Plos One
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Observational Study
Combining patient visual timelines with deep learning to predict mortality.
Deep learning algorithms have achieved human-equivalent performance in image recognition. However, the majority of clinical data within electronic health records is inherently in a non-image format. Therefore, creating visual representations of clinical data could facilitate using cutting-edge deep learning models for predicting outcomes such as in-hospital mortality, while enabling clinician interpretability. The objective of this study was to develop a framework that first transforms longitudinal patient data into visual timelines and then utilizes deep learning to predict in-hospital mortality. ⋯ We converted longitudinal patient data into visual timelines and applied a deep neural network for predicting in-hospital mortality more accurately than current standard clinical models, while allowing for interpretation. Our framework holds promise for predicting several important outcomes in clinical medicine.
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As part of the Models of Child Health Appraised (MOCHA) project, this study aimed to answer the following research questions: 1) How do European citizens perceive the quality of primary health care provided for children? And 2) What are their priorities with respect to quality assessment of primary health care aimed at satisfying children's needs? ⋯ Between countries, significant differences exist in the perceived quality of primary care and priorities with regard to quality assessment. Taking into account the citizens' perspective in decision-making means that aspects with low perceived quality that are highly prioritized warrant further action.
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The Text Forum Threads (TFThs) contain a large amount of Initial-Posts Replies pairs (IPR pairs) which are related to information exchange and discussion amongst the forum users with similar interests. Generally, some user replies in the discussion thread are off-topic and irrelevant. Hence, the content is of different qualities. ⋯ Moreover, crowdsourcing platforms were used for judging the quality of the replies and classified them into high-quality, low-quality or non-quality replies to the Initial-Posts. Then, the high-quality IPR pairs were extracted and identified based on their quality, and they were ranked using three classifiers i.e., Support Vector Machine, Naïve Bayes, and the Decision Trees according to their quality dimensions of relevancy, author activeness, timeliness, ease-of-understanding, politeness, and amount-of-data. In conclusion, the experimental results for the TFThs showed that the proposed approach could improve the extraction of the quality replies and identify the quality features that can be used for the Text Forum Thread Summarization.
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Comparative Study
Prevalence of intimate partner violence against women in Sweden and Spain: A psychometric study of the 'Nordic paradox'.
The high prevalence of intimate partner violence against women (IPVAW) in countries with high levels of gender equality has been defined as the "Nordic paradox". In this study we compared physical and sexual IPVAW prevalence data in two countries exemplifying the Nordic paradox: Sweden (N = 1483) and Spain (N = 1447). Data was drawn from the European Union Agency for Fundamental Rights Survey on violence against women. ⋯ The effect sizes of these differences were large: 89.1% of the Swedish sample had higher values in the physical IPVAW factor than the Spanish average, and this percentage was 99.4% for the sexual IPVAW factor as compared to the Spanish average. In terms of probability of superiority, there was an 80.7% and 96.1% probability that a Swedish woman would score higher than a Spanish woman in the physical and the sexual IPVAW factors, respectively. Our results showed that the higher prevalence of physical and sexual IPVAW in Sweden than in Spain reflects actual differences and are not the result of measurement bias, supporting the idea of the Nordic paradox.
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An infectious disease outbreak such as the 2009 influenza pandemic is an unexpected demand shock to hospital emergency departments (EDs). We analysed changes in key performance metrics in (EDs) in Victoria during this pandemic to assess the impact of this demand shock. ⋯ This pandemic event caused excess demand leading to increased waiting times, did-not-wait patients and access block. Increases in admitted patients were more strongly associated with crowding than non-admitted patients during the pandemic period, so policies to divert or mitigate low-complexity non-admitted patients are unlikely to be effective in reducing ED crowding.