ion of our DES model in simulating the stroke service within any location worldwide.We aimed to create and validate a natural language processing algorithm to extract wound infection-related information from nursing notes. We also estimated wound infection prevalence in homecare settings and described related patient characteristics. In this retrospective cohort study, a natural language processing algorithm was developed and validated against a gold standard testing set. Cases with wound infection were identified using the algorithm and linked to Outcome and Assessment Information Set data to identify related patient characteristics. The final version of the natural language processing vocabulary contained 3914 terms and expressions related to the presence of wound infection. The natural language processing algorithm achieved overall good performance (F-measure = 0.88). The presence of wound infection was documented for 1.03% (n = 602) of patients without wounds, for 5.95% (n = 3232) of patients with wounds, and 19.19% (n = 152) of patients with wound-related hospitalisation or emergency department visits. Diabetes, peripheral vascular disease, and skin ulcer were significantly associated with wound infection among homecare patients. Our findings suggest that nurses frequently document wound infection-related information. The use of natural language processing demonstrated that valuable information can be extracted from nursing notes which can be used to improve our understanding of the care needs of people receiving homecare. By linking findings from clinical nursing notes with additional structured data, we can analyse related patients’ characteristics and use them to develop a tailored intervention that may potentially lead to reduced wound infection-related hospitalizations.Production-based cognitive models, such as Adaptive Control of Thought-Rational (ACT-R) or Soar agents, have been a popular tool in cognitive science to model sequential decision processes. While the models have been useful in articulating assumptions and predictions of various theories, they unfortunately require a significant amount of hand coding, both with respect to what building blocks cognitive processes should consist of and with respect to how these building blocks are selected and ordered in a sequential decision process. Hand coding of large, realistic models poses a challenge for modelers, and also makes it unclear whether the models can be learned and are thus cognitively plausible. The learnability issue is probably most starkly present in cognitive models of linguistic skills, since linguistic skills involve richly structured representations and highly complex rules. We investigate how reinforcement learning (RL) methods can be used to solve the production selection and production ordering problem in ACT-R. We focus on four algorithms from the Q learning family, tabular Q and three versions of deep Q networks (DQNs), as well as the ACT-R utility learning algorithm, which provides a baseline for the Q algorithms. We compare the performance of these five algorithms in a range of lexical decision (LD) tasks framed as sequential decision problems. We observe that, unlike the ACT-R baseline, the Q agents learn even the more complex LD tasks fairly well. However, tabular Q and DQNs show a trade-off between speed of learning, applicability to more complex tasks, and how noisy the learned rules are. PFI-2 This indicates that the ACT-R subsymbolic system for procedural memory could be improved by incorporating more insights from RL approaches, particularly the function-approximation-based ones, which learn and generalize effectively in complex, more realistic tasks.We explored the social synchronization of gaze-shift behaviors when responding to joint attention in children with autism spectrum disorder (ASD). Forty-one children aged 5 to 8 with ASD and 43 typically developing (TD) children watched a video to complete the response to joint attention (RJA) tasks, during which their gaze data were collected. The synchronization of gaze-shift behaviors between children and the female model in the video was measured with the cross-recurrence quantification analysis (CRQA). Ultimately, we discovered that children with ASD had the ability to synchronize their gaze shifts with the female model in the video during RJA tasks. Compared to the TD children, they displayed lower levels of synchronization and longer latency in this synchronized behavior. These findings provide a new avenue to deepen our understanding of the impairments of social interaction in children with ASD. Notably, the analytic method can be further applied to explore the social synchronization of numerous other social interactive behaviors in ASD. LAY SUMMARY This study explored how autistic children synchronized their gazed shifts with others’ gaze cues during joint attention. We found that compared to typical children, autistic children synchronized their gazed shifts less and needed more time to follow others’ gaze. These findings provide a new avenue to deepen our understanding of the impairments of social interaction in children with ASD.

Adverse drug reactions are an important public health concern that affects doctor and dentist prescriptions and healthcare workers’ practice. We planned to evaluate the knowledge, attitudes and practices of healthcare workers in our country about drug hypersensitivity reactions in paediatric patients and to determine the risk factors that may affect them.

This study was carried out in a capital-located university hospital. Healthcare workers authorized to intervene in children (0-18 age group), including medical doctors, nurses, and dentists, were enrolled in the study. The study questionnaire was developed by paediatric allergy and immunology specialists and paediatric nurses by considering the other studies on the same subject.

Three hundred fifty-four (88.5%) out of 400 healthcare workers, whose study survey was distributed, returned to us by filling the questionnaire. According to the groups of the profession, there was a statistically significant difference between the average of correct answers given to the questions evaluating knowledge levels (P<.