Tensor learning of pointwise mutual information from EHR data for early prediction of sepsis

Nesaragi, Naimahmed; Aggarwal, Vaneet

doi:10.1016/j.compbiomed.2021.104430

Cited by 25 publications

(10 citation statements)

References 61 publications

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“…Then, we used pointwise mutual information (PMI) to calculate the similarity of all the words and to then extract the 300 words with the highest co-occurrence frequency. In particular, PMI is a quantitative and systematic approach that measures the correlation between two items and is also an unsupervised machine learning method suitable for performing topic modeling [ 42 ]. PMI utilizes the similarity between words to build a word relationship network, and to then carry out a thematic clustering analysis [ 43 , 44 , 45 ].…”

Section: Methodsmentioning

confidence: 99%

Exploring Public Discussions Regarding COVID-19 Vaccinations on Microblogs in China: Findings from Machine Learning Algorithms

Dang

2022

IJERPH

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Large-scale, widespread COVID-19 vaccination is the most effective means of cutting off the spread of the novel coronavirus and establishing an immune barrier. Due to the large population base in China, it has been a very difficult task to establish such an immune barrier. Therefore, this study aims to explore the public’s discussions related to COVID-19 vaccinations on microblogs and to detect their sentiments toward COVID-19 vaccination so as to improve the vaccination rate in China. This study employed machine learning methods in the field of artificial intelligence to analyze mass data obtained from SinaWeibo. A total of 1,478,875 valid microblog texts were collected between December 2020 and June 2022, the results of which indicated that: (1) overall, negative texts (38.7%) slightly outweighed positive texts (36.1%); “Good” (63%) dominated positive texts, while “disgust” (44.6%) and “fear” (35.8%) dominated negative texts; (2) six overarching themes related to COVID-19 vaccination were identified: public trust in the Chinese government, changes in daily work and study, vaccine economy, international COVID-19 vaccination, the COVID-19 vaccine’s R&D, and COVID-19 vaccination for special groups. These themes and sentiments can clarify the public’s reactions to COVID-19 vaccination and help Chinese officials’ response to vaccine hesitancy. Furthermore, this study seeks to make up for the lack of focus on big data in public health and epidemiology research, and to provide novel insights for future studies.

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Section: Methodsmentioning

confidence: 99%

Exploring Public Discussions Regarding COVID-19 Vaccinations on Microblogs in China: Findings from Machine Learning Algorithms

Dang

2022

IJERPH

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“…Here, a two-stage framework is developed; in the first stage, a machine learning model using statistical and particularly Kolmogorov–Smirnov tests is paired, whereas the second stage predicts whether a patient would develop sepsis. Nesaragi et al discussed a tensor learning of pointwise mutual information from electronic health records (HER) data for early sepsis prediction [ 16 ]. The EHR data of clinical covariates capture both linear relationships and nonlinear correlation for the early sepsis prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients

Singh

Singh²,

Khan³

et al. 2022

Journal of Healthcare Engineering

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In today’s scenario, sepsis is impacting millions of patients in the intensive care unit due to the fact that the mortality rate is increased exponentially and has become a major challenge in the field of healthcare. Such peoples require determinant care which increases the cost of the treatment by using a large number of resources because of the nonavailability of the resources. The treatment of sepsis is available in the early state, but treatment is not started at the right time, and then it converts to the advanced level of sepsis and increases the fatalities. Thus, an intensive analysis is required to detect and identify sepsis at the early stage. There are some models available that work based on the manual score and based on only the biomark features, but these are not fully automated. Some machine learning-based models are also available, which can reduce the mortality rate, but accuracy is not up to date. This paper proposes a machine learning model for early detecting and predicting sepsis in intensive care unit patients. Various models, random forest (RF), linear regression (LR), support vector machine (SVM), naive Bayes (NB), ensemble (of SVM, RF, NB, and LR), XGBoost, and proposed ensemble (of SVM, RF, NB, LR, and XGBoost), are simulated by using the collected data from intensive care unit patient’s database that is based on the clinical laboratory values and vital signs. The performance of the models is evaluated by considering the same datasets. The balanced accuracy of RF, LR, SVM, NB, ensemble (of SVM, RF, NB, and LR), XGBoost, and proposed ensemble (of SVM, RF, NB, LR, and XGBoost) is 0.90, 0.73, 0.93, 0.74, 0.94, 0.95, and 0.96, respectively. It is also evident from the experimental results that the proposed ensemble model performs well as compared to the other models.

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“…Moreover, we did not employ advanced deep learning method in both the recognition of sepsis and the prediction of mortality as described in previous studies. 38–42 However, standardized clinical criteria has been shown to have good reliability for identifying sepsis in the EMR 43,44 and trade-off between data complexity and model interpretation also exists in deep learning algorithm. 45,46 Third, in-hospital mortality could be biased by hospital discharge practice and length of hospital stay, 47 and may not necessarily reflect the quality of care.…”

Section: Discussionmentioning

confidence: 99%

Risk-standardized sepsis mortality map of the United States

Lee

et al. 2022

DIGITAL HEALTH

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Objective Sepsis is the leading cause of in-hospital mortality in the United States (US). Quality improvement initiatives for improving sepsis care depend on accurate estimates of sepsis mortality. While hospital 30-day risk-standardized mortality rates have been published for patients hospitalized with acute myocardial infarction, heart failure, and pneumonia, risk-standardized mortality rates for sepsis have not been well characterized. We aimed to construct a sepsis risk-standardized mortality rate map for the United States, to illustrate disparities in sepsis care across the country. Methods This cross-sectional study included adults from the US Nationwide Inpatient Sample who were hospitalized with sepsis between 1 January 2010 and 30 December 2011. Hospital-level risk-standardized mortality rates were calculated using hierarchical logistic modelling, and were risk-adjusted with predicted mortality derived from (1) the Sepsis Risk Prediction Score, a logistic regression model, and (2) gradient-boosted decision trees, a supervised machine learning (ML) algorithm. Results Among 1,739,033 adults hospitalized with sepsis, 50% were female, and the median age was 71 years (interquartile range: 58–81). The national median risk-standardized mortality rate for sepsis was 18.4% (interquartile range: 17.0, 21.0) by the boosted tree model, which had better discrimination than the Sepsis Risk Prediction Score model (C-statistic 0.87 and 0.78, respectively). The highest risk-standardized mortality rates were found in Wyoming, North Dakota, and Mississippi, while the lowest were found in Arizona, Colorado, and Michigan. Conclusions Wide variation exists in sepsis risk-standardized mortality rates across states, representing opportunities for improvement in sepsis care. This represents the first map of state-level variation of risk-standardized mortality rates in sepsis.

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Tensor learning of pointwise mutual information from EHR data for early prediction of sepsis

Cited by 25 publications

References 61 publications

Exploring Public Discussions Regarding COVID-19 Vaccinations on Microblogs in China: Findings from Machine Learning Algorithms

Exploring Public Discussions Regarding COVID-19 Vaccinations on Microblogs in China: Findings from Machine Learning Algorithms

A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients

Risk-standardized sepsis mortality map of the United States

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