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Biomedical subjects

Yu-Chuan Li

Publications and source records attributed to Yu-Chuan Li.

At least 19 recordsLinked to original sources
PubMed2007

Neuro-fuzzy technology as a predictor of parathyroid hormone level in hemodialysis patients.

Measuring the plasma parathyroid hormone (PTH) concentration is crucial to evaluate renal bone disease in patients with renal failure. Although frequent measurement is needed to avoid inadequate prescription of phosphate binders and vitamin D preparations, artificial intelligence can repeatedly perform the forecasting tasks and may be a satisfactory substitute for laboratory tests. Neuro-fuzzy technology represents a promising forecasting application in clinical medicine. We therefore constructed a coactive neuro-fuzzy inference system (CANFIS) to predict plasma PTH concentrations in hemodialysis patients. The CANFIS was constructed with clinical parameters (patient age, plasma albumin, calcium, phosphorus, alkaline phosphatase, and calcium-phosphorus product) from a cohort of hemodialysis patients, and plasma PTH concentration measured by radioimmunoassay (RIA) was the supervised outcome. The accuracy of the CANFIS was prospectively compared with RIA in another hospital. Plasma PTH concentrations measured by RIA and predicted by CANFIS were 179.04 +/- 38.18 ng/l and 179.34 +/- 37.76 ng/l, respectively (p = 0.15). The CANFIS was able to precisely estimate plasma PTH concentrations in hemodialysis patients. These results suggest that the neuro-fuzzy technology, based on limited clinical parameters, is an excellent alternative to RIA for accurately predicting plasma PTH concentration in hemodialysis patients.

Aged↗
PubMed2006

Mobile information and communication in the hospital outpatient service.

OBJECTIVES: Most healthcare providers provide mobile service for their medical staff; however, few healthcare providers provide mobile service as part of their outpatient service. The mobile outpatient service system (MOSS) focuses on illness treatment, illness prevention and patient relation management for outpatient service users. Initiated in a local hospital in Taiwan, the MOSS pilot project was developed to improve outpatient service quality and pursue higher patient safety. METHOD: This study focuses on the development of the MOSS. The workflow, architecture and target users of the MOSS are delineated. In addition, there were two surveys conducted as part of this study. After a focus group of medical staff identified areas in which outpatient services might be improved by the MOSS, the first survey was administered to outpatients to confirm the focus group's intuitions. The second administration of the survey explored outpatient satisfaction after they used the MOSS service. RESULTS: With regard to outpatient attitudes, about 93% of participants agreed that the mobile outpatient service improved outpatient service quality. In the area of outpatient satisfaction, about 89% of participants indicated they were satisfied with the mobile outpatient service. DISCUSSION/CONCLUSION: Supported by our study finding, we propose that more diverse mobile outpatient services can be provided in the future.

Adult↗
PubMed2006

Safety portal: the safest goes through the air--ubiquitous high-risk reminders bridging out the patient safety in emergency department.

UNLABELLED: Under the safety net of hospital-based patient safety informatics (PSI) system, RFID is build to adapt emergency department safety needs and the process improvement activities. We implemented the RFID framework as the electronic patient identifier into the process improvement of ED workflow; it defined as the safety portal to introduce PSI safety features on the real-time basis. METHODS: Since 2004, we were applying RFID technology into a 200 daily visits emergency department of the regional medical center in Taipei, Taiwan. We then developed wireless web-based RFID safety portal to implement the real-time safety reminders such as the laboratory and radiology reports to the physician who can make decision promptly to the patients in the ED. RESULTS: Under the ED safety portal, the diagnosis time for physicians to make clinical decision is largely reduced nearly 40 percent due to the safety enhancement RFID system. ED physician who could actively receive patient's updated clinical data to make clinical decision via web-based informatics system reasonably decreases. CONCLUSIONS: The effectiveness of RFID system not only enhance patient identification during ED process but combine ED safety net which providing needed data for ED physician and staff who visit ED patients with valuable real-time data on time. Patient safety on ED can be clearly improved from the embracing modern technology and build up a patient centered ED environment.

Emergency Service, Hospital↗
PubMed2006

Applying an artificial neural network to predict osteoporosis in the elderly.

Osteoporosis is an essential index of health and economics in every country. Recognizing asymptomatic elderly population with high risks of osteoporosis remains a difficult challenge. For this purpose, we developed and validated an artificial neural network (ANN) to identify the osteoporotic subjects in the elderly. The study population consisted of 1403 elderly adults (mean age 63.50 +/- 0.24 years ranged from 50 to 91 years old, 157 male and 1246 female) randomly selected into 3 sets, 703 participants in training set, 350 participants in selection set, and the remaining 350 participants in test set. The input variables included demographic characteristics, anthropometric measurements, and clinical data. The outcome variable was dichotomous, either non-osteoporotic (T-score of greater than -2.5) or osteoporotic (T-score of -2.5 or less) groups classified by the measurement from dual energy X-ray absorptiometry. ANN was constructed with data from training and selection sets and validated in test set whose outcome variable was unknown to the network. The performance of ANN was evaluated by discrimination and calibration simultaneously. After training processes, the final best ANN was a multilayer perceptron network which determined seven input variables (gender, age, weight, height, body mass index, postmenopausal status, and coffee consumption) as significant features. The discriminatory power of ANN for test set was excellent (area under receiver operating characteristics curve = 0.82 +/- 0.03). ANN also had statistically good fit represented by statistically insignificant Hosmer-Lemeshow statistic (p = 0.24). These results suggested that our final ANN concurrently had good discriminatory power and good-fit calibration. ANN can be used as a promising tool for the elderly to stratify high risk subjects into osteoporotic group.

