FOCUSED-Short-Term Wind Speed Forecast Correction Algorithm Based on Successive NWP Forecasts for Use in Traffic Control Decision Support Systems.

In this paper, we propose a new algorithm, called FOCUSED (FOrecast Correction Using Successive prEDictions), for forecast correction of short-term wind speed predictions. We developed FOCUSED with the aim of improving the forecast of bora gusts, which frequently result in high-speed wind situations dangerous for traffic. The motivation arises from occasionally ambiguous results of the currently deployed decision support system, which aids traffic management in strong and gusty wind conditions at the coast of Croatia. The proposed correction algorithm uses characteristics of numerical weather prediction models to iteratively forecast the wind speed multiple times for the same future window. We use these iterative predictions as input features of the FOCUSED algorithm and get the corrected predictions as the output. We compared the proposed algorithm with artificial neural networks, random forests, support vector machines, and linear regression to demonstrate the superiority of the algorithm's performance on a data set comprising five years of real data measurements at the Croatian bridge "Krk" and complementary historical forecasts by ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) numerical weather prediction model.

Decision Support for Medication Change of Parkinson's Disease Patients.

BACKGROUND AND OBJECTIVE: Parkinson's disease (PD) is a degenerative disorder of the central nervous system for which currently there is no cure. Its treatment requires long-term, interdisciplinary disease management, and usage of typical medications, including levodopa, dopamine agonists, and enzymes, such as MAO-B inhibitors. The key goal of disease management is to prolong patients' independence and keep their quality of life. Due to the different combinations of motor and non-motor symptoms from which PD patients suffer, in addition to existing comorbidities, the change of medications and their combinations is difficult and patient-specific. To help physicians, we developed two decision support models for PD management, which suggest how to change the medication treatment. METHODS: The models were developed using DEX methodology, which integrates the qualitative multi-criteria decision modelling with rule-based expert systems. The two DEX models differ in the way the decision rules were defined. In the first model, the decision rules are based on the interviews with neurologists (DEX expert model), and in the second model, they are formed from a database of past medication change decisions (DEX data model). We assessed both models on the Parkinson's Progression Markers Initiative (PPMI) and on a questionnaire answered by 17 neurologists from 4 European countries using accuracy measure and the Jaccard index. RESULTS: Both models include 15 sub-models that address possible medication treatment changes based on the given patients' current state. In particular, the models incorporate current state changes in patients' motor symptoms (dyskinesia intensity, dyskinesia duration, OFF duration), mental problems (impulsivity, cognition, hallucinations and paranoia), epidemiologic data (patient's age, activity level) and comorbidities (cardiovascular problems, hypertension and low blood pressure). The highest accuracy of the developed sub-models for 15 medication treatment changes ranges from 69.31 to 99.06 %. CONCLUSIONS: Results show that the DEX expert model is superior to the DEX data model. The results indicate that the constructed models are sufficiently adequate and thus fit for the purpose of making "second-opinion" suggestions to decision support users.

Algebraic Topology of Multi-Brain Connectivity Networks Reveals Dissimilarity in Functional Patterns during Spoken Communications.

Human behaviour in various circumstances mirrors the corresponding brain connectivity patterns, which are suitably represented by functional brain networks. While the objective analysis of these networks by graph theory tools deepened our understanding of brain functions, the multi-brain structures and connections underlying human social behaviour remain largely unexplored. In this study, we analyse the aggregate graph that maps coordination of EEG signals previously recorded during spoken communications in two groups of six listeners and two speakers. Applying an innovative approach based on the algebraic topology of graphs, we analyse higher-order topological complexes consisting of mutually interwoven cliques of a high order to which the identified functional connections organise. Our results reveal that the topological quantifiers provide new suitable measures for differences in the brain activity patterns and inter-brain synchronisation between speakers and listeners. Moreover, the higher topological complexity correlates with the listener's concentration to the story, confirmed by self-rating, and closeness to the speaker's brain activity pattern, which is measured by network-to-network distance. The connectivity structures of the frontal and parietal lobe consistently constitute distinct clusters, which extend across the listener's group. Formally, the topology quantifiers of the multi-brain communities exceed the sum of those of the participating individuals and also reflect the listener's rated attributes of the speaker and the narrated subject. In the broader context, the presented study exposes the relevance of higher topological structures (besides standard graph measures) for characterising functional brain networks under different stimuli.

The cost of systemic therapy for metastatic colorectal carcinoma in Slovenia: discrepancy analysis between cost and reimbursement.

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.

Quantifying the consistency of scientific databases.

Science is a social process with far-reaching impact on our modern society. In recent years, for the first time we are able to scientifically study the science itself. This is enabled by massive amounts of data on scientific publications that is increasingly becoming available. The data is contained in several databases such as Web of Science or PubMed, maintained by various public and private entities. Unfortunately, these databases are not always consistent, which considerably hinders this study. Relying on the powerful framework of complex networks, we conduct a systematic analysis of the consistency among six major scientific databases. We found that identifying a single "best" database is far from easy. Nevertheless, our results indicate appreciable differences in mutual consistency of different databases, which we interpret as recipes for future bibliometric studies.

Abdominal surgery process modeling framework for simulation using spreadsheets.

We provide a continuation of the existing Activity Table Modeling methodology with a modular spreadsheets simulation. The simulation model developed is comprised of 28 modeling elements for the abdominal surgery cycle process. The simulation of a two-week patient flow in an abdominal clinic with 75 beds demonstrates the applicability of the methodology. The simulation does not include macros, thus programming experience is not essential for replication or upgrading the model. Unlike the existing methods, the proposed solution employs a modular approach for modeling the activities that ensures better readability, the possibility of easily upgrading the model with other activities, and its easy extension and connectives with other similar models. We propose a first-in-first-served approach for simulation of servicing multiple patients. The uncertain time duration of the activities is modeled using the function "rand()". The patients movements from one activity to the next one is tracked with nested "if()" functions, thus allowing easy re-creation of the process without the need of complex programming.