AIMC Topic: Data Mining

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Enhancement of hepatitis virus immunoassay outcome predictions in imbalanced routine pathology data by data balancing and feature selection before the application of support vector machines.

BMC medical informatics and decision making
BACKGROUND: Data mining techniques such as support vector machines (SVMs) have been successfully used to predict outcomes for complex problems, including for human health. Much health data is imbalanced, with many more controls than positive cases.

Big data in forensic science and medicine.

Journal of forensic and legal medicine
In less than a decade, big data in medicine has become quite a phenomenon and many biomedical disciplines got their own tribune on the topic. Perspectives and debates are flourishing while there is a lack for a consensual definition for big data. The...

TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records.

Scientific reports
Vast amounts of clinically relevant text-based variables lie undiscovered and unexploited in electronic medical records (EMR). To exploit this untapped resource, and thus facilitate the discovery of informative covariates from unstructured clinical n...

A Regularized Deep Learning Approach for Clinical Risk Prediction of Acute Coronary Syndrome Using Electronic Health Records.

IEEE transactions on bio-medical engineering
OBJECTIVE: Acute coronary syndrome (ACS), as a common and severe cardiovascular disease, is a leading cause of death and the principal cause of serious long-term disability globally. Clinical risk prediction of ACS is important for early intervention...

Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals.

Journal of medical Internet research
BACKGROUND: Social networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts...

Protein asparagine deamidation prediction based on structures with machine learning methods.

PloS one
Chemical stability is a major concern in the development of protein therapeutics due to its impact on both efficacy and safety. Protein "hotspots" are amino acid residues that are subject to various chemical modifications, including deamidation, isom...

Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study.

Journal of medical Internet research
BACKGROUND: Early identification and intervention are imperative for suicide prevention. However, at-risk people often neither seek help nor take professional assessment. A tool to automatically assess their risk levels in natural settings can increa...

Recurrent neural networks for classifying relations in clinical notes.

Journal of biomedical informatics
We proposed the first models based on recurrent neural networks (more specifically Long Short-Term Memory - LSTM) for classifying relations from clinical notes. We tested our models on the i2b2/VA relation classification challenge dataset. We showed ...

Classification and analysis of a large collection of in vivo bioassay descriptions.

PLoS computational biology
Testing potential drug treatments in animal disease models is a decisive step of all preclinical drug discovery programs. Yet, despite the importance of such experiments for translational medicine, there have been relatively few efforts to comprehens...

A statistical framework for biomedical literature mining.

Statistics in medicine
In systems biology, it is of great interest to identify new genes that were not previously reported to be associated with biological pathways related to various functions and diseases. Identification of these new pathway-modulating genes does not onl...