Mobile Apps for Helping Patient-Users: Is It Still Far-Fetched?

Comparison of employment among people with Multiple Sclerosis across Europe

Background

People with Multiple Sclerosis (PwMS) suffer from an increased risk of unemployment during the course of the disease. In recent years progress has been made in increasing the time until patients have to leave the workforce permanently. Such a retirement is often associated with MS but the driving factors including disability progression, support measures at the workplace, and societal aspects are not yet fully understood.

Methods

We consolidated data from four European MS databases from Germany, Poland, Sweden, and the United Kingdom, which were able to provide data on working status, disability progression and quality of life in accordance with the data harmonization framework of the EUReMS (European Registry in Multiple Sclerosis) project.

Results

Factors strongly associated with unemployment are disability progression, low quality of life and being close to the statutory retirement age. Overall, highest employment rate (77%) and lowest effects of gender and disease duration were found in Sweden.

Conclusions

We found remarkable differences between the European registers and the countries studied, which may indicate inequalities at European level. Furthermore, our findings suggest that it is feasible and useful to combine data from different MS registers in Europe, albeit the data structures are heterogeneous.

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region's image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.