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BackgroundClinical characteristics and long-term outcomes of patients with myocardial infarction with non-obstructive coronary arteries (MINOCA) and cancer are insufficiently elucidated.ObjectivesWe sought to characterize these patients hospitalized in a tertiary cardio-oncology center and to find the potential determinants affecting their long-term mortality.MethodsMINOCA was diagnosed in 72 of the 1,011 patients with consecutive myocardial infarction who underwent coronary angiography. Mortality rates and their determinants were analyzed within a median follow-up of 69.2 (37.8–79.9) months.ResultsActive cancer was identified in 21 (29.2%) of patients with MINOCA and in 113 (12.0%) patients with myocardial infarction and obstructive coronary artery disease (MI-CAD) (p < 0.001). MINOCA patients with cancer were characterized by a higher incidence of anemia (47.6 vs. 21.6%, p = 0.03) and more frequently Takotsubo syndrome (19.1 vs. 2.0%, p = 0.01) than in non-cancer MINOCA. The troponin T/hemoglobin ratio was higher in both cancer MINOCA and MI-CAD groups when compared with their respective non-cancer patients (both p < 0.05). The age and sex-standardized mortality rates were significantly higher in cancer MINOCA (26.7%/year) when compared with non-cancer MINOCA (2.3%/year, p = 0.002) and in cancer MI-CAD (25.0%/year) vs. non-cancer MI-CAD (3.7%/year, p < 0.001). Active cancer (HR 3.12, 95% CI 2.41–4.04) was independently associated with higher long-term mortality, while higher hemoglobin levels (HR 0.93, 95% CI 0.88–0.99, per g/dl) and a MINOCA diagnosis (HR 0.69, 95% CI 0.47–0.97) improved long-term survival.ConclusionPatients with MINOCA were comorbid with cancer more frequently than MI-CAD. In turn, an active malignancy was associated with an unfavorable long-term survival both in MI-CAD population and in patients with MINOCA.

Purpose–The purpose of this thesis is to investigate the game reviews and to see how the natural language processing can be used to analyze game reviews. Using Steam platform as our data source and Latent Dirichlet Allocation as one of the main methods of data analysis in this research there will be attempt to figure out what kind of topics will emerge within and how interpretations of these can be used to see the correlation between game updates and game reviews. All these results will be looked throughout the timeframes of the life of the game, from alpha/beta testing to the most current reviews. Design/Methodology/Approach–Within this research Latent Dirichlet Allocation will be used on five different games from multiple different genres to test our hypothesis. Findings–The findings of this research could be used by the fields of both data analysis and game studies as this research contributes towards these areas. It is believed that this research will show how the game reviews can be interpreted and give ideas to further research. Practical implications–In theory this research will heavily be based on opinions of both players/people who left the reviews and a researcher hence these should not be treated as a fact. As there are many factors that could affect the quality of the reviews, there will be taken careful measures to eliminate these one by one (such as length of the reviews, duplicates. Etc.)Originality/value–This research should stimulate the game producers and developers to see a different aspect of the games and allow for better decision making. Key Questions–How natural language processing can be used to analyze this data? How will LDA and LDA Sequential contribute to analysis of Steam reviews? How does pre-processing change the results of the LDA and LDA Sequential? What is the correlation between the game updates and game review analysis results?