AIMC Topic:
Supervised Machine Learning
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Estimating a Bias in ICD Encodings for Billing Purposes.
Studies in health technology and informatics
Jan 1, 2018
ICD encoded diagnoses are a popular criterion for eligibility algorithms for study cohort recruitment. However, "official" ICD encoded diagnoses used for billing purposes are afflicted with a bias originating from legal issues. This work presents an ...
Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2018
Identification of drug targets and drug target interactions are important steps in the drug-discovery pipeline. Successful computational prediction methods can reduce the cost and time demanded by the experimental methods. Knowledge of putative drug ...
Considerations for automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2018
With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide ex...
Improving the explainability of Random Forest classifier - user centered approach.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2018
Machine Learning (ML) methods are now influencing major decisions about patient care, new medical methods, drug development and their use and importance are rapidly increasing in all areas. However, these ML methods are inherently complex and often d...
Data-driven advice for applying machine learning to bioinformatics problems.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2018
As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classific...
Mapping Patient Trajectories using Longitudinal Extraction and Deep Learning in the MIMIC-III Critical Care Database.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2018
Electronic Health Records (EHRs) contain a wealth of patient data useful to biomedical researchers. At present, both the extraction of data and methods for analyses are frequently designed to work with a single snapshot of a patient's record. Health ...
Graph-based semi-supervised learning with genomic data integration using condition-responsive genes applied to phenotype classification.
Journal of the American Medical Informatics Association : JAMIA
Jan 1, 2018
OBJECTIVE: Data integration methods that combine data from different molecular levels such as genome, epigenome, transcriptome, etc., have received a great deal of interest in the past few years. It has been demonstrated that the synergistic effects ...
ProtDec-LTR2.0: an improved method for protein remote homology detection by combining pseudo protein and supervised Learning to Rank.
Bioinformatics (Oxford, England)
Nov 1, 2017
SUMMARY: As one of the most important tasks in protein sequence analysis, protein remote homology detection is critical for both basic research and practical applications. Here, we present an effective web server for protein remote homology detection...
Mastering the game of Go without human knowledge.
Nature
Oct 18, 2017
A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in Alpha...
Prediction of persistent post-surgery pain by preoperative cold pain sensitivity: biomarker development with machine-learning-derived analysis.
British journal of anaesthesia
Oct 1, 2017
BACKGROUND: To prevent persistent post-surgery pain, early identification of patients at high risk is a clinical need. Supervised machine-learning techniques were used to test how accurately the patients' performance in a preoperatively performed ton...
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