Surge Capacity of Taipei's Regional Emergency Medical System during COVID-19: A System Dynamics Approach.

Background: The community transmission of COVID-19 has caused the breakdown of the regional emergency medical system (REMS), impacting the rights and care of regional patients with acute and severe conditions. This study proposes a model for the surge capacity of REMS to plan for readiness and preparedness during challenging events that overload capacity. Methods: The surge capacity of REMS during the COVID-19 pandemic was studied. The data collection included 26 hospitals that received the data. To simulate the dynamics of Taipei's REMS surge capacity, we observed its ability to treat COVID-19 patients with moderate to severe acute respiratory distress syndrome (ARDS). This will involve monitoring the stock of ventilators, physicians, and nurses within the subsystem loops. Results: Healthcare managers and administrators can use the overload model and hypothetical scenarios to develop new scenarios with different demands on surge capacity. The REMS system capacity model can be used as an aid to guide planning and cross-checking for address Prepare to plan. Conclusions: We combined data regarding the availability of ventilators, physicians, nurses, specialized beds, and general acute care beds in our simulations. Thus, our simulations, with support from a well-established regional command and management structure, could help REMS achieve the optimal surge capacity.

Effects of artificial intelligence-assisted dental monitoring intervention in patients with periodontitis: A randomized controlled trial.

AIM: To evaluate the effects of an at-home artificial intelligence (AI)-assisted dental monitoring application on treatment outcomes in patients with periodontitis. MATERIALS AND METHODS: Participants with periodontitis were recruited and randomly assigned to an AI (n = 16), AI and human counselling (AIHC; n = 17), or control (CG; n = 20) group. All participants received non-surgical periodontal treatment. We employed an AI-assisted tool called DENTAL MONITORING® (DM) intervention, a new technological AI monitoring product that utilizes smartphone cameras for intra-oral scanning and assessment. Patients in the AI and AIHC groups received additional (a) DM or (b) DM, respectively, with real-person counselling over 3 months. Periodontal parameters were collected at baseline and follow-ups. A mixed-design model analysed the follow-up effects over time. RESULTS: The AI and AIHC groups, respectively, exhibited greater improvement in probing pocket depth (PPD) (mean diff = -0.9 ± 0.4 and -1.4 ± 0.3, effect size [ES] = 0.76 and 1.98), clinical attachment level (mean diff = -0.8 ± 0.3 and -1.4 ± 0.3, ES = 0.84 and 1.77), and plaque index (mean diff = -0.5 ± 0.2 and - 0.7 ± 0.2, ES = 0.93 and 1.81) at 3-month follow-up than the CG did. The AIHC group had a greater reduction in PPD (ES = 0.46) and clinical attachment level (ES = 0.64) at the 3-month follow-up compared with the AI group. CONCLUSIONS: Using AI monitoring at home had a positive effect on treatment outcomes for patients with periodontitis. Patients who received AI-assisted health counselling exhibited better treatment outcomes than did patients who received AI monitoring alone.

An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks.

This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting.

A Low Cost VLSI Architecture for Spike Sorting Based on Feature Extraction with Peak Search.

The goal of this paper is to present a novel VLSI architecture for spike sorting with high classification accuracy, low area costs and low power consumption. A novel feature extraction algorithm with low computational complexities is proposed for the design of the architecture. In the feature extraction algorithm, a spike is separated into two portions based on its peak value. The area of each portion is then used as a feature. The algorithm is simple to implement and less susceptible to noise interference. Based on the algorithm, a novel architecture capable of identifying peak values and computing spike areas concurrently is proposed. To further accelerate the computation, a spike can be divided into a number of segments for the local feature computation. The local features are subsequently merged with the global ones by a simple hardware circuit. The architecture can also be easily operated in conjunction with the circuits for commonly-used spike detection algorithms, such as the Non-linear Energy Operator (NEO). The architecture has been implemented by an Application-Specific Integrated Circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture is well suited for real-time multi-channel spike detection and feature extraction requiring low hardware area costs, low power consumption and high classification accuracy.

Immigrant-native differences in caries-related knowledge, attitude, and oral health behaviors: a cross-sectional study in Taiwan.

BACKGROUND: With the growing number of transnational marriages in Taiwan, oral health disparities have become a public health issue. This study assessed immigrant-native differences in oral health behaviors of urban mothers and their children. METHODS: We used the baseline data of an oral health promotion program to examine the immigrant-native differences in caries-related knowledge, attitude, and oral health behaviors. A cross-sectional study was conducted to collect data from mothers in urban area, Taiwan. A total of 150 immigrant and 440 native mothers completed the self-report questionnaires. Logistic regression models analyzed the racial differences in oral health behaviors. RESULTS: Approximately 37% of immigrant mothers used dental floss, 25% used fluoride toothpaste, and only 13.5% of them regularly visited a dentist. Less that 40% of immigrant mothers brush their children's teeth before aged one year, 45% replaced child's toothbrush within 3 months, and only half of the mothers regularly took their child to the dentist. Immigrant mothers had lower level of caries-related knowledge and attitudes than native mothers (p < .001). Compared to native group, the immigrant mothers were less likely to use of dental floss ([Adjusted odds ratio (aOR) =0.35], fluoride toothpaste (aOR = 0.29), visit a dentist in the past 2 years (aOR = 0.26), and take their children to regular dental check-up (aOR = 0.38); whereas, they were more likely to not consume sweeten beverages (aOR = 3.13). CONCLUSIONS: The level of caries-related knowledge, attitudes and oral health behaviors were found lower in immigrant mothers than native ones. The findings suggested cross-cultural caries prevention programs aimed at reducing immigrant-native disparities in child oral health care must be developed for these immigrant minorities.