Organisationer i förändring, vad händer då?

Precis som vår värld är även organisationer i ständig för-ändring. Förändring kan vara frivillig och den kan också vara tvingande. Men vad händer med organisationerna som hamnar i dessa situationer? Syftet med den här uppsatsen är att analysera hur olika delar av en organisation kan påverkas av en förändring

Sentence based risk classifier using NLP and machine learning

This project was inspired by the company Dizparc and has a focus onclassification systems together with certain applications of natural languageprocessing. Classification systems are a very extensively researched areadating back to the latter half of the 1900s with multiple different ways of theproblems presented up until its more modern takes in today’s age. There aremany approaches to classification systems with applications of naturallanguage processing, some already existing ones are the combination ofword vectorization methods together with various algorithms such asWord2Vec merged with Transformers or Convolution Neural Networks.Most of the classification systems with applications of natural languageprocessing usually reside within medical research, and therefore access todata is strictly limited. This project was designed to classify inputs using themachine learning algorithms Multinomial Logistic Regression, DecisionTree, and Random Forest, and to compare the models to see which of themwould yield the best results. These results were tested based on the overallaccuracy, and difference in lowest and highest accuracy. Confusion matriceswere also used to check which classes were the easiest to predict. Thatshowed a better result for Random Forest when using certain numbers ofclasses, while Decision Tree was able to reach similar results when usingfewer classes. The quantity and quality of data accumulated may not servesufficient to correctly classify inputs through certain methods

Sentence based risk classifier using NLP and machine learning

This project was inspired by the company Dizparc and has a focus onclassification systems together with certain applications of natural languageprocessing. Classification systems are a very extensively researched areadating back to the latter half of the 1900s with multiple different ways of theproblems presented up until its more modern takes in today’s age. There aremany approaches to classification systems with applications of naturallanguage processing, some already existing ones are the combination ofword vectorization methods together with various algorithms such asWord2Vec merged with Transformers or Convolution Neural Networks.Most of the classification systems with applications of natural languageprocessing usually reside within medical research, and therefore access todata is strictly limited. This project was designed to classify inputs using themachine learning algorithms Multinomial Logistic Regression, DecisionTree, and Random Forest, and to compare the models to see which of themwould yield the best results. These results were tested based on the overallaccuracy, and difference in lowest and highest accuracy. Confusion matriceswere also used to check which classes were the easiest to predict. Thatshowed a better result for Random Forest when using certain numbers ofclasses, while Decision Tree was able to reach similar results when usingfewer classes. The quantity and quality of data accumulated may not servesufficient to correctly classify inputs through certain methods

2021 ESC Guidelines on cardiac pacing and cardiac resynchronization therapy

Pacing is an important part of electrophysiology and of cardiology in general. Whereas some of the situations requiring pacing are clear and have not changed over the years, many others have evolved and have been the subject of extensive recent research, such as pacing after syncope (section 5), pacing following transcatheter aortic valve implantation (TAVI; section 8), cardiac resynchronization therapy (CRT) for heart failure (HF) and for prevention of pacing-induced cardiomyopathy (section 6), and pacing in various infiltrative and inflammatory diseases of the heart, as well as in different cardiomyopathies (section 8). Other novel topics include new diagnostic tools for decision-making on pacing (section 4), as well as a whole new area of pacing the His bundle and the left bundle branch (section 7). In addition, attention has increased in other areas, such as how to systematically minimize procedural risk and avoid complications of cardiac pacing (section 9), how to manage patients with pacemakers in special situations, such as when magnetic resonance imaging (MRI) or irradiation are needed (section 11), how to follow patients with a pacemaker with emphasis on the use of remote monitoring, and how to include shared decision-making in caring for this patient population (section 12). The last pacing guidelines of the European Society of Cardiology (ESC) were published in 2013; therefore, a new set of guidelines was felt to be timely and necessary