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Background. The aim of the study was to estimate the direct medical costs of metastatic colorectal cancer (mCRC) treated at the Institute of Oncology Ljubljana and to question the healthcare payment system in Slovenia. Methods. Using an internal patient database, the costs of mCRC patients were estimated in 2009 by examining (1) mCRC direct medical related costs, and (2) the cost difference between payment received by Slovenian health insurance and actual mCRC costs. Costs were analysed in the treatment phase of the disease by assessing the direct medical costs of hospital treatment with systemic therapy together with hospital treatment of side effects, without assessing radiotherapy or surgical treatment. Follow-up costs, indirect medical costs, and nonmedical costs were not included. Results. A total of 209 mCRC patients met all eligibility criteria. The direct medical costs of mCRC hospitalization with systemic therapy in Slovenia for 2009 were estimated as the cost of medications (cost of systemic therapy + cost of drugs for premedication) + labor cost (the cost of carrying out systemic treatment) + cost of lab tests + cost of imaging tests + KRAS testing cost + cost of hospital treatment due to side effects of mCRC treatment, and amounted to €3,914,697. The difference between the cost paid by health insurance and actual costs, estimated as direct medical costs of hospitalization of mCRC patients treated with systemic therapy at the Institute of Oncology Ljubljana in 2009, was €1,900,757.80. Conclusions. The costs paid to the Institute of Oncology Ljubljana by health insurance for treating mCRC with systemic therapy do not match the actual cost of treatment. In fact, the difference between the payment and the actual cost estimated as direct medical costs of hospitalization of mCRC patients treated with systemic therapy at the Institute of Oncology Ljubljana in 2009 was €1,900,757.80. The model Australian Refined Diagnosis Related Groups (AR-DRG) for cost assessment in oncology being currently used is probably one of the reasons for the discrepancy between pay-outs and actual costs. We propose new method for more precise cost assessment in oncology. [ABSTRACT FROM AUTHOR]

We address the problem of estimating the time-to-employment of a jobseeker using survival analysis and oblique predictive clustering tree. Unlike standard survival analysis, oblique predictive clustering tree can handle categorical and continuous data and is capable of modelling non-linear dependences. Treating the censored data as missing data opens the possibility to perform survival analysis by using structured output prediction in semi-supervised multi-target regression setting. The effectiveness of this approach is shown on a real dataset from Public Employment Services in Slovenia, comprising time-to-employment records with jobseekers' personal and professional characteristics. The performances are compared with six state-of-the-art AI methods. To the best of our knowledge, this is the first example of using semi-supervised oblique predictive clustering tree for survival analysis. • Estimation of time-to-employment of job seekers through structured output prediction. • The approach is based on Oblique Predictive Clustering Trees (SPYCTs). • Interpretation of the model predictions through the SHAP method. • Validation of the model performed on more than 1000 samples of job seekers data. • The model supports job seekers in finding optimal career solutions. [ABSTRACT FROM AUTHOR]