AIMC Topic: Data Mining

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Sentiment analysis in medical settings: New opportunities and challenges.

Artificial intelligence in medicine
OBJECTIVE: Clinical documents reflect a patient's health status in terms of observations and contain objective information such as descriptions of examination results, diagnoses and interventions. To evaluate this information properly, assessing posi...

Toward a complete dataset of drug-drug interaction information from publicly available sources.

Journal of biomedical informatics
Although potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete source of PDDI information. In the current study, all publically available sources of PDDI information ...

A global optimization approach to multi-polarity sentiment analysis.

PloS one
Following the rapid development of social media, sentiment analysis has become an important social media mining technique. The performance of automatic sentiment analysis primarily depends on feature selection and sentiment classification. While info...

Assessment of surveys for the management of hospital clinical pharmacy services.

Artificial intelligence in medicine
OBJECTIVE: Survey data sets are important sources of data, and their successful exploitation is of key importance for informed policy decision-making. We present how a survey analysis approach initially developed for customer satisfaction research in...

Self-training in significance space of support vectors for imbalanced biomedical event data.

BMC bioinformatics
BACKGROUND: Pairwise relationships extracted from biomedical literature are insufficient in formulating biomolecular interactions. Extraction of complex relations (namely, biomedical events) has become the main focus of the text-mining community. How...

Automatic evidence quality prediction to support evidence-based decision making.

Artificial intelligence in medicine
BACKGROUND: Evidence-based medicine practice requires practitioners to obtain the best available medical evidence, and appraise the quality of the evidence when making clinical decisions. Primarily due to the plethora of electronically available data...

DyKOSMap: A framework for mapping adaptation between biomedical knowledge organization systems.

Journal of biomedical informatics
BACKGROUND: Knowledge Organization Systems (KOS) and their associated mappings play a central role in several decision support systems. However, by virtue of knowledge evolution, KOS entities are modified over time, impacting mappings and potentially...

Non-redundant association rules between diseases and medications: an automated method for knowledge base construction.

BMC medical informatics and decision making
BACKGROUND: The widespread use of electronic health records (EHRs) has generated massive clinical data storage. Association rules mining is a feasible technique to convert this large amount of data into usable knowledge for clinical decision making, ...

SimConcept: a hybrid approach for simplifying composite named entities in biomedical text.

IEEE journal of biomedical and health informatics
One particular challenge in biomedical named entity recognition (NER) and normalization is the identification and resolution of composite named entities, where a single span refers to more than one concept (e.g., BRCA1/2). Previous NER and normalizat...

Using support vector machine ensembles for target audience classification on Twitter.

PloS one
The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning...