Background: We present a method utilizing Healthcare Cost and Utilization Project (HCUP) dataset for predicting disease risk of individuals based on their medical diagnosis history. The presented methodology may be incorporated in a variety of applications such as risk management, tailored health communication and decision support systems in healthcare. Methods: We employed the National Inpatient Sample (NIS) data, which is publicly available through Healthcare Cost and Utilization Project (HCUP), to train random forest classifiers for disease prediction. Since the HCUP data is highly imbalanced, we employed an ensemble learning approach based on repeated random sub-sampling. This technique divides the training data into multiple sub-samples, while ensuring that each sub-sample is fully balanced. We compared the performance of support vector machine (SVM), bagging, boosting and RF to predict the risk of eight chronic diseases. Results: We predicted eight disease categories. Overall, the RF ensemble learning method outperformed SVM, bagging and boosting in terms of the area under the receiver operating characteristic (ROC) curve (AUC). In addition, RF has the advantage of computing the importance of each variable in the classification process. Conclusions: In combining repeated random sub-sampling with RF, we were able to overcome the class imbalance problem and achieve promising results. Using the national HCUP data set, we predicted eight disease categories with an average AUC of 88.79%.

More than a third of elderly fall each year in the United States. It has been shown that the longer the lie on the floor, the poorer is the outcome of the medical intervention. To reduce delay of the medical intervention, we have developed an acoustic fall detection system (acoustic-FADE) that automatically detects a fall and reports it promptly to the caregiver. Acoustic-FADE consists of a circular microphone array that captures the sounds in a room. When a sound is detected, acoustic-FADE locates the source, enhances the signal, and classifies it as “fall” or “nonfall.” The sound source is located using the steered response power with phase transform technique, which has been shown to be robust under noisy environments and resilient to reverberation effects. Signal enhancement is performed by the beamforming technique based on the estimated sound source location. Height information is used to increase the specificity. The mel-frequency cepstral coefficient features computed from the enhanced signal are utilized in the classification process. We have evaluated the performance of acoustic-FADE using simulated fall and nonfall sounds performed by three stunt actors trained to behave like elderly under different environmental conditions. Using a dataset consisting of 120 falls and 120 nonfalls, the acoustic-FADE achieves 100% sensitivity at a specificity of 97%.

To address an aging population, we have been investigating sensor networks for monitoring older adults in their homes. In this paper, we report ongoing work in which passive sensor networks have been installed in 17 apartments in an aging in place eldercare facility. The network under development includes simple motion sensors, video sensors, and a bed sensor that captures sleep restlessness and pulse and respiration levels. Data collection has been ongoing for over two years in some apartments. This longevity in sensor data collection is allowing us to study the data and develop algorithms for identifying alert conditions such as falls, as well as extracting typical daily activity patterns for an individual. The goal is to capture patterns representing physical and cognitive health conditions and then recognize when activity patterns begin to deviate from the norm. In doing so, we strive to provide early detection of potential problems which may lead to serious health events if left unattended. We describe the components of the network and show examples of logged sensor data with correlated references to health events. A summary is also included on the challenges encountered and the lessons learned as a result of our experiences in monitoring aging adults in their homes.

More than one third of about 38 million adults 65 and older fall each year in the United States. To address the above problem we propose to develop an acoustic fall detection system (FADE) that will automatically signal a fall to the monitoring caregiver. As opposed to many existent fall detection systems that require the monitored person to wear devices such as accelerometers or gyroscopes at all times, our system is completely unobtrusive by not requiring any wearable devices. To reduce the false alarm rate we employ an array of acoustic sensors to obtain sound source height information. The sound is considered a false alarm if it comes from a source located at a height higher than 2 feet. We tested our system in a pilot study that consisted of a set of 23 falls performed by a stunt actor during six sessions of about 15 minutes each (1.3 hours in total). The actor was previously trained by our nursing collaborators to fall like an elderly person. The use of height information reduced the false alarm hourly rate from 32 to 5 at a 100% fall detection rate.

The possibility of obtaining lower space harmonic content for the fractional-slot, tooth-concentrated windings by multiplying the number of coils, leading to bigger number of layers in the armature slots is presented. By using multiple layer windings it is possible to reduce or even to cancel some space sub-harmonics having as final result lower eddy current losses induced by the armature reaction in the iron structure of the rotor. The general method of doubling (or trebling) the winding and relatively shifting by one (or more) slots is presented in the paper. Two examples of three phase concentrated windings are presented and analyzed as primitive windings, 12 slots/10poles and 9 slots/8 poles. In the first case the only existing space sub-harmonic is reduced firstly from 35.9% (from fundamental wave) to 9.6% and finally it is canceled by using different number of turns per coils. In the second case the two existing sub-harmonics are reduced and balanced by using the developed method.

When clustering produces more than one candidate to partition a finite set of objects O , there are two approaches to validation (i.e., selection of a “best” partition, and implicitly, a best value for c , which is the number of clusters in O ). First, we may use an internal index, which evaluates each partition separately. Second, we may compare pairs of candidates with each other, or with a reference partition that purports to represent the “true” cluster structure in the objects. This paper generalizes many of the classical indices that have been used with outputs of crisp clustering algorithms so that they are applicable for candidate partitions of any type (i.e., crisp or soft, with soft comprising the fuzzy, probabilistic, and possibilistic cases). Space prevents inclusion of all of the possible generalizations that can be realized this way. Here, we concentrate on the Rand index and its modifications. We compare our fuzzy-Rand index with those of Campello, Hullermeier and Rifqi, and Brouwer, and show that our extension of the Rand index is O(n), while the other three are all O(n ² ). Numerical examples are given to illustrate various facets of the new indices. In particular, we show that our indices can be used, even when the partitions are probabilistic or possibilistic, and that our method of generalization is valid for any index that depends only on the entries of the classical (i.e., four-pair types) contingency table for this problem.