A Supervised Learning Approach for ICU Mortality Prediction Based on Unstructured Electrocardiogram Text Reports

2018 ◽  
pp. 126-134 ◽  
Author(s):  
Gokul S. Krishnan ◽  
S. Sowmya Kamath
Keyword(s):  
Supervised Learning ◽  
Mortality Prediction ◽  
Learning Approach ◽  
Icu Mortality

A Supervised Learning Approach for Dynamic Sampling (SLADS) in Raman Hyperspectral Imaging

2018 ◽  
Vol 2018 (15) ◽  
pp. 132-1-1323
Author(s):  
Shijie Zhang ◽  
Zhengtian Song ◽  
G. M. Dilshan P. Godaliyadda ◽  
Dong Hye Ye ◽  
Atanu Sengupta ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Hyperspectral Imaging ◽  
Learning Approach ◽  
Dynamic Sampling

scholarly journals High mortality rate of obstetric critically ill women in Rwanda and its predictability

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alcade Rudakemwa ◽  
Amyl Lucille Cassidy ◽  
Théogène Twagirumugabe
Keyword(s):  
Prediction Models ◽  
Average Length ◽  
Mortality Prediction ◽  
Routine Practice ◽  
Icu Mortality ◽  
Resource Limited ◽  
Obstetric Haemorrhage ◽  
Tertiary Hospitals ◽  
Mortality Prediction Models ◽  
Qsofa Score

Abstract Background Reasons for admission to intensive care units (ICUs) for obstetric patients vary from one setting to another. Outcomes from ICU and prediction models are not well explored in Rwanda owing to lack of appropriate scores. This study aimed to assess reasons for admission and accuracy of prediction models for mortality of obstetric patients admitted to ICUs of two public tertiary hospitals in Rwanda. Methods We prospectively collected data from all obstetric patients admitted to the ICUs of the two public tertiary hospitals in Rwanda from March 2017 to February 2018 to identify reasons for admission, demographic and clinical characteristics, outcome including death and its predictability by both the Modified Early Obstetric Warning Score (MEOWS) and quick Sequential Organ Failure Assessment (qSOFA). We analysed the accuracy of mortality prediction models by MEOWS or qSOFA by using logistic regression adjusting for factors associated with mortality. Area under the Receiver Operating characteristic (AUROC) curves is used to show the predicting capacity for each individual tool. Results Obstetric patients (n = 94) represented 12.8 % of all 747 ICU admissions which is 1.8 % of all 4.999 admitted women for pregnancy or labor. Sepsis (n = 30; 31.9 %) and obstetric haemorrhage (n = 24; 25.5 %) were the two commonest reasons for ICU admission. Overall ICU mortality for obstetric patients was 54.3 % (n = 51) with average length of stay of 6.6 ± 7.525 days. MEOWS score was an independent predictor of mortality (adjusted (a)OR 1.25; 95 % CI 1.07–1.46) and so was qSOFA score (aOR 2.81; 95 % CI 1.25–6.30) with an adjusted AUROC of 0.773 (95 % CI 0.67–0.88) and 0.764 (95 % CI 0.65–0.87), indicating fair accuracy for ICU mortality prediction in these settings of both MEOWS and qSOFA scores. Conclusions Sepsis and obstetric haemorrhage were the commonest reasons for obstetric admissions to ICU in Rwanda. MEOWS and qSOFA scores could accurately predict ICU mortality of obstetric patients in resource-limited settings, but larger studies are needed before a recommendation for their use in routine practice in similar settings.

scholarly journals Fingerprinting Mobile Devices Using Personalized Configurations

2016 ◽  
Vol 2016 (1) ◽  
pp. 4-19 ◽  
Author(s):  
Andreas Kurtz ◽  
Hugo Gascon ◽  
Tobias Becker ◽  
Konrad Rieck ◽  
Felix Freiling
Keyword(s):  
Supervised Learning ◽  
Mobile Devices ◽  
Real World ◽  
Third Party ◽  
Learning Approach ◽  
Total Accuracy ◽  
Over Time

Abstract Recently, Apple removed access to various device hardware identifiers that were frequently misused by iOS third-party apps to track users. We are, therefore, now studying the extent to which users of smartphones can still be uniquely identified simply through their personalized device configurations. Using Apple’s iOS as an example, we show how a device fingerprint can be computed using 29 different configuration features. These features can be queried from arbitrary thirdparty apps via the official SDK. Experimental evaluations based on almost 13,000 fingerprints from approximately 8,000 different real-world devices show that (1) all fingerprints are unique and distinguishable; and (2) utilizing a supervised learning approach allows returning users or their devices to be recognized with a total accuracy of 97% over time

Household Profile Identification for Behavioral Demand Response: A Semi-supervised Learning Approach Using Smart Meter Data

Energy ◽  
2021 ◽  
pp. 121728
Author(s):  
Fei Wang ◽  
Xiaoxing Lu ◽  
Xiqiang Chang ◽  
Xin Cao ◽  
Siqing Yan ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Demand Response ◽  
Learning Approach ◽  
Smart Meter

A Supervised Learning Approach to Search of Definitions

2006 ◽  
Vol 21 (3) ◽  
pp. 439-449 ◽  
Author(s):  
Jun Xu ◽  
Yun-Bo Cao ◽  
Hang Li ◽  
Min Zhao ◽  
Ya-Lou Huang
Keyword(s):  
Supervised Learning ◽  
Learning Approach
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