In neurosurgical or orthopedic clinics, the differential diagnosis of lower back pain is often time-consuming and costly. This is especially true when there are several candidate diagnoses with similar symptoms that might confuse clinic physicians. Therefore, methods for the efficient differential diagnosis can help physicians to implement the most appropriate treatment and achieve the goal of pain reduction for their patients.In this study, we applied data-mining techniques from artificial intelligence technologies, in order to implement a computer-aided auxiliary differential diagnosis for a herniated intervertebral disc, spondylolithesis, and spinal stenosis. We collected questionnaires from 361 patients and analyzed the resulting data by using a linear discriminant analysis, clustering, and artificial neural network techniques to construct a related classification model and to compare the accuracy and implementation efficiency of the different methods.Our results indicate that a linear discriminant analysis has obvious advantages for classification and diagnosis, in terms of accuracy.We concluded that the judgment results from artificial intelligence can be used as a reference for medical personnel in their clinical diagnoses. Our method is expected to facilitate the early detection of symptoms and early treatment, so as to reduce the social resource costs and the huge burden of medical expenses, and to increase the quality of medical care.

In centralized-controlled WDM network, the traffic request arrived at the control node will be blocked if the lightpath is failed to be established, and the blocked request will not be issued afterward. In this paper we consider the second time successful ratio of provisioning for the requests which are blocked in first try phase. By recording the minimum shared resource in the wavelength-link table used for survivable routing, the second-tried traffic will have larger successful opportunity to be established. The mechanisms to implement such strategy will be proposed and the performance improvement will be verified by conducted simulations under several assumptions.

Objective: Emergency care is the frontline of the healthcare system. Taiwanese typically seek emergency care when suffering from an acute or unknown illness, which leads to a large number of emergency patients and the related misallocation of nursing manpower, and the excessive workloads of emergency service providers have become serious issues for Taiwan’s medical institutions. Participants: This study conducted purposive sampling and recruited patients and nursing staffs from the emergency room of a medical center in New Taipei City as the research participants. Methods: This study applied the queueing theory and the derived optimal model to solve the problems of excessive workloads for emergency service providers and misallocation of nursing manpower, in an attempt to provide decision makers with more flexible resource allocation and process improvement suggestions. Results: This study analyzed the causes of emergency service overload and identified solutions for improving nursing manpower utilization. Conclusions: A wait-time model and the queueing theory were used to determine resource parameters for the optimal allocation of patient waiting times and to develop the best model for estimating nursing manpower.

Background Advancements in medical care have increased the average life span in many countries, resulting in a generally longer postretirement life span. However, retirees may find it difficult to adapt to retirement. Therefore, encouraging retirees to engage with society is important. Purpose In this study, a senior social participation mobile software application (SSP-App) was developed to stimulate social participation among seniors with the goal of improving their social participation intentions and behaviors. Methods After developing the SSP-App based on user experiences, a quasi-experimental study was conducted. Participants were recruited from the Keelung Ren'ai Community Center. Next, Random Allocation Software Version 1.0.0 software was used to randomly allocate the participants into experimental and control groups. The 54 participants in the experimental group took part in an SSP-App program, whereas the 53 participants in the control group did not participate in any experimental treatment program. Measurements were conducted at Week 4 (T1) and Week 12 (T2) to evaluate the effects. Data were collected using a demographic datasheet, Geriatric Depression Scale-Short Form, Emotional and Social Support Scale, Social Participation Intention Scale, and Social Participation Behavior Scale. The generalized estimating equations method was used to determine intervention effectiveness. Results The SSP-App has six main functions, including an activity partner message board, an activity search function that provides information about different activities, a “Seniors Learning Kiosk” that provides useful information, transportation information, an activity planning and reminder system, and a “First-Aid Station.” Most participants in the SSP-App precursor test expressed approval. At T1, effects were observed in social participation intention only. However, at T2, effects were observed in both social participation intention and social participation behavior. Conclusions/Implications for Practice The SSP-App developed in this study uses information and communication technology and multiple strategies covering information provision, social support, education, and reminders. Social participation obstacles must be overcome to effectively provide seniors with social participation opportunities and improve their social participation.