The open medical informatics journal最新文献

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality. Periodic spirometry is often recommended for individuals with potential occupational exposure to respiratory hazards and in medical treatment of respiratory disease, to prevent COPD or improve treatment outcome. To achieve the full potential of spirometry monitoring in preserving lung function, it is important to maintain acceptable precision of the longitudinal measurements, apply interpretive strategies that identify individuals with abnormal test results or excessive loss of lung function in a timely manner, and use the results for intervention on respiratory disease prevention or treatment modification. We describe novel, easy-to-use visual and analytical software, Spirometry Longitudinal Data Analysis software (SPIROLA), designed to assist healthcare providers in the above aspects of spirometry monitoring. Software application in ongoing workplace spirometry-based medical monitoring programs helped to identify increased spirometry data variability due to deteriorating test quality and subsequent improvement following interventions, and helped to enhance identification of individuals with excessive decline in lung function.

The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. In this study, we have developed and evaluated the performance of multiple unsupervised data mining techniques in cell image segmentation. We adapt four distinctive, yet complementary, methods for unsupervised learning, including those based on k-means clustering, EM, Otsu's threshold, and GMAC. Validation measures are defined, and the performance of the techniques is evaluated both quantitatively and qualitatively using synthetic and recently published real data. Experimental results demonstrate that k-means, Otsu's threshold, and GMAC perform similarly, and have more precise segmentation results than EM. We report that EM has higher recall values and lower precision results from under-segmentation due to its Gaussian model assumption. We also demonstrate that these methods need spatial information to segment complex real cell images with a high degree of efficacy, as expected in many medical informatics applications.

The development of prosthetic hand systems with both decoration and motion functionality for hand amputees has attracted wide research interests. Motion-related myoelectric potentials measured from the surface of upper part of forearms were mostly employed to construct the interface between amputees and prosthesis.However, finger motions, which play a major role in dexterous hand activities, could not be recognized from surface EMG (Electromyogram) signals.The basic idea of this study is to use motion-related surface vibration, to detect independent finger motions without using EMG signals. In this research, accelerometers were used in a finger tapping experiment to collect the finger motion related mechanical vibration patterns. Since the basic properties of the signals are unknown, a norm based, a correlation coefficient based, and a power spectrum based method were applied to the signals for feature extraction. The extracted features were then fed to back-propagation neural networks to classify for different finger motions.The results showed that, the finger motion identification is possible by using the neural networks to recognize vibration patterns.

Mining information from EMG signals to detect complex motion intention has attracted growing research attention, especially for upper-limb prosthetic hand applications. In most of the studies, recordings of forearm muscle activities were used as the signal sources, from which the intention of wrist and hand motions were detected using pattern recognition technology. However, most daily-life upper limb activities need coordination of the shoulder-arm-hand complex, therefore, relying only on the local information to recognize the body coordinated motion has many disadvantages because natural continuous arm-hand motions can't be realized. Also, achieving a dynamical coupling between the user and the prosthesis will not be possible. This study objective was to investigate whether it is possible to associate the around-shoulder muscles' Electromyogram (EMG) activities with the different hand grips and arm directions movements. Experiments were conducted to record the EMG of different arm and hand motions and the data were analyzed to decide the contribution of each sensor, in order to distinguish the arm-hand motions as a function of the reaching time. Results showed that it is possible to differentiate hand grips and arm position while doing a reaching and grasping task. Also, these results are of great importance as one step to achieve a close loop dynamical coupling between the user and the prosthesis.

Migraine is a neurological disorder that correlates with an increased risk of cerebrovascular lesions. Genetic mutations of the MTHFR gene are correlated to migraine and to the increased risk of artery pathologies. Also, migraine patients show altered hematochemical parameters, linked to an impaired platelet aggregation mechanism. Hence, the vascular assessment of migraineurs is of primary importance.Transcranial Doppler sonography (TCD) is used to measure cerebral blood flow velocity (CBFV) and vasomotor reactivity (by an index measured during breath-holding - BHI). Aim of this study was the metabolic profiling of migraine subjects with T/T677-MTHFR and C/T677-MTHFR mutations and its correlation with CBFV and BHI.Metabonomic multidimensional techniques were used to describe and cluster subjects. Fifty women suffering from migraine (age: 18-64; 21 with aura) underwent TCD examination, hematochemical blood analysis, Born test, and genetic tests for MTHFR mutation. Fourteen (7 with aura) had T/T677, 18 (8 with aura) had C/T677, and 18 (6 with aura) had no mutation. The total number of variables was 24.Unsupervised and supervised techniques_showed the correlation between CBFV and BHI with mutation. Discriminant analysis allowed for classifying the subjects with 95.9% sensitivity and 89.0% specificity. Aura was not correlated to mutation or variations of instrumental data.Our study showed that metabonomics could be effectively applied in clinical problems to show the overall correlation structure of complex systems in pathology. Specifically, our results confirmed the importance of TCD in the metabolic profiling and follow-up of migraine patients.