AIMC Topic: Predictive Value of Tests
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Expert system for predicting reaction conditions: the Michael reaction case.
Journal of chemical information and modeling
Feb 3, 2015
A generic chemical transformation may often be achieved under various synthetic conditions. However, for any specific reagents, only one or a few among the reported synthetic protocols may be successful. For example, Michael β-addition reactions may ...
Validation of electronic health record phenotyping of bipolar disorder cases and controls.
The American journal of psychiatry
Dec 12, 2014
OBJECTIVE: The study was designed to validate use of electronic health records (EHRs) for diagnosing bipolar disorder and classifying control subjects.
Improving the Mann-Whitney statistical test for feature selection: an approach in breast cancer diagnosis on mammography.
Artificial intelligence in medicine
Dec 12, 2014
OBJECTIVE: This work addresses the theoretical description and experimental evaluation of a new feature selection method (named uFilter). The uFilter improves the Mann-Whitney U-test for reducing dimensionality and ranking features in binary classifi...
Mammography
Predictive Value of Tests
Radiographic Image Interpretation, Computer-Assisted
Female
Bayes Theorem
Chi-Square Distribution
Humans
Discriminant Analysis
Reproducibility of Results
Linear Models
Machine Learning
Breast Neoplasms
Algorithms
Diagnosis, Computer-Assisted
Databases, Factual
Models, Statistical
Prediction of hearing loss among the noise-exposed workers in a steel factory using artificial intelligence approach.
International archives of occupational and environmental health
Nov 29, 2014
PURPOSE: Prediction of hearing loss in noisy workplaces is considered to be an important aspect of hearing conservation program. Artificial intelligence, as a new approach, can be used to predict the complex phenomenon such as hearing loss. Using art...
Permutation entropy analysis of vital signs data for outcome prediction of patients with severe traumatic brain injury.
Computers in biology and medicine
Nov 15, 2014
Permutation entropy is computationally efficient, robust to outliers, and effective to measure complexity of time series. We used this technique to quantify the complexity of continuous vital signs recorded from patients with traumatic brain injury (...
Clinical time series prediction: Toward a hierarchical dynamical system framework.
Artificial intelligence in medicine
Nov 6, 2014
OBJECTIVE: Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventi...
Predictors of schizophrenia spectrum disorders in early-onset first episodes of psychosis: a support vector machine model.
European child & adolescent psychiatry
Aug 11, 2014
Identifying early-onset schizophrenia spectrum disorders (SSD) at a very early stage remains challenging. To assess the diagnostic predictive value of multiple types of data at the emergence of early-onset first-episode psychosis (FEP), various suppo...
Diagnostic classification of specific phobia subtypes using structural MRI data: a machine-learning approach.
Journal of neural transmission (Vienna, Austria : 1996)
Jul 19, 2014
While neuroimaging research has advanced our knowledge about fear circuitry dysfunctions in anxiety disorders, findings based on diagnostic groups do not translate into diagnostic value for the individual patient. Machine-learning generates predictiv...
Predicting outcomes in patients with perforated gastroduodenal ulcers: artificial neural network modelling indicates a highly complex disease.
European journal of trauma and emergency surgery : official publication of the European Trauma Society
Jun 14, 2014
PURPOSE: Mortality prediction models for patients with perforated peptic ulcer (PPU) have not yielded consistent or highly accurate results. Given the complex nature of this disease, which has many non-linear associations with outcomes, we explored a...
Epileptic seizure prediction using relative spectral power features.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Jun 4, 2014
OBJECTIVE: Prediction of epileptic seizures can improve the living conditions for refractory epilepsy patients. We aimed to improve sensitivity and specificity of prediction methods, and to reduce the number of false alarms.
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