scholarly journals Prediction of Glucose Metabolism Disorder Risk Using a Machine Learning Algorithm: Pilot Study

JMIR Diabetes ◽  
10.2196/10212 ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. e10212 ◽  
Author(s):  
Katsutoshi Maeta ◽  
Yu Nishiyama ◽  
Kazutoshi Fujibayashi ◽  
Toshiaki Gunji ◽  
Noriko Sasabe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Glucose Metabolism ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Metabolism Disorder ◽  
Glucose Metabolism Disorder ◽  
Disorder Risk

Feasibility of Automating Patient Acuity Measurement Using a Machine Learning Algorithm

2016 ◽  
Vol 24 (3) ◽  
pp. 419-427
Author(s):  
Caitlin W. Brennan ◽  
Frank Meng ◽  
Mark M. Meterko ◽  
Leonard W. D’Avolio
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Language Processing ◽  
Gold Standard ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Machine Learning Algorithm ◽  
Patient Acuity ◽  
Electronic Health Record Data ◽  
Main Challenge

Background and Purpose: One method of determining nurse staffing is to match patient demand for nursing care (patient acuity) with available nursing staff. This pilot study explored the feasibility of automating acuity measurement using a machine learning algorithm. Methods: Natural language processing combined with a machine learning algorithm was used to predict acuity levels based on electronic health record data. Results: The algorithm was able to predict acuity relatively well. A main challenge was discordance among nurse raters of acuity in generating a gold standard of acuity before applying the machine learning algorithm. Conclusions: This pilot study tested applying machine learning techniques to acuity measurement and yielded a moderate level of performance. Higher agreement among the gold standard may yield higher performance in future studies.

scholarly journals Classification of emotions based on electrodermal activity and transfer learning - a pilot study

2021 ◽  
Vol 12 (1) ◽  
pp. 178-183
Author(s):  
Fredrik A. Jacobsen ◽  
Ellen W. Hafli ◽  
Christian Tronstad ◽  
Ørjan G. Martinsen
Keyword(s):  
Machine Learning ◽  
Pilot Study ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Electrodermal Activity ◽  
Machine Learning Algorithm ◽  
Electrodermal Response ◽  
Random Forest Classification ◽  
Forest Classification

Abstract This paper describes the development, execution and results of an experiment assessing emotions with electrodermal response measurements and machine learning. With ten participants, the study was carried out by eliciting emotions through film clips. The data was gathered with the Sudologger 3 and processed with continuous wavelet transformation. A machine learning algorithm was used to classify the data with the use of transfer learning and random forest classification. The results showed that the experiment lays a foundation for further exploration in the field. The addition of augmented data strengthened the classification and proved that more data would benefit the machine learning algorithm. The pilot study brought to light several areas to help with the expansion of the study for larger scale assessment of emotions with electrodermal response measurements and machine learning for the benefit of fields like psychology.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

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