Related Experiment Video
Updated: Aug 30, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Assessing likelihood ratio of clinical symptoms: handling vagueness
A L B Rutten1, C F Stolper, R F G Lugten
1Commissie Methode en Validering VHAN (Dutch Association of Homeopathic Physicians), The Netherlands. lexrtn@concepts.nl
Abstract:
Clinical symptoms including homeopathic symptoms are often vague. There is reluctance to assess clinical symptoms as diagnostic instruments because they are hard to define. Still, clinical symptoms appear effective in daily practice. Expert systems and neural networks handle vague data successfully. Theoretical considerations predict the kind of problems we may expect. There is a difference between quantitative and qualitative vagueness. Vague data cause problems if we try to prove a hypothesis because of expectation bias. We assess likelihood ratio of homeopathic symptoms only to improve the method.
Related Concept Videos
Sensitivity, Specificity, and Predicted Value
Sensitivity is the...
Formulating and Validating Nursing Diagnosis II
Risk nursing diagnoses represent clinical judgments of an individual, family, or community more vulnerable to developing the health problem than others...
Formulating and Validating Nursing Diagnosis I
There are thirteen domains for...
The Availability Heuristic
Classification of Illness
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...
Hazard Ratio
For example, in a clinical trial evaluating a...