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Electrocardiogram (ECG) signal represents autonomous nervous system responses to human emotional states. This research demonstrates that the spectral ECG features within ultra-short-term window duration (10-sec) could be utilized to monitor human emotional states. Experiments were conducted with five different stress protocols including mental and physical tasks. Experimental results showed feasible classification performance of ECG spectral features compared to that of HRV parameters. The averaged classification accuracy across 13 subjects and all stress protocols was 81.16% using Naïve Bayes algorithm. In addition, the results showed stress responses in mental arithmetic tasks was the most separable from those in resting states (87.31%) compared to the other stress situations.

<p>The indiscriminate convergence of technologies makes prediction difficult and can cause many difficulties in technology investment. This makes it difficult to choose capital investment and can induce excessive investment in inefficient technologies. Therefore, analyzing the trend of convergence technology and predicting a highly influential convergence area in the future can induce effective investment, and lead the highly influential technology to achieve great technological development. The purpose of this paper is to analyze technologies that are expected to have high influence in the future through prediction of major fusion areas and to present fusion areas that can be used as indicators of investment. The proposed mechanism selected four prominent journals in the security area and collected metadata to generate a dataset in terms of technological excellence and a dataset in terms of commercialization through patent metadata collection. Thereafter, a process of extracting a main keyword according to a topic from a metadata set by applying a Latent Dirichlet Allocation (LDA) is performed. The extracted topics and keywords are not related to topics and keywords of other years. Therefore, a dynamic topic model (DTM) is applied to analyze the trend of the extracted topics and perform prediction. DTM analyzes the topics in the fusion area classified by LDA and the trend of changing topics linked by year for each topic keyword. Finally, the association of the fusion region is analyzed to derive a fusion region with high influence. These results are believed to be used as an indicator of effective technology investment by providing a high impact area in the convergence area of cybersecurity.</p> <p>&nbsp;</p>

BACKGROUND The early prediction of antibiotic resistance in patients with urinary tract infection is important to guide appropriate antibiotic therapy selection. OBJECTIVE In the present study, we aimed to predict antibiotic resistance in patients with urinary tract infection. Additionally, we aimed to interpret the machine learning models we developed. METHODS We used admission, diagnosis, prescription, and microbiology records of patients who underwent urine culture tests in Yongin Severance Hospital, South Korea. We developed 5 sub-models to classify urinary tract infection pathogens as either sensitive or resistant to cephalosporin, piperacillin/tazobactam, trimethoprim/sulfamethoxazole, fluoroquinolone, and carbapenem. To analyze how each variable contributed to the machine learning model’s predictions of antibiotic resistance, we used the SHapley Additive exPlanations method. Finally, we proposed a prototype machine learning based clinical decision support system to provide clinicians the resistance probabilities for each antibiotic. RESULTS The area under the curve values ranged from 0.710 to 0.826 in the training set and 0.642 to 0.812 in the test set for predicting antibiotic resistance. The administration of drugs before infection and exposure time to these drugs were found to be important variables for predicting antibiotic resistance. CONCLUSIONS The study results demonstrated the potential of machine learning to predict antibiotic resistance in patients with urinary tract infection. Machine learning can assist clinicians in making decisions regarding the selection of appropriate antibiotic therapy in patients with urinary tract infection.