scholarly journals Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data

2015 ◽  
Author(s):  
Hadi Banaee ◽  
Mobyen Uddin Ahmed ◽  
Amy Loutfi
Keyword(s):  
Data Driven ◽  
Physiological Data ◽  
Rule Mining ◽  
Clinical Conditions

scholarly journals Data-driven modelling approach to circadian temperature rhythm profiles in free-living conditions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jari Lipsanen ◽  
Liisa Kuula ◽  
Marko Elovainio ◽  
Timo Partonen ◽  
Anu-Katriina Pesonen
Keyword(s):  
Skin Surface ◽  
Living Conditions ◽  
Data Driven ◽  
Physiological Data ◽  
Free Living ◽  
Physiological Level ◽  
Temperature Rhythm ◽  
Time Series Modelling ◽  
Circadian Preference ◽  
Free Living Conditions

AbstractThe individual variation in the circadian rhythms at the physiological level is not well understood. Albeit self-reported circadian preference profiles have been consolidated, their premises are grounded on human experience, not on physiology. We used data-driven, unsupervised time series modelling to characterize distinct profiles of the circadian rhythm measured from skin surface temperature in free-living conditions. We demonstrate the existence of three distinct clusters of individuals which differed in their circadian temperature profiles. The cluster with the highest temperature amplitude and the lowest midline estimating statistic of rhythm, or rhythm-adjusted mean, had the most regular and early-timed sleep–wake rhythm, and was the least probable for those with a concurrent delayed sleep phase, or eveningness chronotype. While the clusters associated with the observed sleep and circadian preference patterns, the entirely unsupervised modelling of physiological data provides a novel basis for modelling and understanding the human circadian functions in free-living conditions.

scholarly journals Correlating Espresso Quality with Coffee-Machine Parameters by Means of Association Rule Mining

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 100
Author(s):  
Daniele Apiletti ◽  
Eliana Pastor
Keyword(s):  
Real World ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Driven ◽  
Rule Mining ◽  
Electronic Noses ◽  
Data Driven Approach ◽  
The Many ◽  
Coffee Machine

Coffee is among the most popular beverages in many cities all over the world, being both at the core of the busiest shops and a long-standing tradition of recreational and social value for many people. Among the many coffee variants, espresso attracts the interest of different stakeholders: from citizens consuming espresso around the city, to local business activities, coffee-machine vendors and international coffee industries. The quality of espresso is one of the most discussed and investigated issues. So far, it has been addressed by means of human experts, electronic noses, and chemical approaches. The current work, instead, proposes a data-driven approach exploiting association rule mining. We analyze a real-world dataset of espresso brewing by professional coffee-making machines, and extract all correlations among external quality-influencing variables and actual metrics determining the quality of the espresso. Thanks to the application of association rule mining, a powerful data-driven exhaustive and explainable approach, results are expressed in the form of human-readable rules combining the variables of interest, such as the grinder settings, the extraction time, and the dose amount. Novel insights from real-world coffee extractions collected on the field are presented, together with a data-driven approach, able to uncover insights into the espresso quality and its impact on both the life of consumers and the choices of coffee-making industries.

Combining Data-Driven and User-Driven Evaluation Measures to Identify Interesting Rules

2009 ◽  
pp. 38-55 ◽  
Author(s):  
Solange Oliveira Rezende ◽  
Edson Augusto Melanda ◽  
Magaly Lika Fujimoto ◽  
Roberta Akemi Sinoara ◽  
Veronica Oliveira de Carvalho
Keyword(s):  
Data Mining ◽  
Data Driven ◽  
Objective Measures ◽  
Post Processing ◽  
Rule Mining ◽  
Evaluation Measures ◽  
Combining Data ◽  
Pros And Cons ◽  
The Subject

Association rule mining is a data mining task that is applied in several real problems. However, due to the huge number of association rules that can be generated, the knowledge post-processing phase becomes very complex and challenging. There are several evaluation measures that can be used in this phase to assist users in finding interesting rules. These measures, which can be divided into data-driven (or objective measures) and user-driven (or subjective measures), are first discussed and then analyzed for their pros and cons. A new methodology that combines them, aiming to use the advantages of each kind of measure and to make user’s participation easier, is presented. In this way, data-driven measures can be used to select some potentially interesting rules for the user’s evaluation. These rules and the knowledge obtained during the evaluation can be used to calculate user-driven measures, which are used to aid the user in identifying interesting rules. In order to identify interesting rules that use our methodology, an approach is described, as well as an exploratory environment and a case study to show that the proposed methodology is feasible. Interesting results were obtained. In the end of the chapter tendencies related to the subject are discussed.

scholarly journals A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling

2018 ◽  
Vol 3 (2) ◽  
pp. 245-263
Author(s):  
Franco van Wyk ◽  
Anahita Khojandi ◽  
Brian Williams ◽  
Don MacMillan ◽  
Robert L. Davis ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Data Driven ◽  
Physiological Data ◽  
Benefit Analysis ◽  
Data Acquisition Systems ◽  
Using Data ◽  
Data Driven Modeling

A physiological data‐driven model for learners' cognitive load detection using HRV‐PRV feature fusion and optimized XGBoost classification

2019 ◽  
Vol 50 (11) ◽  
pp. 2046-2064
Author(s):  
Junqi Guo ◽  
Yazhu Dai ◽  
Chixiang Wang ◽  
Hao Wu ◽  
Tianyou Xu ◽  
...  
Keyword(s):  
Cognitive Load ◽  
Feature Fusion ◽  
Data Driven ◽  
Physiological Data ◽  
Load Detection

Data-driven and association rule mining-based fault diagnosis and action mechanism analysis for building chillers

2020 ◽  
Vol 216 ◽  
pp. 109957 ◽  
Author(s):  
Jiangyan Liu ◽  
Daliang Shi ◽  
Guannan Li ◽  
Yi Xie ◽  
Kuining Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Driven ◽  
Mechanism Analysis ◽  
Action Mechanism ◽  
Rule Mining

scholarly journals Biomarker-Informed Machine Learning Model of Cognitive Fatigue from a Heart Rate Response Perspective

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3843
Author(s):  
Kar Fye Alvin Lee ◽  
Woon-Seng Gan ◽  
Georgios Christopoulos
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Nervous Activity ◽  
Self Report ◽  
Data Driven ◽  
Psychological State ◽  
Research Progress ◽  
Physiological Data ◽  
Learning Approaches ◽  
Cognitive Fatigue

Cognitive fatigue is a psychological state characterised by feelings of tiredness and impaired cognitive functioning arising from high cognitive demands. This paper examines the recent research progress on the assessment of cognitive fatigue and provides informed recommendations for future research. Traditionally, cognitive fatigue is introspectively assessed through self-report or objectively inferred from a decline in behavioural performance. However, more recently, researchers have attempted to explore the biological underpinnings of cognitive fatigue to understand and measure this phenomenon. In particular, there is evidence indicating that the imbalance between sympathetic and parasympathetic nervous activity appears to be a physiological correlate of cognitive fatigue. This imbalance has been indexed through various heart rate variability indices that have also been proposed as putative biomarkers of cognitive fatigue. Moreover, in contrast to traditional inferential methods, there is also a growing research interest in using data-driven approaches to assessing cognitive fatigue. The ubiquity of wearables with the capability to collect large amounts of physiological data appears to be a major facilitator in the growth of data-driven research in this area. Preliminary findings indicate that such large datasets can be used to accurately predict cognitive fatigue through various machine learning approaches. Overall, the potential of combining domain-specific knowledge gained from biomarker research with machine learning approaches should be further explored to build more robust predictive models of cognitive fatigue.

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