Measuring the burden of herpes zoster and post herpetic neuralgia within primary care in rural Crete, Greece

<p>Abstract</p> <p>Background</p> <p>Research has indicated that general practitioners (GPs) have good clinical judgment in regards to diagnosing and managing herpes zoster (HZ) within clinical practice in a country with limited resources for primary care and general practice. The objective of the current study was to assess the burden of HZ and post herpetic neuralgia (PHN) within rural general practices in Crete, Greece.</p> <p>Methods</p> <p>The current study took place within a rural setting in Crete, Greece during the period of November 2007 to November 2009 within the catchment area in which the Cretan Rural Practice-based Research Network is operating. In total 19 GP's from 14 health care units in rural Crete were invited to participate, covering a total turnover patient population of approximately 25, 000 subjects. For the purpose of this study an electronic record database was constructed and used as the main tool for monitoring HZ and PHN incidence. Stress related data was also collected with the use of the Short Anxiety Screening Test (SAST).</p> <p>Results</p> <p>The crude incidence rate of HZ was 1.4/1000 patients/year throughout the entire network of health centers and satellite practices, while among satellite practices alone it was calculated at 1.3/1000 patients/year. Additionally, the standardised incidence density within satellite practices was calculated at 1.6/1000 patients/year. In regards to the stress associated with HZ and PHN, the latter were found to have lower levels of anxiety, as assessed through the SAST score (17.4 ± 3.9 vs. 21.1 ± 5.7; <it>p </it>= 0.029).</p> <p>Conclusions</p> <p>The implementation of an electronic surveillance system was feasible so as to measure the burden of HZ and PHN within the rural general practice setting in Crete.</p

A Recommendation Specific Human Activity Recognition Dataset with Mobile Device’s Sensor Data

Part 5: Energy Efficiency and Artificial Intelligence (ΕΕΑΙ 2021) WorkshopInternational audienceHuman activity recognition is a challenging field that grabbed considerable research attention in the last decade. Two types of models can be used for such predictions, those which use visual data and those which use data from inertial sensors. To improve the classification algorithms in the sensor category, a new dataset has been created, targeting more realistic activities, during which the user may be more prompt to receive and act upon a recommendation. Contrary to previous similar datasets, which were collected with the device in the user’s pockets or strapped to their waist, the introduced dataset presents activities during which the user is looking on the screen, and thus most likely interacts with the device. The dataset from an initial sample of 31 participants was gathered using a mobile application that prompted users to do 10 different activities following specific guidelines. Finally, towards evaluating the resulting data, a brief classification benchmarking was performed with two other datasets (i.e., WISDM and Actitracker datasets) by employing a Convolutional Neural Network model. The results acquired demonstrate a promising performance of the model tested, as well as a high quality of the dataset created, which is available online on Zenodo

Self-Healing of Semantically Interoperable Smart and Prescriptive Edge Devices in IoT

Smart homes enhance energy efficiency without compromising residents’ comfort. To support smart home deployment and services, an IoT network must be established, while energy-management techniques must be applied to ensure energy efficiency. IoT networks must perpetually operate to ensure constant energy and indoor environmental monitoring. In this paper, an advanced sensor-agnostic plug-n-play prescriptive edge-to-edge IoT network management with micro-services is proposed, supporting also the semantic interoperability of multiple smart edge devices operating in the smart home network. Furthermore, IoT health-monitoring algorithms are applied to inspect network anomalies taking proper healing actions/prescriptions without the need to visit the residency. An autoencoder long short-term memory (AE-LSTM) is selected for detecting problematic situations, improving error prediction to 99.4%. Finally, indicative evaluation results reveal the mitigation of the IoT system breakdowns

Feeding Flaxseed and Lupins during the Transition Period in Dairy Cows: Effects on Production Performance, Fertility and Biochemical Blood Indices

