scholarly journals Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties

APL Materials ◽  
2016 ◽  
Vol 4 (5) ◽  
pp. 053213 ◽  
Author(s):  
Michael W. Gaultois ◽  
Anton O. Oliynyk ◽  
Arthur Mar ◽  
Taylor D. Sparks ◽  
Gregory J. Mulholland ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Thermoelectric Materials ◽  
Learning Models ◽  
Web Based ◽  
Materials Properties ◽  
Machine Learning Models

Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore

2021 ◽  
Vol 12 (02) ◽  
pp. 372-382
Author(s):  
Christine Xia Wu ◽  
Ernest Suresh ◽  
Francis Wei Loong Phng ◽  
Kai Pik Tai ◽  
Janthorn Pakdeethai ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Real Time ◽  
Risk Score ◽  
Patient Specific ◽  
Learning Models ◽  
Medicine Department ◽  
High Risk Patients ◽  
Risk Patients ◽  
Machine Learning Models

Abstract Objective To develop a risk score for the real-time prediction of readmissions for patients using patient specific information captured in electronic medical records (EMR) in Singapore to enable the prospective identification of high-risk patients for enrolment in timely interventions. Methods Machine-learning models were built to estimate the probability of a patient being readmitted within 30 days of discharge. EMR of 25,472 patients discharged from the medicine department at Ng Teng Fong General Hospital between January 2016 and December 2016 were extracted retrospectively for training and internal validation of the models. We developed and implemented a real-time 30-day readmission risk score generation in the EMR system, which enabled the flagging of high-risk patients to care providers in the hospital. Based on the daily high-risk patient list, the various interfaces and flow sheets in the EMR were configured according to the information needs of the various stakeholders such as the inpatient medical, nursing, case management, emergency department, and postdischarge care teams. Results Overall, the machine-learning models achieved good performance with area under the receiver operating characteristic ranging from 0.77 to 0.81. The models were used to proactively identify and attend to patients who are at risk of readmission before an actual readmission occurs. This approach successfully reduced the 30-day readmission rate for patients admitted to the medicine department from 11.7% in 2017 to 10.1% in 2019 (p < 0.01) after risk adjustment. Conclusion Machine-learning models can be deployed in the EMR system to provide real-time forecasts for a more comprehensive outlook in the aspects of decision-making and care provision.

scholarly journals Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations

2020 ◽  
Vol 143 ◽  
pp. 113083 ◽  
Author(s):  
Oscar J. Pellicer-Valero ◽  
María José Rupérez ◽  
Sandra Martínez-Sanchis ◽  
José D. Martín-Guerrero
Keyword(s):  
Machine Learning ◽  
Finite Element Method ◽  
Finite Element ◽  
Real Time ◽  
Biomechanical Modeling ◽  
Learning Models ◽  
Machine Learning Models ◽  
Element Method

scholarly journals A Universal Screening Tool for Dyslexia by a Web-Game and Machine Learning

2022 ◽  
Vol 3 ◽  
Author(s):  
Maria Rauschenberger ◽  
Ricardo Baeza-Yates ◽  
Luz Rello
Keyword(s):  
Machine Learning ◽  
Early Intervention ◽  
Universal Screening ◽  
User Study ◽  
Future Research ◽  
Learning Models ◽  
Early Screening ◽  
Web Based ◽  
Negative Side Effects ◽  
Machine Learning Models

Children with dyslexia have difficulties learning how to read and write. They are often diagnosed after they fail school even if dyslexia is not related to general intelligence. Early screening of dyslexia can prevent the negative side effects of late detection and enables early intervention. In this context, we present an approach for universal screening of dyslexia using machine learning models with data gathered from a web-based language-independent game. We designed the game content taking into consideration the analysis of mistakes of people with dyslexia in different languages and other parameters related to dyslexia like auditory perception as well as visual perception. We did a user study with 313 children (116 with dyslexia) and train predictive machine learning models with the collected data. Our method yields an accuracy of 0.74 for German and 0.69 for Spanish as well as a F1-score of 0.75 for German and 0.75 for Spanish, using Random Forests and Extra Trees, respectively. We also present the game content design, potential new auditory input, and knowledge about the design approach for future research to explore Universal screening of dyslexia. universal screening with language-independent content can be used for the screening of pre-readers who do not have any language skills, facilitating a potential early intervention.

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