An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

ICICLE: A semantic-based retrieval system for WWW images

In this paper, we present ICICLE (Image ChainNet and Incremental Clustering Engine), a prototype system that we have developed to efficiently and effectively retrieve WWW images based on image semantics. ICICLE has two distinguishing features. First, it employs a novel image representation model called Weight ChainNet to capture the semantics of the image content. A new formula, called list space model, for computing semantic similarities is also introduced. Second, to speed up retrieval, ICICLE employs an incremental clustering mechanism, ICC (Incremental Clustering on ChainNet), to cluster images with similar semantics into the same partition. Each cluster has a summary representative and all clusters' representatives are further summarized into a balanced and full binary tree structure. We conducted an extensive performance study to evaluate ICICLE. Compared with some recently proposed methods, our results show that ICICLE provides better recall and precision. Our clustering technique ICC facilitates speedy retrieval of images without sacrificing recall and precision significantly.

Improving learning in undergraduate control engineering courses using context-based learning models

Control Engineering is an essential part of university electrical engineering education. Normally, a control course requires considerable mathematical as well as engineering knowledge and is consequently regarded as a difficult course by many undergraduate students. From the academic point of view, how to help the students to improve their learning of the control engineering knowledge is therefore an important task which requires careful planning and innovative teaching methods. Traditionally, the didactic teaching approach has been used to teach the students the concepts needed to solve control problems. This approach is commonly adopted in many mathematics intensive courses; however it generally lacks reflection from the students to improve their learning. This paper addresses the practice of action learning and context-based learning models in teaching university control courses. This context-based approach has been practised in teaching several control engineering courses in a university with promising results, particularly in view of student learning performances.

Hydrogen storage of magnesium-based nanocomposites system

Hydrogen storage in traditional metallic hydrides can deliver about 1.5 to 2.0 wt pct hydrogen but magnesium hydrides can achieve more than 7 wt pct. However, these systems suffer from high temperature release drawback and chemical instability problems. Recently, big improvements of reducing temperature and increasing kinetics of hydrogenation have been made in nanostructured Mg-based composites. This paper aims to provide an overview of the science and engineering of Mg materials and their nanosized composites with nanostructured carbon for hydrogen storage. The needs in research including preparation of the materials, processing and characterisation and basic mechanisms will be explored. The preliminary experimental results indicated a promising future for chemically stable hydrogen storage using carbon nanotubes modified metal hydrides under lower temperatures.

An Internet-based telerehabilitation system for the assessment of motor speech disorders: A pilot study

Purpose: This pilot study explored the feasibility and effectiveness of an Internet-based telerehabilitation application for the assessment of motor speech disorders in adults with acquired neurological impairment. Method: Using a counterbalanced, repeated measures research design, 2 speech-language pathologists assessed 19 speakers with dysarthria on a battery of perceptual assessments. The assessments included a 19-item version of the Frenchay Dysarthria Assessment (FDA; P. Enderby, 1983), the Assessment of Intelligibility of Dysarthric Speech (K. M. Yorkston & D. R. Beukelman, 1981), perceptual analysis of a speech sample, and an overall rating of severity of the dysarthria. One assessment was conducted in the traditional face-to-face manner, whereas the other assessment was conducted using an online, custom-built telerehabilitation application. This application enabled real-time videoconferencing at 128 kb/s and the transfer of store-and-forward audio and video data between the speaker and speech-language pathologist sites. The assessment methods were compared using the J.M.Bland and D.G.Altman (1986, 1999) limits-of-agreement method and percentage level of agreement between the 2 methods. Results: Measurements of severity of dysarthria, percentage intelligibility in sentences, and most perceptual ratings made in the telerehabilitation environment were found to fall within the clinically acceptable criteria. However, several ratings on the FDA were not comparable between the environments, and explanations for these results were explored. Conclusions: The online assessment of motor speech disorders using an Internet-based telerehabilitation system is feasible. This study suggests that with additional refinement of the technology and assessment protocols, reliable assessment of motor speech disorders over the Internet is possible. Future research methods are outlined.

An analysis of the images attached to referral messages in an e-mail based telemedicine system for developing countries

Little is known about the quality of the images transmitted in email telemedicine systems. The present study was designed to survey the quality of images transmitted in the Swinfen Charitable Trust email referral system. Telemedicine cases were examined for a 3 month period in 2002 and a 3 month period in 2006. The number of cases with images attached increased from 8 (38%) to 37 (53%). There were four types of images (clinical photographs, microscope pictures, notes and X-ray images) and the proportion of radiology images increased from 27 to 48%. The cases in 2002 came from four different hospitals and were associated with seven different clinical specialties. In 2006, the cases came from 19 different hospitals and 20 different specialties. The 46 cases (from both study periods) had a total of 159 attached images. The quality of the images was assessed by awarding each image a score in four categories: focus, anatomical perspective, composition and lighting. The images were scored on a five-point scale (1 = very poor to 5 =very good) by a qualified medical photographer. In comparing image quality between the two study periods, there was some evidence that the quality had reduced, although the average size of the attached images had increased. The median score for all images in 2002 was 16 (interquartile range 14-19) and the median score in 2006 was 15 (13-16). The difference was significant (P < 0.001, Mann-Whitney test).