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Medical applications challenge today's text categorization techniques by demanding both high accuracy and ease-of-interpretation. Although deep learning has provided a leap ahead in accuracy, this leap comes at the sacrifice of interpretability. To address this accuracy-interpretability challenge, we here introduce, for the first time, a text categorization approach that leverages the recently introduced Tsetlin Machine. In all brevity, we represent the terms of a text as propositional variables. From these, we capture categories using simple propositional formulae, such as: if "rash" and "reaction" and "penicillin" then Allergy. The Tsetlin Machine learns these formulae from a labelled text, utilizing conjunctive clauses to represent the particular facets of each category. Indeed, even the absence of terms (negated features) can be used for categorization purposes. Our empirical comparison with Naïve Bayes, decision trees, linear support vector machines (SVMs), random forest, long shortterm memory (LSTM) neural networks, and other techniques, is quite conclusive. The Tsetlin Machine either performs on par with or outperforms all of the evaluated methods on both the 20 Newsgroups and IMDb datasets, as well as on a non-public clinical dataset. On average, the Tsetlin Machine delivers the best recall and precision scores across the datasets. Finally, our GPU implementation of the Tsetlin Machine executes 5 to 15 times faster than the CPU implementation, depending on the dataset. We thus believe that our novel approach can have a significant impact on a wide range of text analysis applications, forming a promising starting point for deeper natural language understanding with the Tsetlin Machine.

Remifentanil IVPCA and epidural provided effective analgesia, with high maternal satisfaction scores and reassuring neonatal outcome. Remifentanil produced more maternal sedation and oxygen desaturation. Close monitoring is, therefore, mandatory.

In Norway, the frequency of epidural for labour analgesia is still relatively low, but seems to be increasing. Systemic opioids are often used instead of or as a supplement. Clinical practice seems to be conservative, and newer short-acting opioids are seldom used systemically.

Background Natural language processing (NLP) based clinical decision support systems (CDSSs) have demonstrated the ability to extract vital information from patient electronic health records (EHRs) to facilitate important decision support tasks. While obtaining accurate, medical domain interpretable results is crucial, it is demanding because real-world EHRs contain many inconsistencies and inaccuracies. Further, testing of such machine learning-based systems in clinical practice has received limited attention and are yet to be accepted by clinicians for regular use. Methods We present our results from the evaluation of an NLP-driven CDSS developed and implemented in a Norwegian Hospital. The system incorporates unsupervised and supervised machine learning combined with rule-based algorithms for clinical concept-based searching to identify and classify allergies of concern for anesthesia and intensive care. The system also implements a semi-supervised machine learning approach to automatically annotate medical concepts in the narrative. Results Evaluation of system adoption was performed by a mixed methods approach applying The Unified Theory of Acceptance and Use of Technology (UTAUT) as a theoretical lens. Most of the respondents demonstrated a high degree of system acceptance and expressed a positive attitude towards the system in general and intention to use the system in the future. Increased detection of patient allergies, and thus improved quality of practice and patient safety during surgery or ICU stays, was perceived as the most important advantage of the system. Conclusions Our combined machine learning and rule-based approach benefits system performance, efficiency, and interpretability. The results demonstrate that the proposed CDSS increases detection of patient allergies, and that the system received high-level acceptance by the clinicians using it. Useful recommendations for further system improvements and implementation initiatives are reducing the quantity of alarms, expansion of the system to include more clinical concepts, closer EHR system integration, and more workstations available at point of care.

ObjectivesThe aim of this systematic review was to examine the effectiveness of pre-anaesthesia assessment clinics (PACs) in improving the quality and safety of perioperative patient care.DesignSystematic review.Data sourcesThe electronic databases CINAHL Plus with Full Text (EBSCOhost), Medline and Embase (OvidSP) were systematically searched on 11 September 2018 and updated on 3 February 2020 and 4 February 2021.Eligibility criteriaThe inclusion criteria for this study were studies published in English or Scandinavian language and scientific original research that included randomised or non-randomised prospective controlled studies. Additionally, studies that reported the outcomes from a PAC consultation with the patient present were included.Data extraction and synthesisTitles, abstracts and full texts were screened by a team of three authors. Risk of bias was assessed using the Joanna Briggs Institute critical appraisal checklist for quasi-experimental studies. Data extraction was performed by one author and checked by four other authors. Results were synthesised narratively owing to the heterogeneity of the included studies.ResultsSeven prospective controlled studies on the effectiveness of PACs were included. Three studies reported a significant reduction in the length of hospital stay and two studies reported a significant reduction in cancellation of surgery for medical reasons when patients were seen in the PAC. In addition, the included studies presented mixed results regarding anxiety in patients. Most studies had a high risk of bias.ConclusionThis systematic review demonstrated a reduction in the length of hospital stay and cancellation of surgery when the patients had been assessed in the PAC. There is a need for high-quality prospective studies to gain a deeper understanding of the effectiveness of PACs.PROSPERO registration numberCRD42019137724.

A total of 168 interns who have graduated from the Medical Schools of Bergen and Tromsø were asked about various aspects of the medical curriculum. In Bergen the curriculum has a traditional structure with a pre-clinical and a clinical part, but in Tromsø the pre-clinical and clinical subjects are integrated. In addition, the students in Tromsø spend long periods in municipal hospitals and in the primary health care service. We were interested in how the interns from the two universities evaluated their respective curricula and how prepared they felt for their current work. There was a response rate of 86% to the questionnaire. The results showed that the interns from Tromsø are more satisfied with their education and feel more confident in their practical skills than the interns from Bergen. They are also more motivated for future work in general practice. In our opinion the main reason for these results is the difference in curricula in the two medical schools. Other possible reasons are also discussed.

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