The Impact of Equating on Detection of Treatment Effects

Equating makes it possible to compare performances on different forms of a test. Three different equating methods (baseline selection, subgroup, and subscore equating) using common-item item response theory equating were examined for their impact on detection of treatment effects in multilevel models

Validation of the influence of biosignals on performance of machine learning algorithms for sleep stage classification

Background Sleep stage identification is critical in multiple areas (e.g. medicine or psychology) to diagnose sleep-related disorders. Previous studies have reported that the performance of machine learning algorithms can be changed depending on the biosignals and feature-extraction processes in sleep stage classification. Methods To compare as many conditions as possible, 414 experimental conditions were applied, considering the combination of different biosignals, biosignal length, and window length. Five biosignals in polysomnography (i.e. electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), electrooculogram left, and electrooculogram right) were used to identify optimal signal combinations for classification. In addition, three different signal-length conditions and six different window-length conditions were applied. The validity of each condition was examined via classification performance from the XGBoost classifiers trained using 10-fold cross-validation. Furthermore, results considering feature importance were examined to validate the experimental results in terms of model explanation. Results The combination of EEG + EMG + ECG with a 40 s window and 120 s signal length resulted in the best classification performance (precision: 0.853, recall: 0.855, F1-score: 0.853, and accuracy: 0.853). Compared to other conditions and feature importance results, EEG signals showed a relatively higher importance for classification in the present study. Conclusion We determined the optimal biosignal and window conditions for the feature-extraction process in machine learning algorithm-based sleep stage classification. Our experimental results inform researchers in the future conduct of related studies. To generalize our results, more diverse methodologies and conditions should be applied in future studies

Anomaly Detection Framework for Thermal Image Dataset Acquired by Mobile Robots

In this paper, we propose an anomaly detection framework that can analyze thermal image data acquired by mobile inspection robots. The proposed framework includes image alignment algorithm (RANSAC-flow) to handle wobbling image data and extract time-series temperature data from the successive thermal images. Also, GAN-based anomaly detection algorithm is integrated in the framework to analyze the time-series temperature data and generate alarm. The proposed framework can be a general solution for the increasing demand on autonomous safety monitoring robots.2

Safety, tolerability, pharmacokinetics and pharmacodynamics of multiple ascending doses of the novel long-acting glucagon analogue HM15136 in overweight and obese patients with co-morbidities

Aim To evaluate the safety, tolerability, pharmacokinetics and pharmacodynamics of multiple ascending doses of the novel long-acting glucagon analogue HM15136 in overweight/obese patients with co-morbidities, with and without type 2 diabetes (T2D).Materials and Methods This was a phase 1, double-blind, randomized, placebo-controlled, two-part trial with a 12-week treatment period of once-weekly subcutaneous HM15136 (0.02/0.04/0.06 mg/kg). Part 1 included patients with dyslipidaemia and/or hypertension and no T2D. Part 2 included patients with dyslipidaemia and/or hypertension plus T2D.Results In part 1, 23/27 (85.2%) patients receiving HM15136 and all patients receiving placebo (9/9 [100%]) experienced a treatment-emergent adverse event (TEAE). Five of 27 (18.5%) patients receiving HM15136 developed anti-HM15136 antibodies. Dose-dependent increases in mean HM15136 serum concentration and fasting plasma glucose (FPG) were observed, as were dose-dependent weight reductions of 0.5%/2.3%/2.6% at doses of 0.02/0.04/0.06 mg/kg, respectively. In part 2, 8/12 (66.7%) patients receiving HM15136 and all patients receiving placebo (4/4 [100.0%]) reported a TEAE. Two (16.7%) patients developed anti-HM15136 antibodies. Dose-dependent increases in mean HM15136 serum concentration were observed. FPG of more than 200 mg/dL was reported in 4/9 (44.4%) and 2/3 (66.7%) patients receiving 0.02 and 0.06 mg/kg, respectively. The 0.06 mg/kg dose was not tolerated in part 2 because of hyperglycaemia. Patients receiving 0.02 mg/kg showed a 0.9% weight reduction. No serious TEAEs leading to discontinuation were reported in either study part.Conclusions This study of HM15136 provides a preliminary safety and tolerability profile with initial insights into its efficacy profile

Integrating a Statistical Topic Model and a Diagnostic Classification Model for Analyzing Items in a Mixed Format Assessment

Selected response items and constructed response (CR) items are often found in the same test. Conventional psychometric models for these two types of items typically focus on using the scores for correctness of the responses. Recent research suggests, however, that more information may be available from the CR items than just scores for correctness. In this study, we describe an approach in which a statistical topic model along with a diagnostic classification model (DCM) was applied to a mixed item format formative test of English and Language Arts. The DCM was used to estimate students’ mastery status of reading skills. These mastery statuses were then included in a topic model as covariates to predict students’ use of each of the latent topics in their written answers to a CR item. This approach enabled investigation of the effects of mastery status of reading skills on writing patterns. Results indicated that one of the skills, Integration of Knowledge and Ideas, helped detect and explain students’ writing patterns with respect to students’ use of individual topics