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  • 11.
    Karlsson, Isak
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Zhao, Jing
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Asker, Lars
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Boström, Henrik
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Predicting Adverse Drug Events by Analyzing Electronic Patient Records2013Ingår i: Artificial Intelligence in Medicine: 14th Conference on Artificial Intelligence in Medicine, AIME 2013. Proceedings / [ed] Niels Peek, Roque Marín Morales, Mor Peleg, Springer Berlin/Heidelberg, 2013, Vol. 7885, 125-129 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    Diagnosis codes for adverse drug events (ADEs) are sometimes missing from electronic patient records (EPRs). This may not only affect patient safety in the worst case, but also the number of reported ADEs, resulting in incorrect risk estimates of prescribed drugs. Large databases of electronic patient records (EPRs) are potentially valuable sources of information to support the identification of ADEs. This study investigates the use of machine learning for predicting one specific ADE based on information extracted from EPRs, including age, gender, diagnoses and drugs. Several predictive models are developed and evaluated using different learning algorithms and feature sets. The highest observed AUC is 0.87, obtained by the random forest algorithm. The resulting model can be used for screening EPRs that are not, but possibly should be, assigned a diagnosis code for the ADE under consideration. Preliminary results from using the model are presented.

  • 12. Kotsifakos, Alexios
    et al.
    Karlsson, Isak
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Papapetrou, Panagiotis
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Athitsos, Vassilis
    Gunopulos, Dimitrios
    Embedding-based subsequence matching with gaps-range-tolerances: a Query-By-Humming application2015Ingår i: The VLDB journal, ISSN 1066-8888, E-ISSN 0949-877X, Vol. 24, nr 4, 519-536 s.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We present a subsequence matching framework that allows for gaps in both query and target sequences, employs variable matching tolerance efficiently tuned for each query and target sequence, and constrains the maximum matching range. Using this framework, a dynamic programming method is proposed, called SMBGT, that, given a short query sequence Q and a large database, identifies in quadratic time the subsequence of the database that best matches Q. SMBGT is highly applicable to music retrieval. However, in Query-By-Humming applications, runtime is critical. Hence, we propose a novel embedding-based approach, called ISMBGT, for speeding up search under SMBGT. Using a set of reference sequences, ISMBGT maps both Q and each position of each database sequence into vectors. The database vectors closest to the query vector are identified, and SMBGT is then applied between Q and the subsequences that correspond to those database vectors. The key novelties of ISMBGT are that it does not require training, it is query sensitive, and it exploits the flexibility of SMBGT. We present an extensive experimental evaluation using synthetic and hummed queries on a large music database. Our findings show that ISMBGT can achieve speedups of up to an order of magnitude against brute-force search and over an order of magnitude against cDTW, while maintaining a retrieval accuracy very close to that of brute-force search.

  • 13.
    Zhao, Jing
    et al.
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Karlsson, Isak
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Asker, Lars
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Boström, Henrik
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Applying Methods for Signal Detection in Spontaneous Reports to Electronic Patient Records2013Ingår i: Proceedings of the  19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery (ACM), 2013Konferensbidrag (Refereegranskat)
    Abstract [en]

    Currently, pharmacovigilance relies mainly on disproportionality analysis of spontaneous reports. However, the analysis of spontaneous reports is concerned with several problems, such as reliability, under-reporting and insucient patient information. Longitudinal healthcare data, such as Electronic Patient Records (EPRs) in which comprehensive information of each patient is covered, is a complementary source of information to detect Adverse Drug Events (ADEs). A wide set of disproportionality methods has been developed for analyzing spontaneous reports to assess the risk of reported events being ADEs. This study aims to investigate the use of such methods for detecting ADEs when analyzing EPRs. The data used in this study was extracted from Stockholm EPR Corpus. Four disproportionality methods (proportional reporting rate, reporting odds ratio, Bayesian condence propagation neural network, and Gamma-Poisson shrinker) were applied in two dierent ways to analyze EPRs: creating pseudo spontaneous reports based on all observed drug-event pairs (event-level analysis) or analyzing distinct patients who experienced a drug-event pair (patient-level analysis). The methods were evaluated in a case study on safety surveillance of Celecoxib. The results showed that, among the top 200 signals, more ADEs were detected by the event-level analysis than by the patient-level analysis. Moreover, the event-level analysis also resulted in a higher mean average precision. The main conclusion of this study is that the way in which the disproportionality analysis is applied, the event-level or patient-level analysis, can have a much higher impact on the performance than which disproportionality method is employed.

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