Flaxseed and lupin seed were offered as an alternative dietary approach in dairy cows, through the partial substitution of soybean meal. Milk production and fertility traits were investigated. A total of 330 animals were allocated into two groups, treated (n = 176) and control (n = 154). From each group, 30 animals were selected for hematological and cytological studies. The experimental feeding period lasted for 81 days (25 days prepartum and 56 days postpartum). The control ration (group C) contained corn, barley, soybean meal, rapeseed cake, corn silage and lucerne hay; whereas, in the treatment group (group T), 50% of the soybean meal was replaced by an equal mixture of flaxseed and lupins. The two rations were formulated to be isonitrogenous and isoenergetic. Milk samples were analyzed for chemical composition, somatic cell count (SCC) content and total colony forming units (CFU). Blood samples were collected, and serum was analyzed for non-esterified fatty acids (NEFA), acute phase proteins (haptoglobin and serum amyloid) and lipid oxidation indices, namely thiobarbituric-acid-reactive substances (TBARS) and catalase activity. To assess polymorphonuclear neutrophils (PMN) numbers, endometrial samples from each cow were collected on days 21 and 42. No difference was recorded between groups in milk yield (p > 0.05). In multiparous cows, NEFA (mMol/L) concentrations were significantly lower in group T than in group C on day 14 (p > 0.009) and on day 42 (p = 0.05), while no difference was detected in the group of primiparous cows. At all time points, serum TBARS and catalase values were similar in both groups (p > 0.05). Multiparous cows in group T expressed the first postpartum estrus and conceived earlier than cows in group C (p ≤ 0.05). Between days 21 to 42 postpartum, the PMN reduction rate was higher in group T animals (p ≤ 0.05). Acute phase protein levels were in general lower in group T animals, and at specific time points differed significantly from group C (p ≤ 0.05). It was concluded that the partial replacement of soybean meal by flaxseed and lupins had no negative effect on milk yield or milk composition, and improved cow fertility; which, along with the lower cost of flaxseed and lupins mixture, may increase milk production profitability

Improving Energy Efficiency in Tertiary Buildings Through User-Driven Recommendations Delivered on Optimal Micro-moments

Part 5: Energy Efficiency and Artificial Intelligence (ΕΕΑΙ 2021) WorkshopInternational audienceSustainable energy is hands down one of the biggest challenges of our times. As the EU sets its focus to reach its 2030 and 2050 goals, the importance of energy efficiency for energy consumers/prosumers becomes prevalent. Over the years, a lot of different approaches have been followed to engage end-users and affect energy-related occupant behaviour towards improving energy efficiency results and long term behaviour changes. This work presents the SIT4Energy user-centered approach for tertiary buildings that delivers an end-to-end solution that takes into consideration a set of tools and models for successfully engaging and affecting the end-user’s energy-related behaviour. Starting from appropriate user profiling models for energy-related behaviour models and an explainable recommendation engine, to on the fly human activity tracking and micro-moments detection on mobile devices, a set of recommendations are delivered to the end-users through a mobile device, presenting valuable information with user-tailored context and on the optimal timing. The overall solution is clearly documented, whereas real-life results are presented from the deployment in offices in a university building. From the evaluation performed it is clearly depicted that a positive impact has been achieved both in terms of energy efficiency as well as energy-related behaviour

Dietary Supplementation with Pomegranate and Onion Aqueous and Cyclodextrin Encapsulated Extracts Affects Broiler Performance Parameters, Welfare and Meat Characteristics

The purpose of this trial was to evaluate the effects of Punica granatum L. and Allium cepa L. peels aqueous and cyclodextrin extracts on broiler chicks&rsquo; performance and welfare status, as well as on the meat chemical composition and oxidative stability. A total of 120 one-day-old male Ross-308 chicks were randomly allocated to three treatments with four replicate pens (10 chicks per pen). Broiler chicks in the control group were fed typical commercial rations in mash form, based on maize and soybean meal. The rations of the other two treatments were further supplemented with the mixture of Punica granatum and Allium cepa aqueous and cyclodextrin extracts at the level of 0.1% of the feed, respectively. At the end of the trial (day 35), tissue samples were collected for analysis. Body weight (BW), feed intake (FI), average daily gain (ADG) and the feed conversion ratio (FCR) during the period of 1&ndash;10 days, 11&ndash;24 days, 25&ndash;35 days and 1&ndash;35 days were evaluated. Litter score, dry matter in litter, pododermatitis and feather score were also assessed at the end of the trial. Data were analyzed with ANOVA using SPSS v25 software. The results showed that BW, FI and FCR values did not differ among the groups. Scoring of pododermatitis, diarrhea, feather, fecal moisture, wooden breast and white stripping did not differ (p &ge; 0.05) among the groups. Punica granatum and Allium cepa aqueous and cyclodextrin extracts favorably affected (p &lt; 0.05) meat composition, color parameters, TBARS and protein carbonyls. Diet supplementation also increased (p &lt; 0.05) &sum;n-3 fatty acids as well as &sum;n-6 fatty acids in the thigh meat. The cis-4,7,10,13,16,19-Docosahexaenoic acid fatty acids in the breast meat of broilers fed with diets supplemented with the aqueous pomegranate and onion peel extracts were found to be higher (p &lt; 0.05), while these fatty acids in the thigh meat were found increased (p &lt; 0.05) in the cyclodextrin group. Aqueous and cyclodextrin pomegranate and onion peel extracts may provide a promising additive to the broilers diet with functional properties, in the absence of stressful conditions

