Strojno učenje putem regresije i SVM

Prvo poglavlje ovoga rada uvodi pojam strojnoga učenja, kao i njegovu povijest i primjenu. Sljedeća dva poglavlja uvode regresiju i klasifikaciju te algoritme linearne regresije i logističke regresije koji služe kao mjerilo usporedbe algoritmu potpornih vektora, koji je glavna tematika ovoga rada. Naposljetku slijedi usporedba metode potpornih vektora sa spomenutim metodama logističke regresije i linearne regresije na konkretnom klasifikacijskom i regresijskom problemu.The first chapter of this work introduces the concept of machine learning, as well as its history and applications. The next two chapters focus on the concept of regression and classification as well as the appropriate algorithms, linear regression and logistic regression. The aforementioned algorithms are used as a baseline for the main algorithm of this work: the support vector machine algorithm which is talked about in detail in the next chapter. In conclusion, the methods are compared on a concrete regression and classification problem

Strojno učenje putem regresije i SVM

Prvo poglavlje ovoga rada uvodi pojam strojnoga učenja, kao i njegovu povijest i primjenu. Sljedeća dva poglavlja uvode regresiju i klasifikaciju te algoritme linearne regresije i logističke regresije koji služe kao mjerilo usporedbe algoritmu potpornih vektora, koji je glavna tematika ovoga rada. Naposljetku slijedi usporedba metode potpornih vektora sa spomenutim metodama logističke regresije i linearne regresije na konkretnom klasifikacijskom i regresijskom problemu.The first chapter of this work introduces the concept of machine learning, as well as its history and applications. The next two chapters focus on the concept of regression and classification as well as the appropriate algorithms, linear regression and logistic regression. The aforementioned algorithms are used as a baseline for the main algorithm of this work: the support vector machine algorithm which is talked about in detail in the next chapter. In conclusion, the methods are compared on a concrete regression and classification problem

Strojno učenje putem regresije i SVM

Prvo poglavlje ovoga rada uvodi pojam strojnoga učenja, kao i njegovu povijest i primjenu. Sljedeća dva poglavlja uvode regresiju i klasifikaciju te algoritme linearne regresije i logističke regresije koji služe kao mjerilo usporedbe algoritmu potpornih vektora, koji je glavna tematika ovoga rada. Naposljetku slijedi usporedba metode potpornih vektora sa spomenutim metodama logističke regresije i linearne regresije na konkretnom klasifikacijskom i regresijskom problemu.The first chapter of this work introduces the concept of machine learning, as well as its history and applications. The next two chapters focus on the concept of regression and classification as well as the appropriate algorithms, linear regression and logistic regression. The aforementioned algorithms are used as a baseline for the main algorithm of this work: the support vector machine algorithm which is talked about in detail in the next chapter. In conclusion, the methods are compared on a concrete regression and classification problem

On the Comparison of Classic and Deep Keypoint Detector and Descriptor Methods

The purpose of this study is to give a performance comparison between several classic hand-crafted and deep key-point detector and descriptor methods. In particular, we consider the following classical algorithms: SIFT, SURF, ORB, FAST, BRISK, MSER, HARRIS, KAZE, AKAZE, AGAST, GFTT, FREAK, BRIEF and RootSIFT, where a subset of all combinations is paired into detector-descriptor pipelines. Additionally, we analyze the performance of two recent and perspective deep detector-descriptor models, LF-Net and SuperPoint. Our benchmark relies on the HPSequences dataset that provides real and diverse images under various geometric and illumination changes. We analyze the performance on three evaluation tasks: keypoint verification, image matching and keypoint retrieval. The results show that certain classic and deep approaches are still comparable, with some classic detector-descriptor combinations overperforming pretrained deep models. In terms of the execution times of tested implementations, SuperPoint model is the fastest, followed by ORB. The source code is published on \url{https://github.com/kristijanbartol/keypoint-algorithms-benchmark}

