Prediction of future hospital admissions - what is the tradeoff between specificity and accuracy?

Ieva Vasiljeva, Ognjen Arandelovic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


Large amounts of electronic medical records collected by hospitals across the developed world offer unprecedented possibilities for knowledge discovery using computer based data mining and machine learning. Notwithstanding significant research efforts, the use of this data in the prediction of disease development has largely been disappointing. In this paper we examine in detail a recently proposed method which has in preliminary experiments demonstrated highly promising results on real-world data. We scrutinize the authors’ claims that the proposed model is scalable and investigate whether the tradeoff between prediction specificity (i.e. the ability of the model to predict a wide number of different ailments) and accuracy (i.e. the ability of the model to make the correct prediction) is practically viable. Our experiments conducted on a data corpus of nearly 3,000,000 admissions support the authors’ expectations and demonstrate that the high prediction accuracy is maintained well even when the number of admission types explicitly included in the model is increased to account for 98% of all admissions in the corpus. Thus several promising directions for future work are highlighted.
Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Bioinformatics and Computational Biology, BICOB 2016
Subtitle of host publicationLas Vegas; United States; 4 April 2016 through 6 April 2016
EditorsN Haspel, T Ioerger
PublisherInternational Society for Computers and Their Applications
ISBN (Electronic)9781943436033
ISBN (Print)9781510822511
Publication statusPublished - Apr 2016
EventBICoB-2016 - Las Vegas, Nevada, United States
Duration: 4 Apr 20166 Apr 2016


Country/TerritoryUnited States
CityLas Vegas, Nevada
Internet address


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