AIMC Topic: Machine Learning

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Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

Journal of proteome research
Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it re...

Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization.

Journal of chemical information and modeling
Matched molecular pair (MMP) analyses are widely used in compound optimization projects to gain insights into structure-activity relationships (SAR). The analysis is traditionally done via statistical methods but can also be employed together with ma...

Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records.

Scientific reports
Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from curr...

Developing a Machine Learning System for Identification of Severe Hand, Foot, and Mouth Disease from Electronic Medical Record Data.

Scientific reports
Children of severe hand, foot, and mouth disease (HFMD) often present with same clinical features as those of mild HFMD during the early stage, yet later deteriorate rapidly with a fulminant disease course. Our goal was to: (1) develop a machine lear...

Classification of hospital admissions into emergency and elective care: a machine learning approach.

Health care management science
Rising admissions from emergency departments (EDs) to hospitals are a primary concern for many healthcare systems. The issue of how to differentiate urgent admissions from non-urgent or even elective admissions is crucial. We aim to develop a model f...

Ensembles of Lasso Screening Rules.

IEEE transactions on pattern analysis and machine intelligence
In order to solve large-scale lasso problems, screening algorithms have been developed that discard features with zero coefficients based on a computationally efficient screening rule. Most existing screening rules were developed from a spherical con...

Rama: a machine learning approach for ribosomal protein prediction in plants.

Scientific reports
Ribosomal proteins (RPs) play a fundamental role within all type of cells, as they are major components of ribosomes, which are essential for translation of mRNAs. Furthermore, these proteins are involved in various physiological and pathological pro...

Evaluation of three machine learning models for self-referral decision support on low back pain in primary care.

International journal of medical informatics
BACKGROUND: Most people experience low back pain (LBP) at least once in their life and for some patients this evolves into a chronic condition. One way to prevent acute LBP from transiting into chronic LBP, is to ensure that patients receive the righ...

Radiology report annotation using intelligent word embeddings: Applied to multi-institutional chest CT cohort.

Journal of biomedical informatics
We proposed an unsupervised hybrid method - Intelligent Word Embedding (IWE) that combines neural embedding method with a semantic dictionary mapping technique for creating a dense vector representation of unstructured radiology reports. We applied I...

Genomic risk prediction of aromatase inhibitor-related arthralgia in patients with breast cancer using a novel machine-learning algorithm.

Cancer medicine
Many breast cancer (BC) patients treated with aromatase inhibitors (AIs) develop aromatase inhibitor-related arthralgia (AIA). Candidate gene studies to identify AIA risk are limited in scope. We evaluated the potential of a novel analytic algorithm ...