AIMC Topic: Data Mining

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Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text.

Yearbook of medical informatics
Natural Language Processing (NLP) methods are increasingly being utilized to mine knowledge from unstructured health-related texts. Recent advances in noisy text processing techniques are enabling researchers and medical domain experts to go beyond ...

Sensor, Signal, and Imaging Informatics.

Yearbook of medical informatics
To summarize significant contributions to sensor, signal, and imaging informatics published in 2016. We conducted an extensive search using PubMed® and Web of Science® to identify the scientific contributions published in 2016 that addressed sensor...

Gene2DisCo: Gene to disease using disease commonalities.

Artificial intelligence in medicine
OBJECTIVE: Finding the human genes co-causing complex diseases, also known as "disease-genes", is one of the emerging and challenging tasks in biomedicine. This process, termed gene prioritization (GP), is characterized by a scarcity of known disease...

Construction accident narrative classification: An evaluation of text mining techniques.

Accident; analysis and prevention
Learning from past accidents is fundamental to accident prevention. Thus, accident and near miss reporting are encouraged by organizations and regulators. However, for organizations managing large safety databases, the time taken to accurately classi...

Machine Learning Approaches on Diagnostic Term Encoding With the ICD for Clinical Documentation.

IEEE journal of biomedical and health informatics
This work focuses on data mining applied to the clinical documentation domain. Diagnostic terms (DTs) are used as keywords to retrieve valuable information from electronic health records. Indeed, they are encoded manually by experts following the Int...

A neural network multi-task learning approach to biomedical named entity recognition.

BMC bioinformatics
BACKGROUND: Named Entity Recognition (NER) is a key task in biomedical text mining. Accurate NER systems require task-specific, manually-annotated datasets, which are expensive to develop and thus limited in size. Since such datasets contain related ...