Leveraging Unsupervised Machine Learning To Find Trends In The Textual Warnings Generated Using Multi-Modality Time Series Sensor Data

With technology and the internet of things (IoT), smart health care is no longer a dream. The devices that wouldn’t usually be generally expected to have an internet connection can communicate with the network independent of human action, and this is referred to as the internet of things. These devices, in our case, are the sensors placed in the elderly assisted living facility Tiger Place. Sensors include the ballistocardiogram (bed sensor) and motion sensors placed in the living room, bathroom, kitchen, etc. These sensors help in measuring nine major features, which defines the per-day activities of each elderly resident in the…

Neural information extraction in biomedical domain: issues and challenges

Much medical data today remain inaccessible thus limiting their impact on patient care. Images and illustrations, scientific articles, and free-text reports do not allow for easy extraction and re-use of the knowledge they contain. They lack the structure and metadata necessary for automated processing and annotation. The resources required to collect and annotate manually are not sufficient to produce enough comprehensive benchmark datasets to bootstrap specialty research. We discuss neural network-based approaches to the problem of extraction of medical information from clinical images and unstructured text sources.