Aims Medication harm has negative clinical and economic consequences, contributing to hospitalisation, morbidity and mortality. The incidence ranges from 4 to 14%, of which up to 50% of events may be preventable. A predictive model for identifying high‐risk inpatients can guide a timely and systematic approach to prioritisation. The aim of this study is to develop and internally validate a risk prediction model for prioritisation of hospitalised patients at risk of medication harm. Methods A retrospective cohort study was conducted in general medical and geriatric specialties at an Australian hospital over six months. Medication harm was identified using International Classification of Disease (ICD‐10) codes and the hospital's incident database. Sixty‐eight variables, including medications and laboratory results, were extracted from the hospital's databases. Multivariable logistic regression was used to develop the final risk model. Performance was evaluated using area under the receiver operative characteristic curve (AuROC) and clinical utility was determined using decision curve analysis. Results The study cohort included 1982 patients with median age 74 years, of which 136 (7%) experienced at least one adverse medication event(s). The model included: length of stay, hospital re‐admission within 12 months, venous or arterial thrombosis and/or embolism, ≥ 8 medications, serum sodium < 126 mmol/L, INR > 3, anti‐psychotic, antiarrhythmic and immunosuppressant medications, and history of medication allergy. Validation gave an AuROC of 0.70 (95% CI: 0.65–0.74). Decision curve analysis identified that the AIME may be clinically useful to help guide decision making in practice. Conclusion We have developed a predictive model with reasonable performance. Future steps include external validation and impact evaluation.

With the widespread of social media websites in the internet, and the huge number of users participating and generating infinite number of contents in these websites, the need for personalisation increases dramatically to become a necessity. One of the major issues in personalisation is building users' profiles, which depend on many elements; such as the used data, the application domain they aim to serve, the representation method and the construction methodology. Recently, this area of research has been a focus for many researchers, and hence, the proposed methods are increasing very quickly. This survey aims to discuss the available user modelling techniques for social media websites, and to highlight the weakness and strength of these methods and to provide a vision for future work in user modelling in social media websites.

To identify and critically appraise studies of prediction models, developed using machine learning (ML) methods, for determining the optimal dosing of unfractionated heparin (UFH).

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Abstract. Reputation systems are employed to provide users with advice on the quality of items on the Web, based on the aggregated value of user-based ratings. Recommender systems are used online to suggest items to users according to the users, expressed preferences. Yet, recommender systems will endorse an item regardless of its reputation value. In this paper, we report the incorporation of reputation models into recommender systems to enhance the accuracy of recommendations. The proposed method separates the implementation of recommender and reputation systems for generality. Our experiment showed that the proposed method could enhance the accuracy of existing recommender systems.

Many websites offer the opportunity for customers to rate items and then use customers' ratings to generate items reputation, which can be used later by other users for decision making purposes. The aggregated value of the ratings per item represents the reputation of this item. The accuracy of the reputation scores is important as it is used to rank items. Most of the aggregation methods didn't consider the frequency of distinct ratings and they didn't test how accurate their reputation scores over different datasets with different sparsity. In this work we propose a new aggregation method which can be described as a weighted average, where weights are generated using the normal distribution. The evaluation result shows that the proposed method outperforms stateof-the-art methods over different sparsity datasets.

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