AIMC Topic: Machine Learning

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Learning Healthcare Systems in Pediatrics: Cross-Institutional and Data-Driven Decision-Support for Intensive Care Environments (CADDIE).

Studies in health technology and informatics Jan 1, 2018
BACKGROUND: The vast amount of data generated in healthcare can be reused to support decision-making by developing clinical decision-support systems. Since evidence is lacking in Pediatrics, it seems to be beneficial to design future systems towards ...
Decision Support Systems, Clinical Data Collection Pediatrics Algorithms Critical Care Humans Child Machine Learning Intensive Care Units, Pediatric
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On the Representation of Machine Learning Results for Delirium Prediction in a Hospital Information System in Routine Care.

Studies in health technology and informatics Jan 1, 2018
Digitalisation of health care for the purpose of medical documentation lead to huge amounts of data, hence having an opportunity to derive knowledge and associations of different attributes recorded. Many health care events can be prevented when iden...
Hospitalization Humans Delirium Machine Learning Hospital Information Systems Prognosis Documentation
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Machine Learning Methods in Computational Toxicology.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2018
Various methods of machine learning, supervised and unsupervised, linear and nonlinear, classification and regression, in combination with various types of molecular descriptors, both "handcrafted" and "data-driven," are considered in the context of ...
Machine Learning Linear Models Toxicology Algorithms Deep Learning Neural Networks, Computer Computer Simulation Quantitative Structure-Activity Relationship Support Vector Machine
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Numerical Models and In Vitro Assays to Study Odorant Receptors.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2018
Unraveling the sense of smell relies on understanding how odorant receptors recognize odorant molecules. Given the vastness of the odorant chemical space and the complexity of the odorant receptor space, computational methods are in line to propose r...
Humans Machine Learning Animals Molecular Docking Simulation Computer Simulation Mutation Receptors, Odorant
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Machine Learning-based Virtual Screening and Its Applications to Alzheimer's Drug Discovery: A Review.

Current pharmaceutical design Jan 1, 2018
BACKGROUND: Virtual Screening (VS) has emerged as an important tool in the drug development process, as it conducts efficient in silico searches over millions of compounds, ultimately increasing yields of potential drug leads. As a subset of Artifici...
Bayes Theorem Humans Neuroprotective Agents Alzheimer Disease Machine Learning Drug Evaluation, Preclinical Drug Discovery
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[Machine Learning-based Prediction of Seizure-inducing Action as an Adverse Drug Effect].

Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan Jan 1, 2018
　During the preclinical research period of drug development, animal testing is widely used to help screen out a drug's dangerous side effects. However, it remains difficult to predict side effects within the central nervous system. Here, we introduce...
Strychnine Drug Evaluation, Preclinical Drug-Related Side Effects and Adverse Reactions Theophylline Enoxacin Diphenhydramine Drug Discovery Mice Seizures Humans Forecasting Machine Learning Animals
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Predicting Depression Among Community Residing Older Adults: A Use of Machine Learning Approch.

Studies in health technology and informatics Jan 1, 2018
The study demonstrated an application of machine learning techniques in building a depression prediction model. We used the NSHAP II data (3,377 subjects and 261 variables) and built the models using a logistic regression with and without L1 regulari...
Machine Learning Algorithms Forecasting Humans Logistic Models Models, Theoretical Aged Depression
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Predicting Risk of 30-Day Readmissions Using Two Emerging Machine Learning Methods.

Studies in health technology and informatics Jan 1, 2018
Decades-long research efforts have shown that Heart Failure (HF) is the most expensive diagnosis for hospitalizations and the most frequent diagnosis for 30-day readmissions. If risk stratification for readmission of HF patients could be carried out ...
Humans Patient Discharge Heart Failure Hospitalization Patient Readmission Risk Assessment United States Machine Learning Forecasting
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Can Staff Distinguish Falls: Experimental Hypothesis Verification Using Japanese Incident Reports and Natural Language Processing.

Studies in health technology and informatics Jan 1, 2018
Falls are generally classified into two groups in clinical settings in Japan: falls from the same level and falls from one level to another. We verified whether clinical staff could distinguish between these two types of falls by comparing 3,078 free...
Risk Management Japan Accidental Falls Natural Language Processing Humans Machine Learning
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Prediction of Nitrated Tyrosine Residues in Protein Sequences by Extreme Learning Machine and Feature Selection Methods.

Combinatorial chemistry & high throughput screening Jan 1, 2018
BACKGROUND: Accurately recognizing nitrated tyrosine residues from protein sequences would pave a way for understanding the mechanism of nitration and the screening of the tyrosine residues in sequences.
Machine Learning Amino Acid Sequence Nitrates Tyrosine Protein Processing, Post-Translational Databases, Protein Computational Biology
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