Simple training tool is insufficient for appropriate diagnosis and treatment of malnutrition: A pre-post intervention study in a tertiary center.

OBJECTIVES To improve malnutrition awareness and management in our department of general internal medicine; to assess patients' nutritional risk; and to evaluate whether an online educational program leads to an increase in basic knowledge and more frequent nutritional therapies. METHODS A prospective pre-post intervention study at a university department of general internal medicine was conducted. Nutritional screening using Nutritional Risk Score 2002 (NRS 2002) was performed, and prescriptions of nutritional therapies were assessed. The intervention included an online learning program and a pocket card for all residents, who had to fill in a multiple-choice questions (MCQ) test about basic nutritional knowledge before and after the intervention. RESULTS A total of 342 patients were included in the preintervention phase, and 300 were in the postintervention phase. In the preintervention phase, 54.1% were at nutritional risk (NRS 2002 ≥3) compared with 61.7% in the postintervention phase. There was no increase in the prescription of nutritional therapies (18.7% versus 17.0%). Forty-nine and 41 residents (response rate 58% and 48%) filled in the MCQ test before and after the intervention, respectively. The mean percentage of correct answers was 55.6% and 59.43%, respectively (which was not significant). Fifty of 84 residents completed the online program. The residents who participated in the whole program scored higher on the second MCQ test (63% versus 55% correct answers, P = 0.031). CONCLUSIONS Despite a high ratio of malnourished patients, the nutritional intervention, as assessed by nutritional prescriptions, is insufficient. However, the simple educational program via Internet and usage of NRS 2002 pocket cards did not improve either malnutrition awareness or nutritional treatment. More sophisticated educational systems to fight malnutrition are necessary.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior, and in particular in behavioral decision making. Such decision making is likely to involve the integration of many synaptic events in space and time. However, using a single reinforcement signal to modulate synaptic plasticity, as suggested in classical reinforcement learning algorithms, a twofold problem arises. Different synapses will have contributed differently to the behavioral decision, and even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike-time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward, but also by a population feedback signal. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference (TD) based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task, the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second task involves an action sequence which is itself extended in time and reward is only delivered at the last action, as it is the case in any type of board-game. The third task is the inspection game that has been studied in neuroeconomics, where an inspector tries to prevent a worker from shirking. Applying our algorithm to this game yields a learning behavior which is consistent with behavioral data from humans and monkeys, revealing themselves properties of a mixed Nash equilibrium. The examples show that our neuronal implementation of reward based learning copes with delayed and stochastic reward delivery, and also with the learning of mixed strategies in two-opponent games.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior. But behavioral decision making is likely to involve the integration of many synaptic events in space and time. So in using a single reinforcement signal to modulate synaptic plasticity a twofold problem arises. Different synapses will have contributed differently to the behavioral decision and, even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward but by a population feedback signal as well. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second one involves an action sequence which is itself extended in time and reward is only delivered at the last action, as is the case in any type of board-game. The third is the inspection game that has been studied in neuroeconomics. It only has a mixed Nash equilibrium and exemplifies that the model also copes with stochastic reward delivery and the learning of mixed strategies.

Fuzzy Web Knowledge Aggregation, Representation, and Reasoning for Online Privacy and Reputation Management

A social Semantic Web empowers its users to have access to collective Web knowledge in a simple manner, and for that reason, controlling online privacy and reputation becomes increasingly important, and must be taken seriously. This chapter presents Fuzzy Cognitive Maps (FCM) as a vehicle for Web knowledge aggregation, representation, and reasoning. With this in mind, a conceptual framework for Web knowledge aggregation, representation, and reasoning is introduced along with a use case, in which the importance of investigative searching for online privacy and reputation is highlighted. Thereby it is demonstrated how a user can establish a positive online presence.

An welchen Vorerfahrungen sollten Planende und Durchführende von Weiterbildungen mit ihren Angeboten zu E-Learning ansetzen, um Lehrende in berufsbegleitenden Studienangeboten zu erreichen?

Obwohl E-Learning-Anteile im Studium ein unverzichtbares Element für das zeit- und ortsunabhängige Lernen für berufstätige Studierende sind, setzen die Lehrenden in den berufsbegleitenden Studiengängen der Technischen Hochschule Ingolstadt (THI) E-Learning wenig bis kaum ein. Weiterbildungen zu dieser Thematik bestehen, führen aber zurzeit nicht zu den gewünschten Änderungen der Lehre. Im Forschungs- und Entwicklungsprojekt „Offene Hochschule Oberbayern (OHO)“ wird daher unter anderem die Frage beantwortet, an welchen Vorerfahrungen in Hinblick auf technische und didaktische Einsatzmöglichkeiten von ELearning, aber auch in Hinblick auf die Haltung gegenüber Online-Lehre, für die Gestaltung von Weiterbildungen für Lehrende angesetzt werden kann. Eine mehrstufige Bedarfsanalyse im „OHO“-Projekt liefert hier Ergebnisse, die ein aus hochschuldidaktischer Sicht entwickeltes Weiterbildungskonzept um die Sicht der Studierenden, der Institution und der Lehrenden anreichert.