. Nevertheless, using routine data for the construction of data-driven clinical pathways, describing processes and trends, is a key topic receiving increasing attention in the literature. Traditional algorithms do not cope well with unstructured processes or data, and do not produce clinically meaningful visualizations. Supporting systems that provide additional information, context, and quality assurance inspection are needed. OBJECTIVE: The objective of the study is to explore how routine hospital data can be used to develop data-driven pathways that describe the journeys that patients take through care, and their potential uses in biomedical research; it proposes a framework for the construction, quality assessment, and visualization of patient pathways for clinical studies and decision support using a case study on prostate cancer. METHODS: Data pertaining to prostate cancer patients were extracted from a large UK hospital from eight different HIS, validated, and complemented with information from the local cancer registry. Data-driven pathways were built for each of the 1904 patients and an expert knowledge base, containing rules on the prostate cancer biomarker, was used to assess the completeness and utility of the pathways for a specific clinical study. Software components were built to provide meaningful visualizations for the constructed pathways. RESULTS: The proposed framework and pathway formalism enable the summarization, visualization, and querying of complex patient-centric clinical information, as well as the computation of quality indicators and dimensions. A novel graphical representation of the pathways allows the synthesis of such information. CONCLUSIONS: Clinical pathways built from routinely collected hospital data can unearth information about patients and diseases that may otherwise be unavailable or overlooked in hospitals. Data-driven clinical pathways allow for heterogeneous data (ie, semistructured and unstructured data) to be collated over a unified data model and for data quality dimensions to be assessed. This work has enabled further research on prostate cancer and its biomarkers, and on the development and application of methods to mine, compare, analyze, and visualize pathways constructed from routine data. This is an important development for the reuse of big data in hospitals.

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. Logistic regression models were constructed to determine clinical associates of OH. Demographics, co-morbidities, cardiovascular risk factors and medications were included in the multivariate models. Simple data mining models in the form of rule sets were developed for each component and they were assessed for predictive accuracy. The best models were validated on a smaller sample. Prevalence of OH was 22.3% in the TIA clinic population (50.6% men, mean 72 years; 49.4% women, mean 75 years). A significant postural drop in systolic blood pressure (BP) (≥ 20 mmHg) was more prevalent than a significant diastolic BP drop (≥ 10 mmHg). Isolated systolic hypertension was common (52.4%). Common factors predicting a significant systolic and diastolic BP fall were older age, previous TIA, being a current smoker, having diabetes and the use of beta-blockers. Both mean arterial and pulse pressure (MAP and PP) derived from supine BP were significantly associated with OH. CONCLUSIONS: OH should be assessed routinely in TIA clinics. MAP and PP may provide information on the predictability of OH.

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. PARTICIPANTS: Patients registered with 288 UK practices for some period between January 1995 and March 2006. The number of records available was 1,787,169. MAIN OUTCOME MEASURES: First diagnosis of myocardial infarction, coronary heart disease, stroke and transient ischaemic attacks recorded. Methods We implemented the Anderson Framingham Coronary Heart Disease and Stroke models, ASSIGN, and a more recent Framingham Cox proportional-hazards model and analysed their calibration and discrimination. RESULTS: Calibration showed that all models tested over-estimated risk particularly for men. ASSIGN showed better discrimination with higher AUROC (0.756/0.792 for men/women), D statistic (1.35/1.58 for men/women), and R²(30.47%/37.39% for men/women). The performance of ASSIGN was comparable to that of QRISK on the same cohort. Models agreed on 93-97% of categorical (high/lower) risk assessments and when they disagreed, ASSIGN was often closer to the estimated Kaplan-Meier incidence. ASSIGN also provided a steeper gradient of deprivation and discriminated between those with and without recorded family history of CVD. The estimated incidence was twice/three times as high for women/men with a recorded family history of CVD. CONCLUSIONS: For systematic CVD risk assessment all models could usefully be applied, but ASSIGN improved on the gradient of deprivation and accounted for recorded family history whereas the Framingham equations did not. However, all models display relatively low specificity and sensitivity. An additional conclusion is that the recording of family history of CVD in primary care databases needs to improve given its importance in risk assessment.

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. There are a number of methods that have been proposed for each step. An evaluation of various methods is conducted using real-world medical documents. The results show that using EM to build the classifier yields better results than SVM. We also experimentally show that careful selection of a subset of features to represent the documents can improve performance.

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. A unique DNA sequence has been found for almost every yeast species described to date. The locations of the unique defining sequences are in accordance with the variability map of large subunit ribosomal RNA and provide detail of the evolution of the D1/D2 region. This approach will be applicable to the rapid identification of unique sequences in other DNA sequence sets. AVAILABILITY: Freely available upon request from the authors. SUPPLEMENTARY INFORMATION: Results are available at http://www.sys.uea.ac.uk/~jjw/project/paper