Anxiety and depression are two important mental health problems among the geriatric population. They are often undiagnosed and directly or indirectly responsible for various morbidities. Early and timely diagnosis has immense effect on appropriate management of anxiety and depression along with its co‐morbidities. Owing to time constraint and enormous patient load, especially in developing county such as India it is hardly possible for a physician or surgeon to identify a geriatric patient suffering from anxiety and depression using any psychometric analysis tool. So, it is of utmost importance to develop a predictive model for automated diagnosis of anxiety and depression among them. This Letter aims to develop an appropriate predictive model, to diagnose anxiety and depression among older patient from socio‐demographic and health‐related factors, using machine learning technology. Ten classifiers were evaluated with a data set of 510 geriatric patients and tested with ten‐fold cross‐validation method. Highest prediction accuracy of 89% was obtained with random forest (RF) classifier. This RF model was tested with another data set from separate 110 older patients for its external validity. Its predictive accuracy was found to be 91% and false positive (FP) rate was 10%, compared with gold standard tool.
Automated health monitoring and alert system development is a demanding research area today. Most of the currently available monitoring and controlling medical devices are wired which limits freeness of working environment. Wireless sensor network (WSN) is a better alternative in such an environment. Neonatal intensive care unit is used to take care of sick and premature neonates. Hypothermia is an independent risk factor for neonatal mortality and morbidity. To prevent it an automated monitoring system is required. In this Letter, an automated neonatal health monitoring system is designed using sensor mobile cloud computing (SMCC). SMCC is based on WSN and MCC. In the authors' system temperature sensor, acceleration sensor and heart rate measurement sensor are used to monitor body temperature, acceleration due to body movement and heart rate of neonates. The sensor data are stored inside the cloud. The health person continuously monitors and accesses these data through the mobile device using an Android Application for neonatal monitoring. When an abnormal situation arises, an alert is generated in the mobile device of the health person. By alerting health professional using such an automated system, early care is provided to the affected babies and the probability of recovery is increased.
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