Publications | Digital Health

Lau AYS, Staccini P; Artificial Intelligence in Health: New Opportunities, Challenges, and Practical Implications. Section Editors for the IMIA Yearbook Section on Education and Consumer Health Informatics. Yearb Med Inform. 2019 Aug;28(1):174-178. doi: 10.1055/s-0039-1677935. Epub 2019 Aug 16.

Objectives : To summarise the state of the art during the year 2018 in consumer health informatics and education, with a special emphasis on the special topic of the International Medical Informatics Association (IMIA) Yearbook for 2019: “Artificial intelligence in health: new opportunities, challenges, and practical implications”. Methods : We conducted a systematic search of

Hunt M, Bradley P, Lapierre SG, Heys S, Thomsit M, Hall MB, Malone KM, Wintringer P, Walker TM, Cirillo DM, Comas I, Farhat MR, Fowler P, Gardy J, Ismail N, Kohl TA, Mathys V, Merker M, Niemann S, Omar SV, Sintchenko V, Smith G, van Soolingen D, Supply P, Tahseen S, Wilcox M, Arandjelovic I, Peto TEA, Crook DW, Iqbal Z. Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe. Wellcome Open Res. 2019 Dec 2;4:191. doi: 10.12688/wellcomeopenres.15603.1. PMID: 32055708; PMCID: PMC7004237.

Two billion people are infected with Mycobacterium tuberculosis, leading to 10 million new cases of active tuberculosis and 1.5 million deaths annually. Universal access to drug susceptibility testing (DST) has become a World Health Organization priority. We previously developed a software tool, Mykrobe predictor, which provided offline species identification and drug resistance predictions for M.

Guo GN, Jonnagaddala J, Farshid S, Huser V, Reich C, & Liaw ST. Comparison of the cohort selection performance of Australian Medicines Terminology to Anatomical Therapeutic Chemical mappings. JAMIA 2019. doi:10.1093/jamia/ocz143

Objective: Electronic health records are increasingly utilized for observational and clinical research. Identification of cohorts using electronic health records is an important step in this process. Previous studies largely focused on the methods of cohort selection, but there is little evidence on the impact of underlying vocabularies and mappings between vocabularies used for cohort selection.

Denecke K, Gabarron E, Grainger R, Konstantinidis ST, Lau A, Rivera-Romero O, Miron-Shatz T, Merolli M. Artificial Intelligence for Participatory Health: Applications, Impact, and Future Implications. Yearb Med Inform. 2019 Aug;28(1):165-173. doi: 10.1055/s-0039-1677902. Epub 2019 Apr 25.

Objective: Artificial intelligence (AI) provides people and professionals working in the field of participatory health informatics an opportunity to derive robust insights from a variety of online sources. The objective of this paper is to identify current state of the art and application areas of AI in the context of participatory health. Methods: A search

Liyanage H; Liaw ST; Konstantara E; Mold F; Schreiber R; Kuziemsky C; Terry AL; de Lusignan S. ‘Benefit-risk of Patients’ Online Access to their Medical Records: Consensus Exercise of an International Expert Group’, Yearbook of Medical Informatics 2018, vol. 27, pp. 156–162; DOI http://dx.doi.org/10.1055/s-0038-1641202

Background: Patients’ access to their computerised medical records (CMRs) is a legal right in many countries. However, little is reported about the benefit-risk associated with patients’ online access to their CMRs. Objective: To conduct a consensus exercise to assess the impact of patients’ online access to their CMRs on the quality of care as defined in six

Liyanage H; Liaw ST; Jonnagaddala J; Hinton W; De Lusignan S, 2018, ‘Common Data Models (CDMs) to Enhance International Big Data Analytics: A Diabetes Use Case to Compare Three CDMs’, Studies in Health Technology and Informatics, vol. 255, pp. 60 – 64, DOI http://dx.doi.org/10.3233/978-1-61499-921-8-60.

Common data models (CDM) have enabled the simultaneous analysis of disparate and large data sources. A literature review identified three relevant CDMs: The Observational Medical Outcomes Partnership (OMOP) was the most cited; next the Sentinel; and then the Patient Centered Outcomes Research Institute (PCORI). We tested these three CDMs with fifteen pre-defined criteria for a

Ford L, Carter GP, Wang Q, Seemann T, Sintchenko V, Glass K, Williamson DA, Howard P, Valcanis M, Castillo CF, Sait M, Howden BP, Kirk MD. Incorporating whole genome sequencing into public health surveillance: Lessons from prospective sequencing of Salmonella Typhimurium in Australia. Foodborne Pathogens and Disease 2018; 15(3):161-167.

