Programpunkter

A safe and reliable flow of information is indispensable for nursing. Its role is particularly emphasised in mobile care, such as preliminary home care or home hospital care. Inefficient information flow poses a threat to patient safety and continuity of care. What measures can be taken to support the flow of information? 

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Amy är den smarta söktjänsten för varje vårdavdelning! Amy är er nya smarta kollega som hjälper er avdelning att spåra utrustning och håller ordning på service och underhåll. Amy vet vart utrustning finns, hur länge den stått där och när den senast blev servad eller rengjord. Ett användarvänligt och enkelt system som hjälper er avdelning att använda utrustningen mer effektivt och minskar hanteringen med att leta efter utrustning och material. Mer tid till att vårda istället för att leta helt enkelt! Presenteras av: OneMed Sverige AB. Presenteras på engelska.

Ai in its current form and function is unrealistic unsupportable and for some unattainable economically speaking. A vision of democratizing and commoditizing Ai while running at scale never seen. In addition how can this new user not human never takes a break be managed without overwhelming the clinical staff.Presented will be a challenge to all vendors in the industry and the future. It’s time for a change.Presented will be an idea for a platform that leverages existing application as we manage human/clinical driven workflows vs data/machine learning workflows going forward.

As a free service you are invited to participate in a matchmaking event open to all delegates, presenters and exhibition participants. The aim of the event is to create win-win- situations and perhaps future collaborations between researchers, practitioners, patients and industry.The matchmaking is only available for conference delegates, investors and exhibitors.

Electrodermal activity (EDA) reflects sympathetic nervous system activitythrough sweating-related changes in skin conductance. Decomposition analysisis used to deconvolve the EDA into slow and fast varying tonic and phasicactivity, respectively. In this study, we used machine learning models to comparethe performance of two EDA decomposition algorithms to detect emotions such asamusing, boring, relaxing, and scary. The EDA data considered in this study wereobtained from the publicly available Continuously Annotated Signals of Emotion(CASE) dataset. Initially, we pre-processed and deconvolved the EDA data intotonic and phasic components using decomposition methods such as cvxEDA andBayesianEDA. Further, twelve-time domain features were extracted from the phasiccomponent of EDA data. Finally, we applied machine learning techniques suchas logistic regression (LR) and support vector machine (SVM), to evaluate the performanceof the decomposition method. Our results imply that the BayesianEDAdecomposition method outperforms the cvxEDA. The mean of first derivative featurediscriminated all the considered emotional pairs with high statistical significance(p<0.05). SVM was able to detect emotions better than the LR classifier.We achieved a 10-fold average classification accuracy, sensitivity, specificity, precision,and f1-score of 88.2%, 76.25%, 92.08%, 76.16%, and 76.15% respectively,using BayesianEDA and SVM classifier. The proposed framework can be utilizedto detect emotional states for the early diagnosis of psychological conditions.

Gyrocardiography motion sensor technology, that can be operated on smartphones with no additional hardware required, has been validated for numerous heart diseases such heart failure, aortic stenosis, atrial fibrillation and pulmonary hypertension. The talk will focus on the scientific evidence from numerous clinical studies and demonstrating clinical utility in several use cases.