AIMC Topic: Data Mining

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Using local lexicalized rules to identify heart disease risk factors in clinical notes.

Journal of biomedical informatics
Heart disease is the leading cause of death globally and a significant part of the human population lives with it. A number of risk factors have been recognized as contributing to the disease, including obesity, coronary artery disease (CAD), hyperte...

The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs.

Journal of biomedical informatics
This paper describes a supervised machine learning approach for identifying heart disease risk factors in clinical text, and assessing the impact of annotation granularity and quality on the system's ability to recognize these risk factors. We utiliz...

Automatic de-identification of electronic medical records using token-level and character-level conditional random fields.

Journal of biomedical informatics
De-identification, identifying and removing all protected health information (PHI) present in clinical data including electronic medical records (EMRs), is a critical step in making clinical data publicly available. The 2014 i2b2 (Center of Informati...

RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To develop and evaluate RobotReviewer, a machine learning (ML) system that automatically assesses bias in clinical trials. From a (PDF-formatted) trial report, the system should determine risks of bias for the domains defined by the Cochra...

Fuzzy association rule mining and classification for the prediction of malaria in South Korea.

BMC medical informatics and decision making
BACKGROUND: Malaria is the world's most prevalent vector-borne disease. Accurate prediction of malaria outbreaks may lead to public health interventions that mitigate disease morbidity and mortality.

Exploring Spanish health social media for detecting drug effects.

BMC medical informatics and decision making
BACKGROUND: Adverse Drug reactions (ADR) cause a high number of deaths among hospitalized patients in developed countries. Major drug agencies have devoted a great interest in the early detection of ADRs due to their high incidence and increasing hea...

Using text mining techniques to extract phenotypic information from the PhenoCHF corpus.

BMC medical informatics and decision making
BACKGROUND: Phenotypic information locked away in unstructured narrative text presents significant barriers to information accessibility, both for clinical practitioners and for computerised applications used for clinical research purposes. Text mini...

Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases.

Scientific reports
Phenotypes are the observable characteristics of an organism arising from its response to the environment. Phenotypes associated with engineered and natural genetic variation are widely recorded using phenotype ontologies in model organisms, as are s...

Interactive Cohort Identification of Sleep Disorder Patients Using Natural Language Processing and i2b2.

Applied clinical informatics
UNLABELLED: Nationwide Children's Hospital established an i2b2 (Informatics for Integrating Biology & the Bedside) application for sleep disorder cohort identification. Discrete data were gleaned from semistructured sleep study reports. The system sh...

Identifying synonymy between relational phrases using word embeddings.

Journal of biomedical informatics
Many text mining applications in the biomedical domain benefit from automatic clustering of relational phrases into synonymous groups, since it alleviates the problem of spurious mismatches caused by the diversity of natural language expressions. Mos...