In Australia, the incidence of Salmonella Typhimurium has increased dramatically over the past decade. Whole-genome sequencing (WGS) is transforming public health microbiology, but poses challenges for surveillance. To compare WGS-based approaches with conventional typing for Salmonella surveillance, we performed concurrent WGS and multilocus variable-number tandem-repeat analysis (MLVA) of Salmonella Typhimurium isolates from the Australian Capital

Beller E, Clark J, Tsafnat G, Adams C, Diehl H, Lund H, Ouzzani M, Thayer K, Thomas J, Turner T, Xia J, Robinson K, Glasziou P, founding members of the Ig. Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR). Systematic reviews. Syst Rev 7, 77 (2018)

Systematic reviews (SR) are vital to health care, but have become complicated and time-consuming, due to the rapid expansion of evidence to be synthesised. Fortunately, many tasks of systematic reviews have the potential to be automated or may be assisted by automation. Recent advances in natural language processing, text mining and machine learning have produced

Quiroz JC, Laranjo L, Kocaballi AB, Briatore A, Berkovsky S, Rezazadegan D, Coiera E. Identifying relevant information in medical conversations to summarize a clinician-patient encounter. Health Informatics Journal. 0(0):1460458220951719.

To inform the development of automated summarization of clinical conversations, this study sought to estimate the proportion of doctor-patient communication in general practice (GP) consultations used for generating a consultation summary. Two researchers with a medical degree read the transcripts of 44 GP consultations and highlighted the phrases to be used for generating a summary

Rezazadegan D, Berkovsky S, Quiroz JC, Kocaballi AB, Wang Y, Laranjo L, Coiera E. Automatic Speech Summarisation: A Scoping Review. arXiv preprint arXiv:200811897. 2020

Speech summarisation techniques take human speech as input and then output an abridged version as text or speech. Speech summarisation has applications in many domains from information technology to health care, for example improving speech archives or reducing clinical documentation burden. This scoping review maps the speech summarisation literature, with no restrictions on time frame,

Kocaballi AB, Ijaz K, Laranjo L, Quiroz JC, Rezazadegan D, Tong HL, Willcock S, Berkovsky S, Coiera E. Envisioning an artificial intelligence documentation assistant for future primary care consultations: A co-design study with general practitioners. Journal of the American Medical Informatics Association. 2020.

Objective The study sought to understand the potential roles of a future artificial intelligence (AI) documentation assistant in primary care consultations and to identify implications for doctors, patients, healthcare system, and technology design from the perspective of general practitioners. Materials and Methods Co-design workshops with general practitioners were conducted. The workshops focused on (1) understanding

Wang Y, Coiera E, Magrabi F. Can Unified Medical Language System–based semantic representation improve automated identification of patient safety incident reports by type and severity? Journal of the American Medical Informatics Association. 2020.

Objective The study sought to evaluate the feasibility of using Unified Medical Language System (UMLS) semantic features for automated identification of reports about patient safety incidents by type and severity. Materials and Methods Binary support vector machine (SVM) classifier ensembles were trained and validated using balanced datasets of critical incident report texts (n_type = 2860, n_severity = 1160) collected

Hassanzadeh H, Karimi S, Nguyen. Matching Patients to Clinical Trials Using Semantically Enriched Document Representation. Journal of Biomedical Informatics, Volume 105, 2020, 103406

Recruiting eligible patients for clinical trials is crucial for reliably answering specific questions about medical interventions and evaluation. However, clinical trial recruitment is a bottleneck in clinical research and drug development. Our goal is to provide an approach towards automating this manual and time-consuming patient recruitment task using natural language processing and machine learning techniques.

Quiroz JC, Laranjo L, Tufanaru C, Kocaballi AB, Rezazadegan D, Berkovsky S, Coiera E. Empirical Analysis of Zipf’s Law, Power Law, and Lognormal Distributions in Medical Discharge Reports 2020, Eprint 2003.13352, ArXiv, Cs.CL

Bayesian modelling and statistical text analysis rely on informed probability priors to encourage good solutions. This paper empirically analyses whether text in medical discharge reports follow Zipf’s law, a commonly assumed statistical property of language where word frequency follows a discrete power law distribution. We examined 20,000 medical discharge reports from the MIMIC-III dataset. Methods

Yin K, Jung J, Coiera E, Laranjo L, Blandford A, Khoja A, Tai W, Phillips DP, Lau AYS. Patient Work and Contexts – A Scoping Review. J Med Internet Res 2020;22(6):e16656

Background: Self-management (or self-care) is widely promoted but many patients struggle to practise it effectively. Moreover, few studies have analysed the nature and volume of work required from patients in self-care and how such work fits into the context of their daily life. Objective: To review the characteristics of patient work in adult patients. Patient