| Predicting length of stay and mortality among hospitalized patients with type 2 diabetes mellitus and hypertension | 1 | 0.36 | 2021 |
| Characterizing database granularity using SNOMED-CT hierarchy. | 0 | 0.34 | 2020 |
| mHealth for Integrated People-Centred Health Services in the Western Pacific: A Systematic Review | 0 | 0.34 | 2020 |
| Cohort Selection for Clinical Trials Using Multiple Instance Learning. | 0 | 0.34 | 2020 |
| Generation of Surrogates for De-Identification of Electronic Health Records. | 0 | 0.34 | 2019 |
| Comparison of the cohort selection performance of Australian Medicines Terminology to Anatomical Therapeutic Chemical mappings. | 0 | 0.34 | 2019 |
| Statistical principle-based approach for recognizing and normalizing microRNAs described in scientific literature. | 0 | 0.34 | 2019 |
| Statistical supervised meta-ensemble algorithm formedical record linkage. | 0 | 0.34 | 2019 |
| Enhanced functionalities for annotating and indexing clinical text with the NCBO Annotator+. | 1 | 0.34 | 2018 |
| Common Data Models (CDMs) to Enhance International Big Data Analytics: A Diabetes Use Case to Compare Three CDMs. | 0 | 0.34 | 2018 |
| Automatic Coding of Death Certificates to ICD-10 Terminology. | 0 | 0.34 | 2017 |
| ZikaHack 2016: A digital disease detection competition. | 0 | 0.34 | 2017 |
| Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms. | 0 | 0.34 | 2017 |
| Combining Multiple Classifiers Using Global Ranking for ReachOut.com Post Triage. | 0 | 0.34 | 2016 |
| Feature Engineering For Recognizing Adverse Drug Reactions From Twitter Posts | 7 | 0.50 | 2016 |
| NTTMUNSW BioC modules for recognizing and normalizing species and gene/protein mentions. | 2 | 0.43 | 2016 |
| MET network in PubMed: a text-mined network visualization and curation system. | 4 | 0.40 | 2016 |
| Improving the dictionary lookup approach for disease normalization using enhanced dictionary and query expansion. | 3 | 0.41 | 2016 |
| Integration and Analysis of Heterogeneous Colorectal Cancer Data for Translational Research. | 1 | 0.35 | 2016 |
| A preliminary study on automatic identification of patient smoking status in unstructured electronic health records | 2 | 0.41 | 2015 |
| Impact of data quality assessment on development of clinical predictive models. | 0 | 0.34 | 2015 |
| TMUNSW: Identification of disorders and normalization to SNOMED-CT terminology in unstructured clinical notes | 1 | 0.39 | 2015 |
| Utilizing different word representation methods for twitter data in adverse drug reactions extraction | 2 | 0.37 | 2015 |
| A context-aware approach for progression tracking of medical concepts in electronic medical records | 7 | 0.52 | 2015 |
| Coronary artery disease risk assessment from unstructured electronic health records using text mining | 10 | 0.52 | 2015 |
| TMUNSW: Disorder Concept Recognition and Normalization in Clinical Notes for SemEval-2014 Task 7 | 3 | 0.47 | 2014 |
| HTNSystem: Hypertension Information Extraction System for Unstructured Clinical Notes. | 2 | 0.42 | 2014 |
