AI Medical Compendium Topic:
Data Mining

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Querying archetype-based EHRs by search ontology-based XPath engineering.

Journal of biomedical semantics
BACKGROUND: Legacy data and new structured data can be stored in a standardized format as XML-based EHRs on XML databases. Querying documents on these databases is crucial for answering research questions. Instead of using free text searches, that le...

Extracting cancer mortality statistics from death certificates: A hybrid machine learning and rule-based approach for common and rare cancers.

Artificial intelligence in medicine
OBJECTIVE: Death certificates are an invaluable source of cancer mortality statistics. However, this value can only be realised if accurate, quantitative data can be extracted from certificates-an aim hampered by both the volume and variable quality ...

Identifying Suicide Ideation and Suicidal Attempts in a Psychiatric Clinical Research Database using Natural Language Processing.

Scientific reports
Research into suicide prevention has been hampered by methodological limitations such as low sample size and recall bias. Recently, Natural Language Processing (NLP) strategies have been used with Electronic Health Records to increase information ext...

An effective neural model extracting document level chemical-induced disease relations from biomedical literature.

Journal of biomedical informatics
Since identifying relations between chemicals and diseases (CDR) are important for biomedical research and healthcare, the challenge proposed by BioCreative V requires automatically mining causal relationships between chemicals and diseases which may...

Identifying β-thalassemia carriers using a data mining approach: The case of the Gaza Strip, Palestine.

Artificial intelligence in medicine
Thalassemia is considered one of the most common genetic blood disorders that has received excessive attention in the medical research fields worldwide. Under this context, one of the greatest challenges for healthcare professionals is to correctly d...

Leveraging Wikipedia knowledge to classify multilingual biomedical documents.

Artificial intelligence in medicine
This article presents a classifier that leverages Wikipedia knowledge to represent documents as vectors of concepts weights, and analyses its suitability for classifying biomedical documents written in any language when it is trained only with Englis...

Unsupervised Medical Entity Recognition and Linking in Chinese Online Medical Text.

Journal of healthcare engineering
Online medical text is full of references to medical entities (MEs), which are valuable in many applications, including medical knowledge-based (KB) construction, decision support systems, and the treatment of diseases. However, the diverse and ambig...

DDC-Outlier: Preventing Medication Errors Using Unsupervised Learning.

IEEE journal of biomedical and health informatics
Electronic health records have brought valuable improvements to hospital practices by integrating patient information. In fact, the understanding of these data can prevent mistakes that may put patients' lives at risk. Nonetheless, to the best of our...

Deep learning improves prediction of drug-drug and drug-food interactions.

Proceedings of the National Academy of Sciences of the United States of America
Drug interactions, including drug-drug interactions (DDIs) and drug-food constituent interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug events (ADEs), with causal mechanisms often unknown. Several computationa...

Predicting Hospital Readmission via Cost-Sensitive Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics
With increased use of electronic medical records (EMRs), data mining on medical data has great potential to improve the quality of hospital treatment and increase the survival rate of patients. Early readmission prediction enables early intervention,...