Development of a Pulsed Xenon Ultraviolet Disinfection Device for Real-Time Air Disinfection in AmbulancesRead the full article
Journal of Healthcare Engineering provides a vehicle for the exchange of advanced knowledge, emerging technologies, and innovative ideas related to all aspects of engineering involved in healthcare delivery processes and systems.
Chief Editor, Professor Zollo, has research expertise in euro-robotics and biomedical technologies for neuroscience, rehabilitation and assistance robotics, and robotic and mechatronic devices for personal assistance and service robotics.
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Second-Generation Sequencing with Deep Reinforcement Learning for Lung Infection Detection
Recently, deep reinforcement learning, associated with medical big data generated and collected from medical Internet of Things, is prospective for computer-aided diagnosis and therapy. In this paper, we focus on the application value of the second-generation sequencing technology in the diagnosis and treatment of pulmonary infectious diseases with the aid of the deep reinforcement learning. Specifically, the rapid, comprehensive, and accurate identification of pathogens is a prerequisite for clinicians to choose timely and targeted treatment. Thus, in this work, we present representative deep reinforcement learning methods that are potential to identify pathogens for lung infection treatment. After that, current status of pathogenic diagnosis of pulmonary infectious diseases and their main characteristics are summarized. Furthermore, we analyze the common types of second-generation sequencing technology, which can be used to diagnose lung infection as well. Finally, we point out the challenges and possible future research directions in integrating deep reinforcement learning with second-generation sequencing technology to diagnose and treat lung infection, which is prospective to accelerate the evolution of smart healthcare with medical Internet of Things and big data.
Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson’s Patients
In the last few years, the importance of measuring gait characteristics has increased tenfold due to their direct relationship with various neurological diseases. As patients suffering from Parkinson’s disease (PD) are more prone to a movement disorder, the quantification of gait characteristics helps in personalizing the treatment. The wearable sensors make the measurement process more convenient as well as feasible in a practical environment. However, the question remains to be answered about the validation of the wearable sensor-based measurement system in a real-world scenario. This paper proposes a study that includes an algorithmic approach based on collected data from the wearable accelerometers for the estimation of the gait characteristics and its validation using the Tinetti mobility test and 3D motion capture system. It also proposes a machine learning-based approach to classify the PD patients from the healthy older group (HOG) based on the estimated gait characteristics. The results show a good correlation between the proposed approach, the Tinetti mobility test, and the 3D motion capture system. It was found that decision tree classifiers outperformed other classifiers with a classification accuracy of 88.46%. The obtained results showed enough evidence about the proposed approach that could be suitable for assessing PD in a home-based free-living real-time environment.
Empirical Study on the Transparency of Security Risk Information in Chinese Listed Pharmaceutical Enterprises Based on the ANP-DS Method
Frequent outbreaks of drug safety incidents pose a massive threat to public health and safety, while the transparency of security risk information in medical enterprises is not optimistic. Therefore, this study uses the analytic network process (Dempster-Shafer method) to construct a transparent comprehensive evaluation model for security risk information in listed pharmaceutical enterprises from the perspective of government supervision and listed pharmaceutical enterprises. On the basis of 59,305 data obtained by 303 enterprises listed in the Chinese biomedical sector, this research conducted an empirical study on the transparency of safety risk information in Chinese listed pharmaceutical enterprises. The current study found that the transparency of security risk information in Chinese listed pharmaceutical enterprises is generally between “general” and “relatively good” and tends to be “relatively good.” However, administrative punishment information, adverse drug reaction reporting systems, and production processes need continuous improvement.
Studying the Optical 3D Accuracy of Intraoral Scans: An In Vitro Study
There are various scanners available in dental practice with various accuracies. The aim of this study was to compare the 3D capturing accuracy of scans obtained from Trios 3 and Dental Wings scanner. A reference mandibular model was printed from FormLab with reference points in three axes (X, Y, and XY and Z). The printed model was scanned 5 times with 3 scans: normal scan by Trios 3 (Trios 3A), high-resolution scan by Trios 3 (Trios 3B), and normal scan by Dental Wings. After scan, the stereolithography (stl) files were generated. Then, the measurements were made from the computer software using Rhinoceros 3D (Rhino, Robert McNeel & Associates for Windows, Washington DC, USA). The measurements made with digital caliper were taken as control. Statistical analysis was done using one-way ANOVA with post hoc using Sheffe (). Trios 3 presented higher accuracy than Dental Wings and high resolution showed better results. The Dental Wings showed less accuracy at the measurements >50 mm of length and >30 mm in width. There was no significant difference () of control with the Trios 3A and Trios 3B. Similarly, for the measurements in Z-axis, there was no significant difference of control with each scan (Trios 3A, Trios 3B, and Dental Wings). Accuracy of the scan is affected by the length of the scanning area and scanning pattern. It is less recommended to Dental Wings scan >3-unit prosthesis and that crosses the midline.
Effects of Video-Game Based Therapy on Balance, Postural Control, Functionality, and Quality of Life of Patients with Subacute Stroke: A Randomized Controlled Trial
Purpose. To determine the effects of a structured protocol using commercial video games on balance, postural control, functionality, quality of life, and level of motivation in patients with subacute stroke. Methods. A randomized controlled trial was conducted. A control group (n = 25) received eight weeks of conventional rehabilitation consisting of five weekly sessions based on an approach for task-oriented motor training. The experimental group (n = 23) received conventional rehabilitation + video-game based therapy for eight weeks with commercial video games using the Xbox 360° video games console and the Kinect® device with the same total treatment time for both groups. The Modified Rankin Scale, Barthel Index, Tinetti scale, Functional Reach test, Get Up and Go test, Baropodometry, EuroQoL 5D (EQ-5D), satisfaction, adherence, and motivation were used as outcome measures. Results. In the between-group comparison, statistically significant differences were observed in the Modified Rankin scores (), the Barthel Index (), the Tinetti gait assessment (), the Functional Reach test (), the Get Up and Go test (), the pain/discomfort dimension (), and anxiety/depression dimension () of the EQ-5D and the VAS (visual analog scale) () on the perceived health status based on the EQ-5D questionnaire. Regarding the scale of motivation, self-esteem, and adherence, statistically significant differences were achieved in motivation (), self-esteem (), and adherence () variables. Conclusion. A protocol of semi-immersive video-game based therapy, combined with conventional therapy, may be effective for improving balance, functionality, quality of life, and motivation in patients with subacute stroke. This trial is registered with NCT03528395.
Dynamic Modeling and Simulation of a Body Weight Support System
This paper proposes a body weight support (BWS) system with a series elastic actuator (SEA) to facilitate walking assistance and motor relearning during gait rehabilitation. This system comprises the following: a mobile platform that ensures movement of the system on the ground, a BWS mechanism with an SEA that is capable of providing the desired unloading force, and a pelvic brace to smooth the pelvis motions. The control of the body weight support is realized by an active weight-offload method, and a dynamic model of the BWS system with offload mass of a human is conducted to simulate the control process and optimize the parameters. Preliminary results demonstrate that the BWS system can provide the desired support force and vertical motion of the pelvis.