2020 | Digital Health

Wang Y, Verspoor K, Baldwin T. (2020) Learning from Unlabelled Data for Clinical Semantic Textual Similarity. Proceedings of the 3rd Clinical Natural Language Processing Workshop at EMNLP2020.

Domain pretraining followed by task fine-tuning has become the standard paradigm for NLP tasks, but requires in-domain labelled data for task fine-tuning. To overcome this, we propose to utilise domain unlabelled data by assigning pseudo labels from a general model. We evaluate the approach on two clinical STS datasets, and achieve r= 0.80 on N2C2-STS.

Wang Y, Liu F, Verspoor K, Baldwin T (2020) Evaluating the Utility of Model Configurations and Data Augmentation on Clinical Semantic Textual Similarity. Proceedings of the Workshop on Biomedical Natural Language Processing (BioNLP) at ACL2020.

In this paper, we apply pre-trained language models to the Semantic Textual Similarity (STS) task, with a specific focus on the clinical domain. In low-resource setting of clinical STS, these large models tend to be impractical and prone to overfitting. Building on BERT, we study the impact of a number of model design choices, namely

Scott AM, Clark J, Mar CD, Glasziou P. Increased fluid intake to prevent urinary tract infections: systematic review and meta-analysis. Br J Gen Pract. 2020 Feb 27;70(692):e200-e207. doi: 10.3399/bjgp20X708125. PMID: 31988085; PMCID: PMC6988703.

Background: Approximately 15% of community-prescribed antibiotics are used in treating urinary tract infections (UTIs). Increase in antibiotic resistance necessitates considering alternatives. Aim: To assess the impact of increased fluid intake in individuals at risk for UTIs, for impact on UTI recurrence (primary outcome), antimicrobial use, and UTI symptoms (secondary outcomes). Design and setting: A systematic

Redfern J, Coorey G, Mulley J, Scaria A, Neubeck L, Hafiz N, Pitt C, Weir K, Forbes J, Parker S, Bampi F, Coenen A, Enright G, Wong A, Nguyen T, Harris M, Zwar N, Chow CK, Rodgers A, Heeley E, Panaretto K, Lau A, Hayman N, Usherwood T, Peiris D. A digital health intervention for cardiovascular disease management in primary care (CONNECT) randomized controlled trial. NPJ Digit Med. 2020 Sep 10;3:117. doi: 10.1038/s41746-020-00325-z. PMID: 32964140; PMCID: PMC7484809.

Digital health applications (apps) have the potential to improve health behaviors and outcomes. We aimed to examine the effectiveness of a consumer web-based app linked to primary care electronic health records (EHRs). CONNECT was a multicenter randomized controlled trial involving patients with or at risk of cardiovascular disease (CVD) recruited from primary care (Clinical Trial

Rahimi A, Baldwin T, Verspoor K. (2020) WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking. COLING 2020.

We present our work on aligning the Unified Medical Language System (UMLS) to Wikipedia, to facilitate manual alignment of the two resources. We propose a cross-lingual neural reranking model to match a UMLS concept with a Wikipedia page, which achieves a recall@1of 72%, a substantial improvement of 20% over word- and char-level BM25, enabling manual

Pederson M, Verspoor K, Jenkinson M, Law M, Abbott DF, Jackson GD (2020). Artificial Intelligence for clinical decision support in neurology. Brain Communications. doi: 10.1093/braincomms/fcaa096

Artificial intelligence is one of the most exciting methodological shifts in our era. It holds the potential to transform healthcare as we know it, to a system where humans and machines work together to provide better treatment for our patients. It is now clear that cutting edge artificial intelligence models in conjunction with high-quality clinical

Ellis LA, Lee MD, Ijaz K, Smith J, Braithwaite J, Yin K. COVID-19 as ‘Game Changer’ for the Physical Activity and Mental Well-Being of Augmented Reality Game Players During the Pandemic: Mixed Methods Survey Study. J Med Internet Res 2020;22(12):e25117

Background: Location-based augmented reality (AR) games, such as Pokémon GO and Harry Potter: Wizards Unite, have been shown to have a beneficial impact on the physical activity, social connectedness, and mental health of their players. In March 2020, global social distancing measures related to the COVID-19 pandemic prompted the AR games developer Niantic Inc to

Akhtyamova L, Martínez P, Verspoor K, Cardiff J. (2020) Testing Contextualized Word Embeddings to Improve NER in Spanish Clinical Case Narratives. IEEE Access. doi: 10.1109/ACCESS.2020.3018688

In the Big Data era, there is an increasing need to fully exploit and analyze the huge quantity of information available about health. Natural Language Processing (NLP) technologies can contribute by extracting relevant information from unstructured data contained in Electronic Health Records (EHR) such as clinical notes, patients’ discharge summaries and radiology reports. The extracted

Clark, J., Scott, A.M. and Glasziou, P., 2020. Not All Systematic Reviews Can Be Completed in 2 Weeks-But Many Can Be (And Should Be). Journal of Clinical Epidemiology, pp.S0895-4356.

