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Challenges associated with missing data in electronic health records: A case study of a risk prediction model for diabetes using data from Slovenian primary care.

G. Stiglic, P. Kocbek, N. Fijacko, A. Sheikh, and M. Pajnkihar. Health Informatics J., (2019)

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Evaluation of Mobile Phone Mortality Risk Score Applications Using Data from the Electronic Medical Records.N. Fijacko, L. Gosak, P. Kocbek, L. Cilar, A. Markota, and G. Stiglic. MIE, volume 270 of Studies in Health Technology and Informatics, page 1273-1274. IOS Press, (2020)Interpretability of machine learning-based prediction models in healthcare.G. Stiglic, P. Kocbek, N. Fijacko, M. Zitnik, K. Verbert, and L. Cilar. WIREs Data Mining Knowl. Discov., (2020)Improving Primary Healthcare Workflow Using Extreme Summarization of Scientific Literature Based on Generative AI.G. Stiglic, L. Kopitar, L. Gosak, P. Kocbek, Z. He, P. Chakraborty, P. Meyer, and J. Bian. CoRR, (2023)Generating Extremely Short Summaries from the Scientific Literature to Support Decisions in Primary Healthcare: A Human Evaluation Study.P. Kocbek, L. Gosak, K. Musovic, and G. Stiglic. AIME, volume 13263 of Lecture Notes in Computer Science, page 373-382. Springer, (2022)Using (Automated) Machine Learning and Drug Prescription Records to Predict Mortality and Polypharmacy in Older Type 2 Diabetes Mellitus Patients.S. Kocbek, P. Kocbek, T. Zupanic, G. Stiglic, and B. Gabrys. ICONIP (4), volume 1142 of Communications in Computer and Information Science, page 624-632. Springer, (2019)Challenges associated with missing data in electronic health records: A case study of a risk prediction model for diabetes using data from Slovenian primary care.G. Stiglic, P. Kocbek, N. Fijacko, A. Sheikh, and M. Pajnkihar. Health Informatics J., (2019)A Review of Mortality Risk Prediction Models in Smartphone Applications.N. Fijacko, R. Creber, L. Gosak, P. Kocbek, L. Cilar, P. Creber, and G. Stiglic. J. Medical Syst., 45 (12): 107 (2021)Local Interpretability of Calibrated Prediction Models: A Case of Type 2 Diabetes Mellitus Screening Test.S. Kocbek, P. Kocbek, L. Cilar, and G. Stiglic. CoRR, (2020)Local vs. Global Interpretability of Machine Learning Models in Type 2 Diabetes Mellitus Screening.L. Kopitar, L. Cilar, P. Kocbek, and G. Stiglic. KR4HC/ProHealth/TEAAM@AIME, volume 11979 of Lecture Notes in Computer Science, page 108-119. Springer, (2019)Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Data.P. Kocbek, N. Fijacko, C. Soguero-Ruíz, K. Mikalsen, U. Maver, P. Brzan, A. Stozer, R. Jenssen, S. Skrøvseth, and G. Stiglic. Comput. Math. Methods Medicine, (2019)

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