AIMC Topic: Data Collection

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Named entity recognition in electronic health records using transfer learning bootstrapped Neural Networks.

Neural networks : the official journal of the International Neural Network Society
Neural networks (NNs) have become the state of the art in many machine learning applications, such as image, sound (LeCun et al., 2015) and natural language processing (Young et al., 2017; Linggard et al., 2012). However, the success of NNs remains d...

An interpretable natural language processing system for written medical examination assessment.

Journal of biomedical informatics
OBJECTIVE: The assessment of written medical examinations is a tedious and expensive process, requiring significant amounts of time from medical experts. Our objective was to develop a natural language processing (NLP) system that can expedite the as...

Comparison of text processing methods in social media-based signal detection.

Pharmacoepidemiology and drug safety
PURPOSE: Adverse event (AE) identification in social media (SM) can be performed using various types of natural language processing (NLP) and machine learning (ML). These methods can be categorized by complexity and precision level. Co-occurrence-bas...

Health Data in Dentistry: An Attempt to Master the Digital Challenge.

Public health genomics
BACKGROUND: Biomedical research has recently moved through three stages in digital healthcare: (1) data collection; (2) data sharing; and (3) data analytics. With the explosion of stored health data (HD), dental medicine is edging into its fourth sta...

Leveraging Contextual Sentences for Text Classification by Using a Neural Attention Model.

Computational intelligence and neuroscience
We explored several approaches to incorporate context information in the deep learning framework for text classification, including designing different attention mechanisms based on different neural network and extracting some additional features fro...

Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings.

Nature communications
In order to advance precision medicine, detailed clinical features ought to be described in a way that leverages current knowledge. Although data collected from biomedical research is expanding at an almost exponential rate, our ability to transform ...

Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing.

Circulation. Cardiovascular quality and outcomes
BACKGROUND: Data sharing accelerates scientific progress but sharing individual-level data while preserving patient privacy presents a barrier.

A novel IRBF-RVM model for diagnosis of atrial fibrillation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is one of the common cardiovascular diseases, and electrocardiography (ECG) is a key indicator for the detection and diagnosis of AF and other heart diseases. In this study, an improved machine learn...

Using Machine Learning to Derive Just-In-Time and Personalized Predictors of Stress: Observational Study Bridging the Gap Between Nomothetic and Ideographic Approaches.

Journal of medical Internet research
BACKGROUND: Investigations into person-specific predictors of stress have typically taken either a population-level nomothetic approach or an individualized ideographic approach. Nomothetic approaches can quickly identify predictors but can be hinder...

Hierarchical sequence labeling for extracting BEL statements from biomedical literature.

BMC medical informatics and decision making
BACKGROUND: Extracting relations between bio-entities from biomedical literature is often a challenging task and also an essential step towards biomedical knowledge expansion. The BioCreative community has organized a shared task to evaluate the robu...