Survey of self-assessed preparedness for clinical practice in one Croatian medical school

<p>Abstract</p> <p>Background</p> <p>The Croatian higher education system is in the process of reforming its medical curricula to comply with European Union standards. We conducted a survey of students enrolled at the University of Zagreb (Croatia) asking them to rate their perception of preparedness for clinical practice prior to initiation of the reform process. The purpose of the survey was to identify self-perceived deficiencies in education and to establish a reference point for the later assessment of ongoing educational reform.</p> <p>Findings</p> <p>One-hundred and forty seven (N = 147) graduates reported the levels of perceived preparedness on 30 items grouped into 8 educational domains. Main domains were: understanding science, practical skills/patient management, holistic care, prevention, interpersonal skills, confidence/coping skills, collaboration, and self-directed learning. For each item, graduates self assessed their preparedness on a scale ranging from 1 to 4, with 1 = "Very inadequate", 2 = "Somewhat inadequate", 3 = "Somewhat adequate", and 4 = "Very adequate". In 7 out of 8 domains the achieved median score was ≥ 3. Students expressed low confidence (defined when ≥ 25% of respondents supplied a rating for the survey question as: "very inadequate" or "somewhat inadequate") with interpersonal skills (discussing terminal disease, counseling distraught patients, balancing professional and personal life), and in performing certain basic semi-invasive or invasive procedures.</p> <p>Conclusion</p> <p>Zagreb medical graduates identified several deficiencies within educational domains required for standard clinical practice. Ongoing educational efforts need to be directed towards the correction of these deficiencies in order to achieve standards required by the European Union.</p

Temporal trends in primary care-recorded self-harm during and beyond the first year of the COVID-19 pandemic: Time series analysis of electronic healthcare records for 2.8 million patients in the Greater Manchester Care Record

Summary: Background: Surveillance of temporal trends in clinically treated self-harm is an important component of suicide prevention in the dynamic context of COVID-19. There is little evidence beyond the initial months following the onset of the pandemic, despite national and regional restrictions persisting to mid-2021. Methods: Descriptive time series analysis utilizing de-identified, primary care health records of 2.8 million patients from the Greater Manchester Care Record. Frequencies of self-harm episodes between 1st January 2019 and 31st May 2021 were examined, including stratification by sex, age group, ethnicity, and index of multiple deprivation quintile. Findings: There were 33,444 episodes of self-harm by 13,148 individuals recorded during the study period. Frequency ratios of incident and all episodes of self-harm were 0.59 (95% CI 0.51 to 0.69) and 0.69 (CI 0.63 to 0.75) respectively in April 2020 compared to February 2020. Between August 2020 and May 2021 frequency ratios were 0.92 (CI 0.88 to 0.96) for incident episodes and 0.86 (CI 0.84 to 0.88) for all episodes compared to the same months in 2019. Reductions were largest among men and people living in the most deprived neighbourhoods, while an increase in all-episode self-harm was observed for adolescents aged 10–17. Interpretation: Reductions in primary care-recorded self-harm persisted to May 2021, though they were less marked than in April 2020 during the first national lockdown. The observed reductions could represent longer term reluctance to seek help from health services. Our findings have implications for the ability for services to offer recommended care for patients who have harmed themselves

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING: British Heart Foundation Data Science Centre, led by Health Data Research UK

Three classical problems in mathematics

U ovom radu ćemo detaljno analizirati tri klasična problema u matematici te njihovu nerje šivost koristeći isključivo jednobridno ravnalo i šestar. Rad je podijeljen na tri dijela za svaki od tri klasična problema: duplikacija kocke, trisekcija kuta i kvadratura kruga. Svako poglavlje se dijeli na nova tri dijela: kratki uvod u problem, pokušaji rješavanja tih problema te razlog nerješivosti. Na kraju rada nalazi se poglavlje o origamiju i njegovoj primjeni na dva od tri problema kao zanimljivost