Towards vegetation species discrimination by using data-driven descriptors

Background Scales for evaluating insomnia differ in number of items, response format, and result in different scores distributions and score ranges and may not facilitate meaningful comparisons. Objectives Transform ordinal item-scores of three scales of insomnia to continuous, equidistant, monotonic, normally distributed scores, avoiding limitations of summative scoring of Likert scales. Methods Equidistant item-scores by weighted sum using data-driven weights to different levels of different items, considering cell frequencies of Item-Levels matrix, followed by normalization and conversion to [1, 10]. Equivalent test-scores (as sum of transformed item- scores) for a pair of scales were found by Normal Probability curves. Empirical illustration given. Results Transformed test-scores are continuous, monotonic and followed Normal distribution with no outliers and tied scores. Such test-scores facilitate ranking, better classification and meaningful comparison of scales of different lengths and formats and finding equivalent score combinations of two scales. For a given value of transformed test-score of a scale, easy alternate method avoiding integration proposed to find equivalent scores of another scales. Equivalent scores of scales help to relate various cut-off scores of different scales and uniformity in interpretations. Integration of various scales of insomnia is achieved by finding one-to-one correspondence among the equivalent score of various scales with correlation over 0.99 Conclusion Resultant test-scores facilitated undertaking analysis in parametric set up. Considering the theoretical advantages including meaningfulness of operations, better comparison, use of such method of transforming scores of Likert items/test is recommended test and items, Future studies were suggested.

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Prediction of industrial debutanizer column compositions using data-driven ANFIS- and ANN-based approaches

Neural Computing and Applications ◽

10.1007/s00521-020-05593-0 ◽

2021 ◽

Author(s):

Syeda Anmol Fatima ◽

Nasser Ramli ◽

Syed Ali Ammar Taqvi ◽

Haslinda Zabiri

Keyword(s):

Data Driven ◽

Using Data

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Impact of COVID-19 Epidemics on Bed Requirements in a Healthcare Center Using Data-Driven Discrete-Event Simulation

2020 Winter Simulation Conference (WSC) ◽

10.1109/wsc48552.2020.9384093 ◽

2020 ◽

Author(s):

Jules Le Lay ◽

Vincent Augusto ◽

Xiaolan Xie ◽

Edgar Alfonso-Lizarazo ◽

Bienvenu Bongue ◽

...

Keyword(s):

Discrete Event Simulation ◽

Discrete Event ◽

Data Driven ◽

Event Simulation ◽

Healthcare Center ◽

Using Data

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Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches

npj Digital Medicine ◽

10.1038/s41746-021-00465-w ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Brinnae Bent ◽

Peter J. Cho ◽

Maria Henriquez ◽

April Wittmann ◽

Connie Thacker ◽

...

Keyword(s):

Type 2 Diabetes ◽

Data Driven ◽

Self Management ◽

Noninvasive Method ◽

Medical Interventions ◽

Interstitial Glucose ◽

Using Data ◽

Glucose Prediction ◽

Digital Biomarkers

AbstractPrediabetes affects one in three people and has a 10% annual conversion rate to type 2 diabetes without lifestyle or medical interventions. Management of glycemic health is essential to prevent progression to type 2 diabetes. However, there is currently no commercially-available and noninvasive method for monitoring glycemic health to aid in self-management of prediabetes. There is a critical need for innovative, practical strategies to improve monitoring and management of glycemic health. In this study, using a dataset of 25,000 simultaneous interstitial glucose and noninvasive wearable smartwatch measurements, we demonstrated the feasibility of using noninvasive and widely accessible methods, including smartwatches and food logs recorded over 10 days, to continuously detect personalized glucose deviations and to predict the exact interstitial glucose value in real time with up to 84% and 87% accuracy, respectively. We also establish methods for designing variables using data-driven and domain-driven methods from noninvasive wearables toward interstitial glucose prediction.

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