Dear Editors, We would like to thank Yan et al. for providing us with an opportunity to expand on the applicability of our 2-week systematic review (2weekSR) processes [1]. Yan et al. correctly point out that “it is important to recognize not all systematic reviews (SRs) are created equal and complexity of the [SR] topic

Godinho MA, Borda A, Kostkova P, Molnar A, Liaw ST. ‘Serious Games’ for unboxing Global Digital Health policymaking. BMJ Stel 2020; 0:1–2. doi:10.1136/bmjstel-2020-000606

The news headlines report daily on the global political impacts of digital technology: the secondary use of social media data has been implicated in election meddling, though the complex issues surrounding data governance, data ownership and the ethics of personalised advertising remain to be addressed. Meanwhile, digital automation drives unemployment and income inequality, even as

Liaw ST, Georgiou A, Marin H. Evaluation of digital health and information technology in primary care. Int J Med Informatics 2020. DOI: https://doi.org/10.1016/j.ijmedinf.2020.104285.

Primary care is where the health care system meets the people – the first point of care for the citizenry. General practice and primary care DH&IT tools are essential strategies to strengthen health systems to achieve health priorities and universal health coverage and sustainable development goals. While primary care DH&IT systems are just as if

Struelens MJ, Sintchenko V. Pathogen genomics: Empowering infectious disease surveillance and outbreak investigations. Frontiers in Public Health 2020 May 19;8:179.

Comparative microbial genomics analysis by high-throughput whole-genome sequencing (WGS) offers exquisite resolution for epidemiological investigations of infectious disease. This approach has revolutionized outbreak detection and monitoring of transmission dynamics of infectious agents and antimicrobial resistance across humans, animals, and environment. The objective of this Research Topic was to assemble articles on genomic epidemiological approaches to

Williams RD, Markus AF, Yang C, Duarte Salles T, Duvall SL, Falconer T, Jonnagaddala J, Kim C, Rho Y, Williams A, Alberga A, An MH, Aragón M, Areia C, Burn E, Choi Y, Drakos I, Fernandes Abrahão M, Fernández-Bertolín S, Hripcsak G, Kaas-Hansen B, Kandukuri P, Kors JA, Kostka K, Liaw S-T, Lynch KE, Matheny ME, Machnicki G, Morales D, Nyberg F, Park RW, Prats-Uribe A, Pratt N, Rao G, Reich CG, Rivera M, Seinen T, Shoaibi A, Spotnitz ME, Steyerberg EW, Suchard MA, You SC, Zhang L, Zhou L, Ryan PB, Prieto-Alhambra D, Reps JM, Rijnbeek PR. Seek COVER: Development and validation of a personalized risk calculator for COVID-19 outcomes in an international network. medRxiv. 2020:2020.05.26.20112649. preprint

Objective: To develop and externally validate COVID-19 Estimated Risk (COVER) scores that quantify a patient’s risk of hospital admission (COVER-H), requiring intensive services (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis. Methods: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries. We

Reps JM, Kim C, Williams RD, Markus AF, Yang C, Salles TD, Falconer T, Jonnagaddala J, Williams A, Fernandez-Bertolin S, DuVall SL, Kostka K, Rao G, Shoaibi A, Ostropolets A, Spotnitz ME, Zhang L, Casajust P, Steyerberg E, Nyberg F, Kaas-Hansen BS, Choi YH, Morales D, Liaw S-T, Abrahao MTF, Areia C, Matheny ME, Aragon M, Park RW, Hripcsak G, Reich CG, Suchard MA, You SC, Ryan PB, Prieto-Alhambra D, Rijnbeek PR. Can we trust the prediction model? Demonstrating the importance of external validation by investigating the COVID-19 Vulnerability (C-19) Index across an international network of observational healthcare datasets. medRxiv. 2020:2020.06.15.20130328.

Background: SARS-CoV-2 is straining healthcare systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate between patients requiring hospitalization and those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia

Moynihan R, Sanders S, Michaleff ZA, Scott A, Clark J, To EJ, Jones M, Kitchener E, Fox M, Johansson M, Lang E, Duggan A, Scott I, Albarqouni L. Pandemic impacts on healthcare utilisation: a systematic review. medRxiv. 2020:2020.10.26.20219352.

Objectives: To determine the extent and nature of changes in utilisation of healthcare services during COVID-19 pandemic. Design: Systematic review. Eligibility: Eligible studies compared utilisation of services during COVID-19 pandemic to at least one comparable period in prior years. Services included visits, admissions, diagnostics, and therapeutics. Studies were excluded if from single-centres or studied only