Using machine learning for healthcare treatment planning.

We present a methodology for using machine learning for planning treatments. As a case study, we apply the proposed methodology to Breast Cancer. Most of the application of Machine Learning to breast cancer has been on diagnosis and early detection. By contrast, our paper focuses on applying Machine Learning to suggest treatment plans for patients with different disease severity. While the need for surgery and even its type is often obvious to a patient, the need for chemotherapy and radiation therapy is not as obvious to the patient. With this in mind, the following treatment plans were considered in this study: chemotherapy, radiation, chemotherapy with radiation, and none of these options (only surgery). We use real data from more than 10,000 patients over 6 years that includes detailed cancer information, treatment plans, and survival statistics. Using this data set, we construct Machine Learning classifiers to suggest treatment plans. Our emphasis in this effort is not only on suggesting the treatment plan but on explaining and defending a particular treatment choice to the patient.

The RNA-Seq data analysis shows how the ontogenesis defines aging.

This paper presents a global statistical analysis of the RNA-Seq results of the entire Mus musculus genome. We explain aging by a gradual redistribution of limited resources between two major tasks of the organism: its self-sustenance based on the function of the housekeeping gene group (HG) and functional differentiation provided by the integrative gene group (IntG). All known disorders associated with aging are the result of a deficiency in the repair processes provided by the cellular infrastructure. Understanding exactly how this deficiency arises is our primary goal. Analysis of RNA production data of 35,630 genes, from which 5,101 were identified as HG genes, showed that RNA production levels in the HG and IntG genes had statistically significant differences (p-value <0.0001) throughout the entire observation period. In the reproductive period of life, which has the lowest actual mortality risk for Mus musculus, changes in the age dynamics of RNA production occur. The statistically significant dynamics of the decrease of RNA production in the HG group in contrast to the IntG group was determined (p-value = 0.0045). The trend toward significant shift in the HG/IntG ratio occurs after the end of the reproductive period, coinciding with the beginning of the mortality rate increase in Mus musculus indirectly supports our hypothesis. The results demonstrate a different orientation of the impact of ontogenesis regulatory mechanisms on the groups of genes representing cell infrastructures and their organismal functions, making the chosen direction promising for further research and understanding the mechanisms of aging.

Understanding Smartwatch Battery Utilization in the Wild.

Smartwatch battery limitations are one of the biggest hurdles to their acceptability in the consumer market. To our knowledge, despite promising studies analyzing smartwatch battery data, there has been little research that has analyzed the battery usage of a diverse set of smartwatches in a real-world setting. To address this challenge, this paper utilizes a smartwatch dataset collected from 832 real-world users, including different smartwatch brands and geographic locations. First, we employ clustering to identify common patterns of smartwatch battery utilization; second, we introduce a transparent low-parameter convolutional neural network model, which allows us to identify the latent patterns of smartwatch battery utilization. Our model converts the battery consumption rate into a binary classification problem; i.e., low and high consumption. Our model has 85.3% accuracy in predicting high battery discharge events, outperforming other machine learning algorithms that have been used in state-of-the-art research. Besides this, it can be used to extract information from filters of our deep learning model, based on learned filters of the feature extractor, which is impossible for other models. Third, we introduce an indexing method that includes a longitudinal study to quantify smartwatch battery quality changes over time. Our novel findings can assist device manufacturers, vendors and application developers, as well as end-users, to improve smartwatch battery utilization.