{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:00:44Z","timestamp":1764784844528,"version":"build-2065373602"},"reference-count":93,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T00:00:00Z","timestamp":1645747200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Laboratory tests play an essential role in the early and accurate diagnosis of diseases. In this paper, we propose SUNRISE, a visual analytics system that allows the user to interactively explore the relationships between laboratory test results and a disease outcome. SUNRISE integrates frequent itemset mining (i.e., Eclat algorithm) with extreme gradient boosting (XGBoost) to develop more specialized and accurate prediction models. It also includes interactive visualizations to allow the user to interact with the model and track the decision process. SUNRISE helps the user probe the prediction model by generating input examples and observing how the model responds. Furthermore, it improves the user\u2019s confidence in the generated predictions and provides them the means to validate the model\u2019s response by illustrating the underlying working mechanism of the prediction models through visualization representations. SUNRISE offers a balanced distribution of processing load through the seamless integration of analytical methods with interactive visual representations to support the user\u2019s cognitive tasks. We demonstrate the usefulness of SUNRISE through a usage scenario of exploring the association between laboratory test results and acute kidney injury, using large provincial healthcare databases from Ontario, Canada.<\/jats:p>","DOI":"10.3390\/informatics9010017","type":"journal-article","created":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T10:00:40Z","timestamp":1645783240000},"page":"17","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Visual Analytics for Predicting Disease Outcomes Using Laboratory Test Results"],"prefix":"10.3390","volume":"9","author":[{"given":"Neda","family":"Rostamzadeh","sequence":"first","affiliation":[{"name":"Insight Lab, Western University, London, ON N6A 3K7, Canada"},{"name":"ICES, London, ON N6A 3K7, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2452-8494","authenticated-orcid":false,"given":"Sheikh S.","family":"Abdullah","sequence":"additional","affiliation":[{"name":"Insight Lab, Western University, London, ON N6A 3K7, Canada"},{"name":"ICES, London, ON N6A 3K7, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kamran","family":"Sedig","sequence":"additional","affiliation":[{"name":"Insight Lab, Western University, London, ON N6A 3K7, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amit X.","family":"Garg","sequence":"additional","affiliation":[{"name":"ICES, London, ON N6A 3K7, Canada"},{"name":"Department of Medicine, Epidemiology, and Biostatistics, Western University, London, ON N6A 3K7, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric","family":"McArthur","sequence":"additional","affiliation":[{"name":"ICES, London, ON N6A 3K7, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,25]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"An Application of Machine Learning to Haematological Diagnosis","volume":"8","author":"Kukar","year":"2018","journal-title":"Sci. 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