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OBJECTIVES/SPECIFIC AIMS: To learn the edit distance costs of a symbolic univariate time series representation through a stochastic finite-state transducer to predict patient outcomes in intensive care units. METHODS/STUDY POPULATION: High frequency data of patients in intensive care units were used as a data set. The nearest neighbor method with edit distance costs (learned by the FST) were used to classify the patient status within an hour after 10 hours of data. Several experiments were developed to estimate the parameters that better fit the model regarding the prediction metrics. RESULTS/ANTICIPATED RESULTS: Different metrics were obtained for the several parameters. These metrics were metrics (ie, accuracy, precision, and F-measure). DISCUSSION/SIGNIFICANCE OF IMPACT: Our best results are compared with published works, where most of the metrics (ie, accuracy, precision, and F-measure) were improved.

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Bootstrapping a Neural Morphological Generator from Morphological Analyzer Output for Inuktitut

10.33011/computel.v2i.455 ◽

2019 ◽

Vol 2 (1) ◽

Author(s):

Jeffrey Micher

Keyword(s):

Neural Network ◽

Training Data ◽

Data Set ◽

Set Size ◽

The Neural Network ◽

Surface Character ◽

Finite State ◽

Character Sequences ◽

Finite State Transducer

We present a method for building a morphological generator from the output of an existing analyzer for Inuktitut, in the absence of a two-way finite state transducer which would normally provide this functionality. We make use of a sequence to sequence neural network which “translates” underlying Inuktitut morpheme sequences into surface character sequences. The neural network uses only the previous and the following morphemes as context. We report a morpheme accuracy of approximately 86%. We are able to increase this accuracy slightly by passing deep morphemes directly to output for unknown morphemes. We do not see significant improvement when increasing training data set size, and postulate possible causes for this.

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Superiority of Supervised Machine Learning on Reading Chest X-Rays in Intensive Care Units

Frontiers in Medicine ◽

10.3389/fmed.2021.676277 ◽

2021 ◽

Vol 8 ◽

Author(s):

Kumiko Tanaka ◽

Taka-aki Nakada ◽

Nozomi Takahashi ◽

Takahiro Dozono ◽

Yuichiro Yoshimura ◽

...

Keyword(s):

Machine Learning ◽

Intensive Care ◽

Pleural Effusion ◽

Diagnostic Accuracy ◽

Intensive Care Units ◽

Area Under The Curve ◽

Chest Radiographs ◽

Data Set ◽

Portable Chest ◽

Icu Physicians

Purpose: Portable chest radiographs are diagnostically indispensable in intensive care units (ICU). This study aimed to determine if the proposed machine learning technique increased in accuracy as the number of radiograph readings increased and if it was accurate in a clinical setting.Methods: Two independent data sets of portable chest radiographs (n = 380, a single Japanese hospital; n = 1,720, The National Institution of Health [NIH] ChestX-ray8 dataset) were analyzed. Each data set was divided training data and study data. Images were classified as atelectasis, pleural effusion, pneumonia, or no emergency. DenseNet-121, as a pre-trained deep convolutional neural network was used and ensemble learning was performed on the best-performing algorithms. Diagnostic accuracy and processing time were compared to those of ICU physicians.Results: In the single Japanese hospital data, the area under the curve (AUC) of diagnostic accuracy was 0.768. The area under the curve (AUC) of diagnostic accuracy significantly improved as the number of radiograph readings increased from 25 to 100% in the NIH data set. The AUC was higher than 0.9 for all categories toward the end of training with a large sample size. The time to complete 53 radiographs by machine learning was 70 times faster than the time taken by ICU physicians (9.66 s vs. 12 min). The diagnostic accuracy was higher by machine learning than by ICU physicians in most categories (atelectasis, AUC 0.744 vs. 0.555, P < 0.05; pleural effusion, 0.856 vs. 0.706, P < 0.01; pneumonia, 0.720 vs. 0.744, P = 0.88; no emergency, 0.751 vs. 0.698, P = 0.47).Conclusions: We developed an automatic detection system for portable chest radiographs in ICU setting; its performance was superior and quite faster than ICU physicians.

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Compassionate care during withdrawal of treatment: A secondary analysis of ICU nurses' experiences

Nursing Ethics ◽

10.1177/0969733016687159 ◽

2017 ◽

Vol 25 (8) ◽

pp. 1075-1086 ◽

Cited By ~ 2

Author(s):

Nikolaos Efstathiou ◽

Jonathan Ives

Keyword(s):

Intensive Care ◽

Intensive Care Units ◽

Secondary Analysis ◽

Treatment Withdrawal ◽

Primary Study ◽

Compassionate Care ◽

Large Hospital ◽

Data Set ◽

Withdrawal Of Treatment ◽

The Family

Background: Withdrawal of treatment is a common practice in intensive care units when treatment is considered futile. Compassion is an important aspect of care; however, it has not been explored much within the context of treatment withdrawal in intensive care units. Objectives: The aim was to examine how concepts of compassion are framed, utilised and communicated by intensive care nurses in the context of treatment withdrawal. Design: The study employed a qualitative approach conducting secondary analysis of an original data set. In the primary study, 13 nurses were recruited from three intensive care units within a large hospital in United Kingdom. Deductive framework analysis was used to analyse the data in relation to compassionate care. Ethical considerations: The primary study was approved by the local Research Ethics Committee and the hospital’s Research and Development services. Findings: Compassionate care was mostly directed to the patient’s family and was demonstrated through care and emotional support to the family. It was predominantly expressed through attempts to maintain the patient’s dignity by controlling symptoms, maintaining patient cleanliness and removing technical apparatus. Conclusion: This study’s findings provide insight about compassionate care during treatment withdrawal which could help to understand and develop further clinicians’ roles. Prioritising the family over the patient raised concerns among nurses, who motivated by compassion, may feel justified in taking measures that are in the interests of the family rather than the patient. Further work is needed to explore the ethics of this.

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