Patients' narratives about traumatic experiences and symptoms are useful in clinical screening and diagnostic procedures. In this study, we presented an automated assessment system to screen patients for posttraumatic stress disorder via a natural language processing and text-mining approach. Four machine-learning algorithms-including decision tree, naive Bayes, support vector machine, and an alternative classification approach called the product score model-were used in combination with n-gram representation models to identify patterns between verbal features in self-narratives and psychiatric diagnoses. With our sample, the product score model with unigrams attained the highest prediction accuracy when compared with practitioners' diagnoses. The addition of multigrams contributed most to balancing the metrics of sensitivity and specificity. This article also demonstrates that text mining is a promising approach for analyzing patients' self-expression behavior, thus helping clinicians identify potential patients from an early stage.
In contexts where robots share their workspace with humans, safety is of utmost importance. Consequently, in recent years, a big impulse has been given to the design of human-friendly robots by involving both mechanical and control design aspects. Regarding controller design, this often involves introducing compliance and ensuring asymptotic stability using an interaction control scheme and passivity theory. Moreover, when human operators physically interact with the robot during work, strict safety measures become necessary with some of these including power and force limitations. In this letter, a novel impedance control technique for collaborative robots is presented. The featured controller allows a safe human-robot interaction through energy and power limitations, assuring passivity through energy tanks. The proposed controller is evaluated with a KUKA LWR 4+ arm in a comanipulation environment.
The aim of this study was to identify the information about commonly prescribed drugs that junior doctors should know in order to prescribe rationally in daily practice, defined as essential drug knowledge (EDK). A two-round Internet Delphi study was carried out involving general practitioners from one practice cluster, and registrars and consultants from two Dutch academic and eight teaching hospitals. A preliminary list of 377 potential EDK items for three commonly prescribed drugs was assessed on a dichotomous scale; an item was considered EDK if at least 80% consensus was reached. The consensus list of EDK items was discussed by the research team to identify similarities between the three drugs, with a view to forming a list of general EDK items applicable to other commonly prescribed drugs. Sixty experts considered 93 of the 377 items (25%) as EDK. These items were then used to form a list of 10 general EDK items. The list of EDK items identified by primary and secondary care doctors could be used in medical curricula and training programmes and for assessing the prescribing competence of future junior doctors. Further research is needed to evaluate the generalizability of this list for other commonly prescribed drugs.Rational prescribing (i.e. effectively, safely and at low cost) is an essential skill for medical doctors to reduce the frequency of avoidable adverse drugs reactions and prescribing errors [1,2]. Unfortunately, numerous studies have revealed that the prescribing performance of junior doctors is inadequate and that they make many avoidable prescribing errors, resulting in inefficiencies in patient care and even patient harm [1][2][3][4][5]. There is considerable evidence that a major factor contributing to prescribing errors is a lack of basic knowledge of pharmacology and pharmacotherapy among recent graduates [6][7][8][9][10][11][12][13][14][15][16]. Thus, improving the pharmacology and pharmacotherapy knowledge of medical students might prevent or reduce the number of these errors in the future [1,6].In order to prescribe adequately, it is important to identify which information about individual drugs should be ready knowledge and which can be looked up (e.g. by using a mobile app or electronic prescribing system). Some studies suggest that medical students should use, and have a thorough knowledge of, a core list of commonly prescribed drugs, such as the essential drug list [17] or the student formulary [18][19][20][21], so that they can prescribe these drugs appropriately, under the supervision of a senior doctor. This knowledge of drug information and prescribing competence should be tested in an examination before graduation. Until now, there has been no clear and robust definition of what graduates should know about commonly prescribed drugs (e.g. doses, contraindications, etc.) to prescribe rationally. Therefore, the aim of this study was to identify which information about commonly prescribed drugs junior doctors should have acquired in order to prescribe rationally in dail...
This paper revisits a well-known synthesis problem in iterative learning control, where the objective is to optimize a performance criterion over a class of causal iterations. The approach taken here adopts an infinite-time setting and looks at limit behavior.The first part of the paper considers iterations without current-cycle-feedback (CCF) term. A notion of admissibility is introduced to distinguish between pairs of operators that define a robustly converging iteration and pairs that do not. The set of admissible pairs is partitioned into disjoint equivalence classes. Different members of an equivalence class are shown to correspond to different realizations of a (stabilizing) feedback controller. Conversely, every stabilizing controller is shown to allow for a (non-unique) factorization in terms of admissible pairs. Class representatives are introduced to remove redundancy. The smaller set of representative pairs is shown to have a trivial parameterization that coincides with the Youla parameterization of all stabilizing controllers (stable plant case).The second part of the paper considers the general family of CCF-iterations. Results derived in the non-CCF case carry over, with the exception that the set of equivalent controllers now forms but a subset of all stabilizing controllers. Necessary and sufficient conditions for full generalization are given. ᭧
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