Aged↗
PubMed2005

Applying an artificial neural network to predict total body water in hemodialysis patients.

BACKGROUND: Estimating total body water (TBW) is crucial in determining dry weight and dialytic dose for hemodialysis patients. Several anthropometric equations have been used to predict TBW, but a more accurate method is needed. We developed an artificial neural network (ANN) to predict TBW in hemodialysis patients. METHODS: Demographic data, anthropometric measurements, and multifrequency bioelectrical impedance analysis (MF-BIA) were investigated in 54 patients. TBW measured by MF-BIA (TBW-BIA) was the reference. The predictive value of TBW based on ANN and five anthropometric equations (58% of actual body weight, Watson formula, Hume formula, Chertow formula, and Lee formula) was evaluated. RESULTS: Predictive TBW values derived from anthropometric equations were significantly higher than TBW-BIA (31.341 +/- 6.033 liters). The only non-significant difference was between TBW-ANN (31.468 +/- 5.301 liters) and TBW-BIA (p = 0.639). ANN had the strongest Pearson's correlation coefficient (0.911) and smallest root mean square error (2.480); its peak centered most closely to zero with the shortest tails in an empirical cumulative distribution plot when compared with the other five equations. CONCLUSION: ANN could surpass traditional anthropometric equations and serve as a feasible alternative method of TBW estimation for chronic hemodialysis patients.

Adult↗
PubMed2005

Outcome prediction after moderate and severe head injury using an artificial neural network.

Many studies have constructed predictive models for outcome after traumatic brain injury. Most of these attempts focused on dichotomous result, such as alive vs dead or good outcome vs poor outcome. If we want to predict more specific levels of outcome, we need more sophisticated models. We conducted this study to determine if artificial neural network modeling would predict outcome in five levels of Glasgow Outcome Scale (death, persistent vegetative state, severe disability, moderate disability, and good recovery) after moderate to severe head injury. The database was collected from a nation-wide epidemiological study of traumatic brain injury in Taiwan from July 1, 1995 to June 30, 1998. There were total 18583 records in this database and each record had thirty-two parameters. After pruning the records with minor cases (GCS 13) and missing data in the 132 variables, the number of cases decreased from 18583 to 4460. A step-wise logistic regression was applied to the remaining data set and 10 variables were selected as being statically significant in predicting outcome. These 10 variables were used as the input neurons for constructing neural network. Overall, 75.8% of predictions of this model were correct, 14.6% were pessimistic, and 9.6% optimistic. This neural network model demonstrated a significant difference of performance between different levels of Glasgow Outcome Scale. The prediction performance of dead or good recovery is best and the prediction of vegetative state is worst. An artificial neural network may provide a useful "second opinion" to assist neurosurgeon to predict outcome after traumatic brain injury.

Brain Injuries↗
PubMed2005

Pervasive Observation Medicine: The Application of RFID to Improve Patient Safety in Observation Unit of Hospital Emergency Department.

Over the past decade, observation medicine has become an important component of emergency medicine. There are several settings in which observation medicine has been useful and valuable.(1) RFID as the patient identification, not only generates the on-line laboratory data and radiology report via hand-held wireless PDA, this RFID system help physician stream-line patient admission to acute bed or ICU in the emergency department more effectively.

Emergency Medicine↗
PubMed2005

Using mobile technology to improve healthcare service quality.

Improving healthcare service quality for illness of treatment, illness prevention and patient service is difficult for most hospitals because the hospitals are lack adequate resources and labor. In order to provide better healthcare service quality for patients, mobile technology can be used to manage healthcare in a way that provides the optimal healthcare service for patients. Pursuing utilization of mobile technology for better patient service, Taipei Medical University Municipal W. F. Teaching Hospital has implemented a mobile healthcare service (m-HS) system to increase healthcare service quality. The m-HS system improves the quality of medical care as well as healthcare service. The m-HS is a multi-functional healthcare management agent, meets the mobile tendency of the present society. This study seeks to discuss the m-HS architecture and workflow processes. We believe the m-HS does have the potential to improve healthcare service quality. Finally, the conclusions and suggestions for the m-HS are given.

Computer Systems↗
PubMed2005

The evaluation of Game-Based e-learning for medical education: a preliminary survey.

Game-Based e-learning (GBeL) was newly designed platform for education of students with higher education, especially for medical students. The purpose of this study was to preliminarily evaluate the attraction of GBeL, the motivation toward GBeL and the learning effect. We found more than 80% of students thought that GBeL was attractive and more interesting than traditional class. However, the percentage of enhancing interest to learn and learning more complete and deep knowledge were less than 50%.

Computer-Assisted Instruction↗