SIPHS: Semantic interpretation of personal health messages for generating public health summaries

Open online data such as microblogs and discussion board messages have the potential to be an incredibly valuable source of information about health in populations. Such data has been rapidly growing, is low cost, real-time and seems likely to cover a significant proportion of the demographic. To take two examples, PatientsLikeMe has enjoyed 10% growth and now has over 200,000 users covering over 1500 health conditions; the generic Twitter service is expanding at a rate of 30% annually with over 200 million active users. Going beyond simple keyword search and harnessing this data for public health represents both an opportunity and a challenge to natural language processing (NLP). This fellowship proposal is about helping health experts leverage social media for their own clinical and scientific studies through automatic techniques that encode messages according to a machine understandable semantic representation. There are three major challenges this project seeks to address: (1) knowledge brokering: to develop algorithms to identify and code the informal descriptions of conditions, treatments, medications, behaviours and attitudes to standard ontologies such as the UMLS; (2) knowledge management: to create a structured resource of patient vocabulary used in blog texts and link it to existing coding systems; and (3) adding insight to evidence: to work with domain experts to utilize the coded information to automatically generate meaningful summaries for follow up investigation. At the technological level the fellowship seeks to pioneer new methods for NLP and machine learning (ML). Social media remains a challenging area for NLP for a variety of reasons: short de-contextualised messages, high levels of ambiguity/out of vocabulary words, use of slang and an evolving vocabulary, as well as inherent bias towards sensational topics. The fellowship seeks to harness the progress made so far in NLP for social media analysis in the commercial domain and develop it further to provide meaningful public health evidence. One key aspect not previously addressed is in the clinical coding of patient messages. Although knowledge brokering systems exist for clinical and scientific texts (e.g. the NLM's MetaMap), their performance on social media messages has been poor. The fellowship will utilise the rich availability of ontological resources in biomedicine together with ML on annotated message data to disambiguate informal language. Research will also aim to understanding the communicative function of messages, for example whether the message reports direct experience or is related to news, humour or marketing. If these problems are successfully overcome an important barrier to data integration with other types of clinical data will be removed. The advantage of providing health coding for social media reports is its potential for studying very-large scale cohorts and also in real-time early alerting of aberrations. In the fellowship I will research the potential for multi-variate time series alerting from semantically coded features, working with domain experts to evaluate across a range of metrics (e.g. sensitivity, timeliness, false alerting rates). A variety of approaches will be explored to generate real time risk summaries across social media sources. Two real-world applications have been chosen to take this forwards: early alerting for Adverse drug reactions (ADRs) and Infectious disease surveillance (IDS). Project outcomes will include fundamental technologies as well as open source algorithms, data sets and ontology. An exciting aspect of this fellowship is inter-disciplinary collaboration across stakeholders at all levels: scientists, public health experts and industry. Finally, participation will be opened up to the international community through the release of open source data. Colleagues working on social media technologies will be invited to participate in discussions with users at a new challenge evaluation workshop.

The SIPHS project aims to revolutionise how health experts leverage personal health evidence for their own clinical and scientific studies through automatic techniques that encode social media messages according to a machine understandable semantic representation. SIPHS will deliver state of the art knowledge extraction solutions for evidence relating to human diseases. This is highly relevant to a range of experts across domains such as public health, pharmacology and molecular biology.

Who will benefit from this research?
1. Public health experts performing infectious disease surveillance (IDS), situation awareness and risk assessment functions will benefit from becoming more efficient and having access to earlier warnings and greater coverage about health threats such as pandemic influenza, chemical/ biological/ radiological/ nuclear (CBRN) terrorist attacks;
2. Researchers and engineers in human language technologies, e-Science and information retrieval will benefit from software tools and data sets that can reliably encode social media messages for clinically important concepts;
3. The pharmaceutical industry and those involved in biotechnology and drug discovery will benefit from having access to a new and extensive database of evidence about adverse drug reactions and potentially novel therapeutic properties for licensed drugs;
4. Life scientists and clinicians involved in translational studies will benefit from having a novel database of evidence about phenotype associations to drugs and human diseases that links to the existing scientific and clinical data infrastructure through networks. As noted in Section 2(b) I reiterate that SIPHS is highly relevant to initiatives such as ELIXIR which coordinates and links European biomedical resources;
5. The public will benefit from having improved technologies for early detection of health threats and improved understanding about those technologies through the PI's outreach activities, e.g. a public blog, participation in the Cambridge Science Festival, press releases and a Wikipedia page.

How will they benefit?
1. Building on Dr. Collier's existing global public health network, the PI will continue to work directly with public health experts at Public Health England, the CORDS network and at the WHO to deploy the proposed technologies and database. The innovative techniques advocated in this proposal extends proven high throughput techniques developed by the PI which successfully detected A(H1N1). The techniques supplement scarce human expertise, bring in evidence beyond national boundaries and cover segments of the population who may not interact with traditional sensor networks (e.g. patients who may not visit a GP). The novel techniques will be measured against existing human surveillance network standard;
2. The fellowship pioneers new methods for Natural Language Processing (NLP) and Machine Learning (ML) on social media. We propose to develop a novel combination of supervised and semi-supervised approaches on maximally rich NLP features in order to understand the context of personal health messages, ground layman's terms to clinical standards and provide timely alert summaries. Researchers and engineers will benefit from tools, data sets and techniques;
3. The technology in this proposal will help the pharmaceutical industry in the monitoring of patient reports for ADRs as required by EU and national regulations and to reveal novel therapeutics;
4. The database developed through the SIPHS project will generate high visibility in the lifescience and clinical communities. The integration of the different data resources and the automatic analysis of the social media will lead to benefits for the research community and the general public. If the problem of message coding in personal health messages is successfully overcome an important barrier to data integration - for example with data from clinical trials or electronic patient records - will be removed.