201919th International Congress of Metrology
|Number of page(s)||6|
|Section||Metrology For Health Care / Métrologie pour la Santé|
|Published online||23 September 2019|
Metrological references for health care based on entropy
RI.SE Metrology, Eklandagatan 86, S-412 61, Göteborg, Sweden
2 Modus Outcomes, Spirella Building, Letchworth Garden City, SG6 4ET, UK
3 NeuroMET coordinator Milena Quaglia, LGC
* Corresponding author: email@example.com
Consistent diagnosis in healthcare relies, in part, on quality assurance of categorical observations, such as responses to ability tests and patient surveys. Linking classifications on such nominal and ordinal scales to decision-making involves a combination of logit transformations and novel entropy-based estimates of measurement information throughout the measurement process. This paper presents how entropy can explain and predict entity attributes (such as task difficulty), instrument ability and resolution, and measurement system response. Cognitive ability studies in the EMPIR NeuroMET project are taken as an example, showing how better understanding of both entity and measurement system attributes leads to more fit-for-purpose and better targeted treatment.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.