Electronic health record (EHR) resources are valuable but remain underexplored because most clinical information, especially phenotype information, is buried in the free text of EHRs. An intelligent ...annotation tool plays an important role in unlocking the full potential of EHRs by transforming free-text phenotype information into a computer-readable form. Deep phenotyping has shown its advantage in representing phenotype information in EHRs with high fidelity; however, most existing annotation tools are not suitable for the deep phenotyping task. Here, we developed an intelligent annotation tool named PIAT with a major focus on the deep phenotyping of Chinese EHRs. PIAT can improve the annotation efficiency for EHR-based deep phenotyping with a simple but effective interactive interface, automatic preannotation support, and a learning mechanism. Specifically, experts can proofread automatic annotation results from the annotation algorithm in the web-based interactive interface, and EHRs reviewed by experts can be used for evolving the underlying annotation algorithm. In this way, the annotation process of deep phenotyping EHRs will become easier. In conclusion, we create a powerful intelligent system for the deep phenotyping of Chinese EHRs. It is hoped that our work will inspire further studies in constructing intelligent systems for deep phenotyping English and non-English EHRs.
Wearable computers provide significant opportunities for sensing and data collection in user's natural environment (NE). However, they require both raw data and annotations to train their respective ...signal processing algorithms. Collecting these annotations is often burdensome for the users. Our proposed methodology leverages the notion of location from nearable sensors in Internet of Things (IoT) platforms and learns users' patterns of behavior without any prior knowledge. It also requests users for annotations and labels only when the algorithms are unable to automatically annotate the data. We validate our proposed approach in the context of diet monitoring, a significant application that often requires considerable user compliance. Our approach improves eating detection accuracy by 2.4% with requested annotations restricted to 20 per day.
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