November | 2020 | Digital Health

Chen J, Lyell D, Laranjo L, Magrabi F. Effect of Speech Recognition on Problem Solving and Recall in Consumer Digital Health Tasks: Controlled Laboratory Experiment. J Med Internet Res 2020;22(6):e14827 doi: 10.2196/14827[published Online First: Epub Date]

Background: Recent advances in natural language processing and artificial intelligence have led to widespread adoption of speech recognition technologies. In consumer health applications, speech recognition is usually applied to support interactions with conversational agents for data collection, decision support, and patient monitoring. However, little is known about the use of speech recognition in consumer health

Akbar S, Coiera, Enrico, Magrabi F. Safety concerns with consumer-facing mobile health applications and their consequences: a scoping review. Journal of the American Medical Informatics Association. 2019; 27(2):330-40.

Objective To summarize the research literature about safety concerns with consumer-facing health apps and their consequences. Materials and Methods We searched bibliographic databases including PubMed, Web of Science, Scopus, and Cochrane libraries from January 2013 to May 2019 for articles about health apps. Descriptive information about safety concerns and consequences were extracted and classified into

Yang Y, Walker TM, Walker AS, Wilson DJ, Peto TEA, Crook DW, Shamout F; CRyPTIC Consortium, Zhu T, Clifton DA. DeepAMR for predicting co-occurent resistance of Mycobacterium tuberculosis. Bioinformatics 2019;35(18):3240-3249.

Motivation: Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages. Results: We

Vo K, Jonnagaddala J and Liaw ST. Statistical supervised meta-ensemble algorithm for medical record linkage. J Biomed Inform. (online publication). 2019. https://doi.org/10.1016/j.jbi.2019.103220

Identifying unique patients across multiple care facilities or services is a major challenge in providing continuous care and undertaking health research. Identifying and linking patients without compromising privacy and security is an emerging issue in the big data era. The large quantity and complexity of the patient data emphasize the need for effective linkage methods

O’Connor AM, Tsafnat G, Gilbert SB, Thayer KA, Shemilt I, Thomas J, Glasziou P, Wolfe MS. Still moving toward automation of the systematic review process: a summary of discussions at the third meeting of the International Collaboration for Automation of Systematic Reviews (ICASR). Systematic Reviews. 2019; 8(1):57.

The third meeting of the International Collaboration for Automation of Systematic Reviews (ICASR) was held 17–18 October 2017 in London, England. ICASR is an interdisciplinary group whose goal is to maximize the use of technology for conducting rapid, accurate, and efficient systematic reviews of scientific evidence. The group seeks to facilitate the development and widespread

Liyanage H, Liaw ST, Jonnagaddala J, et al. Artificial Intelligence in Primary Health Care: Perceptions, Issues, and Challenges. Yearbook of Medical Informatics 2019. http://dx.doi.org/10.1055/s-0039-1677901

Background: Artificial intelligence (AI) is heralded as an approach that might augment or substitute for the limited processing power of the human brain of primary health care (PHC) professionals. However, there are concerns that AI-mediated decisions may be hard to validate and challenge, or may result in rogue decisions. Objective: To form consensus about perceptions,

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

Domestic Scholarships – COVID-19 and future crisis preparedness in healthcare

Apply now The Australian Institute of Health Innovation at Macquarie University are seeking suitably qualified candidates with pioneering ideas for research into understanding the current health system response to the pandemic and strategies for future crisis preparedness. They have FIVE scholarships available with the Ph.D. topic to be determined taking into account the interests, experience, and prior