Chen A, Jonnagaddala J, Nekkantti C, Liaw ST. Generation of surrogates for de-identification of electronic health records. Paper presentation. Medinfo 2019: Health and Wellbeing e-Networks for All. L. Ohno-Machado & B. Séroussi (Eds) © 2019 International Medical Informatics Association (IMIA) and IOS Press. doi:10.3233/SHTI190185

Unstructured electronic health records are valuable resources for research. Before they are shared with researchers, protected health information needs to be removed from these unstructured documents to protect patient privacy. The main steps involved in removing protected health information are accurately identifying sensitive information in the documents and removing the identified information. To keep the

Williamson D, Kirk M, Sintchenko V, Howden B. The importance of public health genomics to ensure health security for Australia. Medical Journal of Australia 2019;210(7):295-297.e1.

Coordination is required to future‐proof Australia’s capacity and leadership in public health genomics. Infectious diseases are an ever‐present risk to society, particularly because of globalisation and the threat of antimicrobial‐resistant organisms. Recently, a World Health Organization (WHO) team conducted a joint external evaluation of Australia’s core capacities under the International Health Regulations. The evaluation gave

Scott AM, Clark J, Del Mar C, Glasziou P, Increased fluid intake to prevent urinary tract infections: systematic review and meta-analysis. British Journal of General Practice, 70(692), pp.e200-e207.

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

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.

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.

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

Kocaballi AB, Coiera E, Tong HL, White S, Quiroz JC, Rezazadegan F, Willcock S, Laranjo L. A network model of activities in primary care consultations. Journal of the American Medical Informatics Association. 2019; 26(10):1074-82.

Objective The objective of this study is to characterize the dynamic structure of primary care consultations by identifying typical activities and their inter-relationships to inform the design of automated approaches to clinical documentation using natural language processing and summarization methods. Materials and Methods This is an observational study in Australian general practice involving 31 consultations

Kocaballi AB, Berkovsky S, Quiroz J, Laranjo da Silva L, Tong HL, Rezadegan D, Briatore A, Coiera E. The Personalization of conversational agents in healthcare: a systematic review. Journal of Medical Internet Research. 2019; 21(11):e15360.

Background: The personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents. Objective: The goal of this systematic review was to understand the ways in which personalization has been used with conversational agents in