Abstract 1122‐000089: Characterization of Critical Sequelae in Ischemic Stroke Using Natural Language Processing

Introduction: Automated processing of electronic health data to classify complications of ischemic stroke serves numerous purposes, including improved electronic phenotyping for clinical research. Here, we present a natural language processing (NLP) approach to identify critical findings in acute ischemic stroke from unstructured radiology reports of computed tomography (CT) and magnetic resonance imaging (MRI). Methods: Text reports of CT and MRI scans taken from 2292 patients admitted for large (>1/2 middle cerebral artery territory), acute anterior circulation ischemic stroke were gathered from a single‐institution retrospective cohort. Reports were reviewed and labelled for the presence of hemorrhagic conversion, intracerebral edema, midline shift, intraventricular hemorrhage and parenchymal hematoma as defined by European Cooperative Acute Stroke Study PH1 and PH2 categories. For binary classifications, we quantified co‐occurrence of individual words within reports using two separate NLP methods: Bag‐of‐Words (BOW) and Term Frequency‐Inverse Document Frequency (TF‐IDF). We then trained Lasso regression, random forest, and neural network classifiers to predict all complications based on word co‐occurrence. Classifier performance was measured by area under receiver operating characteristic curves (AUC) using five separate folds of an internal test dataset. To predict midline shift as a continuous outcome, we developed a semantic rule‐based system (RBS) based on regular radiographic report expressions. This system was tested using an external validation dataset of 1472 acute large anterior circulation stroke reports from a separate hospital. Results: 2292 reports were fully labelled for the presence of all stroke complications. Lasso regression consistently displayed the best discrimination among all models. For BOW and TF‐IDF, Lasso yielded respective AUCs of 0.894 and 0.919 (hemorrhagic conversion), 0.935 and 0.950 (intracerebral edema), 0.968 and 0.963 (midline shift), 0.933 and 0.904 (intraventricular hemorrhage), and 0.873 and 0.879 (parenchymal hematoma). All models were well‐calibrated to underlying complication rates. The RBS also achieved strong performance in quantifying midline shift, achieving a mean absolute error (MAE) of 0.103 mm, sensitivity of 99.1% and specificity of 97.5% in the original cohort. In the external validation set of 1472 additional stroke reports, this same system achieved a MAE of 0.126 mm, sensitivity of 99.5% and specificity of 97.5% for midline shift. Wilcoxon rank sum testing on bootstrapped samples confirmed no statistically‐significant differences in RBS performance between institutions when comparing MAE (p = 0.918), sensitivity (p = 0.152), and specificity (p = 0.929). Conclusions: A machine learning pipeline based on Lasso regression successfully identified critical complications of large anterior circulation ischemic stroke from unstructured radiology reports, while our RBS quantified midline shift with a high degree of generalized accuracy between different institutions. We propose that these systems may warrant prospective validation in care settings and data mining for stroke research

Natural language processing of radiology reports to detect complications of ischemic stroke

Background Abstraction of critical data from unstructured radiologic reports using natural language processing (NLP) is a powerful tool to automate the detection of important clinical features and enhance research efforts. We present a set of NLP approaches to identify critical findings in patients with acute ischemic stroke from radiology reports of computed tomography (CT) and magnetic resonance imaging (MRI). Methods We trained machine learning classifiers to identify categorical outcomes of edema, midline shift (MLS), hemorrhagic transformation, and parenchymal hematoma, as well as rule-based systems (RBS) to identify intraventricular hemorrhage (IVH) and continuous MLS measurements within CT/MRI reports. Using a derivation cohort of 2289 reports from 550 individuals with acute middle cerebral artery territory ischemic strokes, we externally validated our models on reports from a separate institution as well as from patients with ischemic strokes in any vascular territory. Results In all data sets, a deep neural network with pretrained biomedical word embeddings (BioClinicalBERT) achieved the highest discrimination performance for binary prediction of edema (area under precision recall curve [AUPRC] > 0.94), MLS (AUPRC > 0.98), hemorrhagic conversion (AUPRC > 0.89), and parenchymal hematoma (AUPRC > 0.76). BioClinicalBERT outperformed lasso regression (p  Conclusions Our study demonstrates robust performance and external validity of a core NLP tool kit for identifying both categorical and continuous outcomes of ischemic stroke from unstructured radiographic text data. Medically tailored NLP methods have multiple important big data applications, including scalable electronic phenotyping, augmentation of clinical risk prediction models, and facilitation of automatic alert systems in the hospital setting