Background: Headaches have not only medical but also great socioeconomic significance, therefore, it is necessary to evaluate the overall impact of headaches on a patient’s life, including their work and work efficiency. The aim of this study was to determine the impact of individual headache types on work and work efficiency. Methods: This research was designed as a cross-sectional study performed by administering a questionnaire among employees. The questionnaire consisted of general questions, questions about headache features, and questions about the impact of headaches on work. Results: Monthly absence from work was mostly represented by migraine sufferers (7.1%), significantly more than with sufferers with tension-type headaches (2.23%; p = 0.019) and other headache types (2.15%; p = 0.025). Migraine sufferers (30.2%) worked in spite of a headache for more than 25 h, which was more frequent than with sufferers from tension-type and other-type headaches (13.4%). On average, headache sufferers reported work efficiency ranging from 66% to 90%. With regard to individual headache types, this range was significantly more frequent in subjects with tension-type headaches, whereas 91–100% efficiency was significantly more frequent in subjects with other headache types. Lower efficiency, i.e., 0–40% and 41–65%, was significantly more frequent with migraine sufferers. Conclusions: Headaches, especially migraines, significantly affect the work and work efficiency of headache sufferers by reducing their productivity. Loss is greater due to reduced efficiency than due to absenteeism.

Clustering is one of the most fundamental and essential data analysis tasks with broad applications. It has been studied in various research fields: data mining, machine learning, pattern recognition and in engineering, economics and biomedical data analysis. Headache is not a disease that typically shortens one’s life, but it can be a serious social as well as a health problem. Approximately 27 billion euros per year are lost through reduced work productivity in the European community. This paper is focused on a new strategy based on a hybrid model for combining fuzzy partition method and maximum likelihood estimation clustering algorithm for diagnosing primary headache disorder. The proposed hybrid system is tested on two data sets for diagnosing headache disorder collected from Clinical Centre of Vojvodina in Serbia.

(1) Background: Modern medicine generates a great deal of information that stored in medical databases. Simultaneously, extracting useful knowledge and making scientific decisions for diagnosis and treatment of diseases becomes increasingly necessary. Headache disorders are the most prevalent of all the neurological conditions. Headaches have not only medical but also great socioeconomic significance. The aim of this research is to develop an intelligent system for diagnosing primary headache disorders. (2) Methods: This research applied various mathematical, statistical and artificial intelligence techniques, among which the most important are: Calinski-Harabasz index, Analytical Hierarchy Process, and Weighted Fuzzy C-means Clustering Algorithm. These methods, techniques and methodologies are used to create a hybrid intelligent system for diagnosing primary headache disorders. The proposed intelligent diagnostic system is tested with original real-world data set with different metrics. (3) Results: First at all, nine of 20 attributes – features from International Headache Society (IHS) criteria are selected, and then only five most important attributes from IHS criteria are selected. The calculation result based on the Calinski–Harabasz index value (178) for the optimal number of clusters is three, and they present three classes of headaches: (i) migraine, (ii) tension-type headaches (TTHs), and (iii) other primary headaches (OPHs). The proposed hybrid intelligent system shows the following quality metrics: Accuracy 75%; Precision 67% for migraine, 74% for TTHs, 86% for OPHs, and Average Precision 77%; Recall 86% for migraine, 73% for TTHs, 67% for OPHs, Average Recall 75%; F₁ score 75% for migraine, 74% for TTHs, 75% for OPHs, and Average F₁ score 75%. (4) Conclusions: The hybrid intelligent system presents qualitative and respectable experimental results. The implementation of existing diagnostics systems and the development of new diagnostics systems in medicine is necessary in order to help physicians make quality diagnosis and decide the best treatments for the patients.

Background: Multiple sclerosis (MS) is one of the most common demyelinating diseases of the central nervous system. We aimed to investigate serum and cerebrospinal fluid levels of different laboratory inflammatory biomarkers in patients with MS. Methods: A total of 120 subjects participated in the study, 60 of whom were diagnosed with MS, 30 with the final diagnosis of non-inflammatory diseases of the central nervous system (CNS), and 30 healthy subjects representing the control group. Regarding the progression of radiological findings after 2 years from the initial diagnosis, the MS group was divided into stationary radiological findings (n=30) and radiologically proven disease progression (n=30). In all patients, we analyzed levels of laboratory inflammatory biomarkers: C reactive protein (CRP), Neutrophil-to-lymphocyte ratio (NLR), Growth differentiation factor 15 (GDF15) in serum samples, and neurofilaments (NFs) in cerebrospinal fluid (CSF). NFs and GDF15 were analyzed initially, while CRP and NLR values were analyzed initially and after two years. Results: We found statistically lower GDF15 values and initial CRP values in the MS group regarding the group with non-inflammatory diseases of the CNS (p<0.0001). On the other side, we determined a significant elevation of laboratory markers CRP and NLR, initially and after a two-year period, in the MS subgroup with the progression of magnetic resonance imaging (MRI) findings (p<0.0001 and p=0.050, respectively). Also, we found a positive correlation between CRP and NFs (r=0.243, p=0.04), as well as a positive correlation between CRP and GDF15 in patients with MS (r=0.769, p<0.0001). Conclusions: We found a significant elevation of laboratory markers of systemic inflammation, CRP, and NLR in MS patients who developed disease progression based on MRI findings. There is a need for further studies to validate current parameters to be considered as useful markers of MS activity and disability.

Background and objectives: Non-motor symptoms in the form of increased sensitivity are often associated with the onset of idiopathic Bell’s palsy (IBP). The aims were to determine whether the pain threshold in the retroauricular regions (RAR) in IBP patients and the time of its occurrence is related to IBP severity. Materials and Methods: The study was conducted among 220 respondents (142 IBP patients, 78 healthy subjects (HS)). The degree of IBP was graded using the House–Brackmann and Sunnybrook Grading Scales (II—mild dysfunction, VI—total paralysis), whereas the pain thresholds were measured using the digital pressure algometer. Results: We found no difference in the degree of the pain threshold between the right and left RAR in the HS group. IBP patients belonging to groups II, III, IV, and V had lower pain thresholds in both RARs than HS and IBP patients belonging to group VI. There was no difference in the degree of pain threshold in RAR between the affected and unaffected side in IBP patients. The incidence of retroauricular pain that precedes paralysis and ceases after its occurrence in groups II and III of IBP patients is noticeably lower and the incidence of retroauricular pain that occurred only after the onset of paralysis is more frequent. Also, we found that the incidence of retroauricular pain that precedes paralysis and ceases after its occurrence in groups V and VI of IBP patients was more frequent. Conclusions: The degree of pain threshold lowering in RAR (bilaterally) is inversely related to the severity of IBP. We suggest that the occurrence of retroauricular pain before the onset of facial weakness is associated with higher severity of IBP while the occurrence after the onset is associated with lower severity of IBP.

Demand for healthcare is increasing due to a growing and ageing population. Choosing an adequate schedule for medical staff can be a difficult dilemma for managers. The goal of nurse scheduling is to minimize the cost of the staff while maximizing their preferences and the overall benefits for the unit. This paper is focused on a new hybrid strategy based on detecting the optimal solution in nurse scheduling problem. The new proposed hybrid approach is obtained by combining case-based reasoning and general linear empirical model with arbitrary coefficients. The model is tested with original real-world data set obtained from the Oncology Institute of Vojvodina in Serbia.