Academic literature on the topic 'Riskwork'

Objective: To assess the risk of different cancer sites among the male smokersof the Southern Punjab, Pakistan. Study Design: Case-control design. Period: March - July2012. Setting: A data set of 596 males, belonging to the Southern Punjab was collectedfrom the Outdoor Ward of Cancer, Oncology Ward of Nishtar Hospital and Multan Institute ofNuclear Medicine and Radiotherapy (MINAR) Hospital. Method: Through a self-administeredquestionnaire, smoking status and respondent’s history and medical record of various typesof cancers were noted. The Chi-square test was used to assess the association betweentobacco smoking and cancer disease. For the risk analysis, odds ratios and attributable riskwere computed. Results: Among the respondents, 49.0% smoked tobacco. From the medicalrecord, 438 respondents were confirmed cancerous. The average age to start tobacco wasnoted to be 23.41 ± 4.85 while the age was 45.29 ± 12.24 years for tobacco cessation. Thepercentage of lung cancer among smokers is 24.01 which is highest among all the statedcancer sites. The risk of a smoker getting all types of the stated cancers is at least three times.The risk of lung cancer attributed to smoking is 17.65 and 50.7% of all the stated cancers.Conclusions: Smokers in the Southern Punjab can greatly reduce their risk (more than 50%)of cancer if they quit smoking.

AimsTo provide awareness of safety concerns around use of alcohol hand sanitiser on a mental health ward, and to consider ways of improving how learning for a serious adverse incident in one trust can better be communicated to other trustsBackgroundDD a male patient with history of paranoid schizophrenia alongside historic illicit drug use and current alcohol dependency admitted detained to Bluestone hospital following bizarre behaviour at a wake. Had been non-compliant with medication. Transferred to PICU due to going AWOL and returning under influence of alcohol.2nd April overnight staff noted him to become over-sedated, presenting with slurred speech and appeared under influence of alcohol – transferred to A + E due to deteriorating GCS – was intubated, and transferred to ICU. Blood alcohol level was 373. Several empty bottles of hand sanitiser from dispensers on ward found in his room and he later disclosed he had accessed further alcohol hand sanitiser in sluice while washing clothesSAI learning outcomes from one healthcare trust in Northern Ireland not currently routinely shared with other trustsMethodLiterature review carried out to search for reports of similar incidents – 1 previous review article suggesting one death and 11 other major complications from consumption of alcohol hand sanitiser over 5 year period 2005-2009.Quality improvement steps implemented to address this riskWard policy was reviewed to ensure patients no longer had unsupervised access to wash clothesLiaised with Infection Control to assess the need for alcohol hand sanitiser to be available to patients given the ward is effectively a community settingIntoxication policy reviewed and education sessions on this provided to all medical and nursing staffRegional regular PICU staff update seminar launched for purpose of bringing PICU staff from across Northern Ireland together to share learning from SAIs and casesResultInfection control agreed alcohol hand sanitiser dispensers could be removed from wards and kept only in locked nursing office with use of visitors.Learning from this case shared with other trusts locally at newly launched regional PICU update seminarNo further incidents to dateConclusionPatient access to alcohol hand sanitisers found to be a significant safety risk in PICU settingFollowing implementation of quality improvement steps no further incidents of patients swallowing alcohol hand sanitiserImproved awareness of risk of alcohol intoxication on ward with nursing staff escalating concerns to on-call doctor more frequently

Ekonomisk brottslighet är mer lukrativt jämfört med annan brottslighet som narkotika, häleri och människohandel. Tidiga åtgärder som försvårar att kriminella kan använda företag för brottsliga syften gör att stora kostnader för samhället kan undvikas. En genomgång av litteraturen visade också att det finns stora brister i samarbetet mellan svenska myndigheter för att upptäcka grov ekonomisk brottslighet. Idag uppdagas brotten först ofta efter att en konkurs inletts. I studier har maskininlärningsmodeller prövats för att kunna upptäcka ekonomisk brottslighet och några svenska myndigheter använder maskininlärningsmodeller för att upptäcka brott men mer avancerade metoder används idag av danska myndigheter. Bolagsverket har idag ett omfattande register för bolag i Sverige och denna studie syftar till att undersöka om maskininlärning kan användas för att identifiera misstänkta bolag, genom att använda digitalt inlämnade årsredovisningar och information ur bolagsverkets register för att kunna träna klassificeringsmodeller att identifiera misstänkta bolag. För att träna modellen så har stämningsansökningar inhämtats från Ekobrottsmyndigheten som kunnat kopplas till specifika bolag av de inlämnade årsredovisningar. Principalkomponentanalys används för att visuellt visa på skillnader mellan grupperna misstänkta och icke misstänkta bolag och analyserna visade på ett överlapp mellan grupperna och ingen tydlig klustring av grupperna. Data var obalanserat med 38 misstänkta bolag av totalt 1009 bolag och därför användes översamplingstekniken SMOTE för att skapa mer syntetiskt data och för att öka antalet i gruppen misstänkta. Två maskininlärningsmodeller Random Forest och Stödvektormaskin (SVM) jämfördes i en 10 fold korsvalidering. Där båda uppvisade en recall på runt 0.91 men där Random Forest hade en mycket högre precision och med högre accuracy. Random Forest valdes och tränades på nytt och uppvisades en recall på 0.75 när den testades på osett data bestående av 8 misstänkta av 202 bolag. Ett sänkt tröskelvärde resulterade i en högre recall men med en större antal felklassificerade bolag. Studien visar tydligt problemet med obalans i data och de utmaningar man ställs inför med mindre data. Ett större data hade möjligjort ett strängare urval på brottstyper som hade kunnat ge en mer robust modell som skulle kunna användas av bolagsverket för att lättare kunna identifiera misstänkta bolag i deras register.
Economic crimes are more lucrative compared to other crimes as drugs, selling of stolen gods, trafficing. Early preventions that make it more difficult for criminals to use companies for criminal purposes can reduce large costs for sociaty. A litterature study showed that there are large weaknesses in the collaboration between Swedish authorities to detect serious economic crimes.Today most crimes among companies that commit fraud are found after a company has declared bancruptcy. In studies, machine learning models have been tested to detect economic crimes and some swedish authorites are now using machine learning methods to detect different crimes and more advanced methods are used by the danish authorites. Bolagsverket has a large register of companies in Sweden and the aim of this study is to investigate if machinelearning can be used to detect on annual reports that have been digitaly submited and information in Bolagsverket’s register to be able to train classificationsmodels and identify companies that are suspicious. To be able to train the model lawsuits have been collected from the Swedish Economic Crime Authority that can be connected to specific companies through their digitally submited annual report. Principal component analysis is used to visually show differences between the groups suspect companies and not suspected companies and the analysis show that there is an overlap between the groups and no clear clustering between the groups. Because the dataset was unbalanced with 38 suspicious companies out of 1009 companies the oversampling tecnique SMOTE was used to create more synthethic data and more suspects in the dataset. The two machinelearnings models Random Forest and support vector machine (SVM) was compared in a 10 fold crossvalidation. Both models showed a recall on around 0.91 but Random Forest had a much higher precision with a higher accuracy. Random Forest was chosen and was trained again and showed a recall on 0.75 when it was tested on unseen data with 8 suspects out of 202 companies. Lowering the treshold resulted in a higher recall but with a larger portion of wrongly classfied companies. The study shows clearly the problem with an unbalanced dataset and the challanges with a small dataset. A larger dataset could have made it possible to make a more selective selection of certain crimes that could have resulted in a more robust model that could be used by Bolagsverket to easier identify suspicous companies in their register.