The time-line as a device to enhance recall in standardized research interviews: a split ballot study

A split ballot experiment was performed on a time-line procedure that was designed to increase the accuracy of responses to retrospective questions. The time-line unites several aided recall properties and was applied to help respondents to reconstruct their educational history. The data were collected during two main waves (1987 and 1991) of a longitudinal social survey in the Netherlands ðN 1; 257Þ: In both 1987 and 1991 respondents were asked about their educational history from August 1983 on. The agreement between the 1991 reports and 1987 reports about the period 1983-1987 was used as a measure of recall accuracy. It was hypothesized that the time-line would enhance recall accuracy regarding the number of educational courses attended, the starting year of the courses, and the entire set of types of courses attended. Additionally, it was expected that the time-line would be especially helpful if the difficulty of the recall task was high – that is to say, in the case of a high frequency or low saliency of the courses followed. The general picture of the results is that the time-line procedure improved data quality in most conditions and never resulted in inferior data quality, supporting the assumption that it indeed may enhance recall. Key words: Data collection; recall accuracy; memory cues; life history. 1

Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images

It is necessary to accurately identify dental implant brands and the stage of treatment to ensure efficient care. Thus, the purpose of this study was to use multi-task deep learning to investigate a classifier that categorizes implant brands and treatment stages from dental panoramic radiographic images. For objective labeling, 9767 dental implant images of 12 implant brands and treatment stages were obtained from the digital panoramic radiographs of patients who underwent procedures at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2020. Five deep convolutional neural network (CNN) models (ResNet18, 34, 50, 101 and 152) were evaluated. The accuracy, precision, recall, specificity, F1 score, and area under the curve score were calculated for each CNN. We also compared the multi-task and single-task accuracies of brand classification and implant treatment stage classification. Our analysis revealed that the larger the number of parameters and the deeper the network, the better the performance for both classifications. Multi-tasking significantly improved brand classification on all performance indicators, except recall, and significantly improved all metrics in treatment phase classification. Using CNNs conferred high validity in the classification of dental implant brands and treatment stages. Furthermore, multi-task learning facilitated analysis accuracy

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67\% accuracy and 96.02\% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.Comment: 9 pages, 4 figures, Accepted by Scientific Report

Effects of Trait Anxiety and Depression on Working Memory Updating

This study used a reaction time (RT) based keep-track task to investigate whether or not trait anxiety or depression are associated with working memory updating. The dependent variables were RT for within-trial update completion, RT and accuracy measures for probe response, and recall accuracy for the most recent words from each category. The effects of anxiety on within-trial update RT and probe response RT were not significant. However, there was a trend such that RT for completion of working memory updates was slower for subjects with higher levels of anxiety symptoms. Results indicate a relationship with trait depression such that that RT was significantly faster when the presented word was not relevant to the categories displayed in current length of trials. Final recall accuracy was high, and there was no association with anxiety or depression. Data collection is ongoing, and these relationships will likely become clearer as sample size increases

Real-Time State Estimation in a Flight Simulator Using fNIRS

Working memory is a key executive function for flying an aircraft. This function is particularly critical when pilots have to recall series of air traffic control instructions. However, working memory limitations may jeopardize flight safety. Since the functional near-infrared spectroscopy (fNIRS) method seems promising for assessing working memory load, our objective is to implement an on-line fNIRS-based inference system that integrates two complementary estimators. The first estimator is a real-time state estimation MACD-based algorithm dedicated to identifying the pilot’s instantaneous mental state (not-on-task vs. on-task). It does not require a calibration process to perform its estimation. The second estimator is an on-line SVM-based classifier that is able to discriminate task difficulty (low working memory load vs. high working memory load). These two estimators were tested with 19 pilots who were placed in a realistic flight simulator and were asked to recall air traffic control instructions. We found that the estimated pilot’s mental state matched significantly better than chance with the pilot’s real state (62% global accuracy, 58% specificity, and 72% sensitivity). The second estimator, dedicated to assessing single trial working memory loads, led to 80% classification accuracy, 72% specificity, and 89% sensitivity. These two estimators establish reusable blocks for further fNIRS-based passive brain computer interface development