The preliminary findings emphasize the advantages of wearable balance telerehabilitation technologies when performing in-home balance rehabilitation exercises.While there have been several efforts to use mHealth technologies to support asthma management, none so far offer personalised algorithms that can provide real-time feedback and tailored advice to patients based on their monitoring. This work employed a publicly available mHealth dataset, the Asthma Mobile Health Study (AMHS), and applied machine learning techniques to develop early warning algorithms to enhance asthma self-management. The AMHS consisted of longitudinal data from 5,875 patients, including 13,614 weekly surveys and 75,795 daily surveys. We applied several well-known supervised learning algorithms (classification) to differentiate stable and unstable periods and found that both logistic regression and naïve Bayes-based classifiers provided high accuracy (AUC > 0.87). We found features related to the use of quick-relief puffs, night symptoms, frequency of data entry, and day symptoms (in descending order of importance) as the most useful features to detect early evidence of loss of control. We found no additional value of using peak flow readings to improve population level early warning algorithms.Accurate cancer patient prognosis stratification is essential for oncologists to recommend proper treatment plans. Deep learning models are capable of providing good prediction power for such stratification. The main challenge is that only a limited number of labeled patients are available for cancer prognosis. To overcome this, we proposed Wasserstein Generative Adversarial Network-based Deep Adversarial Data Augmentation (wDADA) that leverages generative adversarial networks to perform data augmentation and assist in model training. We used the proposed framework to train our model for predicting disease-specific survival (DSS) of breast cancer patients from the METABRIC dataset. We found that wDADA achieved 0.6726± 0.0278, 0.7538±0.0328, and 0.6507 ±0.0248 in terms of accuracy, AUC, and concordance index in predicting 5-year DSS, respectively, which is comparable to our previously proposed Bimodal model (accuracy 0.6889±0.0159; AUC 0.7546± 0.0183; concordance index 0.6542±0.0120), which needs careful calibration and extensive search on pre-trained network architectures. The flexibility of the proposed wDADA allows us to incorporate it with ensemble learning and semi-supervised learning to further improve performance. Our results indicate that it is possible to utilize generative adversarial networks to train deep models in medical applications, wherein only limited data are available.It is necessary to know the amount of food on dishes in order to encourage taking medicine after eating. Also, for health management, it is vital to record what and how much a person ate. Although there are research cases using weight sensors or color cameras, it has been difficult to estimate the food volume accurately and inexpensively at home. In previous works, the authors developed a technique for estimating volume based on a depth image acquired by a depth camera. In this paper, the authors propose a new point cloud processing method for a more accurate estimation. A point cloud is a set of coordinate points on objects and is suitable for processing objects three-dimensionally. The authors have developed a technique for recognizing dishes on the dining table based on a point cloud and constructing the dish space. Additionally, another technique was developed for estimating the volume of food in the dish space.Evidence-based medicine is a major evolution in the way medical practice and reasoning are structured. This approach aims at guiding patient care through rigorous, explicit and judicious evidences. In this contribution, we present the case study of the deployment of a smartphone-based system to manage clinical pathways and its impact during two years in the pediatric department of the university hospital of Rennes, France. We also tackle smartphone acceptability and easiness of use by pediatricians.The Clock Drawing Test, where the participant is asked to draw a clock from memory and copy a model clock, is widely used for screening of cognitive impairment. The digital version of the clock test, the digital clock drawing test (dCDT), employs accelerometer and pressure sensors of a digital pen to capture time and pressure information from a participant's performance in a granular digital format. While visual features of the clock drawing test have previously been studied, little is known about the relationship between demographic and cognitive impairment characteristics with dCDT latency and graphomotor features. Here, we examine dCDT feature clusters with respect to sociodemographic and cognitive impairment outcomes. https://www.selleckchem.com/products/U0126.html Our results show that the clusters are not significantly different in terms of age and gender, but did significantly differ in terms of education, Mini-Mental State Exam scores, and cognitive impairment diagnoses.This study shows that features extracted from digital clock drawings can provide important information regarding cognitive reserve and cognitive impairments.Brain connectivity analysis is a new multidisciplinary approach in neuroscience for determining neurological disorders from brain imaging data. But, there is no end-to-end toolchain that processes raw MRI data and extracts brain connectivity network metrics. Again, the existing method of cortical parcellation from MRI data is mainly based on fixed Brodmann atlas; which does not support neonate's brain or adult's brain with neuroplasticity anomalies. In this work, we design an end-to-end toolchain that processes raw MRI data and generates network metrics for brain connectivity analysis using non-anatomical equal-area parcellation. We process the structural and diffusion MRI data to generate the parcellated and segmented image, extract white matter tracks and build structural connectome and then interface it with Brain Connectivity Toolbox to extract graph theory measures.Clinical relevance An automated tool for end-to-end processing of MRI data to brain connectivity pattern extraction and its quantitative characterisation for diagnosing brain disorder.