Data-Driven Machine Learning Approaches for Predicting In-Hospital Sepsis Mortality

• URL: http://arxiv.org/abs/2408.01612v1
• Date: Sat, 3 Aug 2024 00:28:25 GMT
• Title: Data-Driven Machine Learning Approaches for Predicting In-Hospital Sepsis Mortality
• Authors: Arseniy Shumilov, Yueting Zhu, Negin Ashrafi, Gaojie Lian, Shilong Ren, Maryam Pishgar,
• Abstract summary: This research aims to develop an interpretable and accurate ML model to help clinical professionals predict in-hospital mortality. We analyzed ICU patient records from the MIMIC-III database based on specific criteria and extracted relevant data. The Random Forest model was the most effective in predicting sepsis-related in-hospital mortality.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background: Sepsis is a severe condition responsible for many deaths worldwide. Accurate prediction of sepsis outcomes is crucial for timely and effective treatment. Although previous studies have used ML to forecast outcomes, they faced limitations in feature selection and model comprehensibility, resulting in less effective predictions. Thus, this research aims to develop an interpretable and accurate ML model to help clinical professionals predict in-hospital mortality. Methods: We analyzed ICU patient records from the MIMIC-III database based on specific criteria and extracted relevant data. Our feature selection process included a literature review, clinical input refinement, and using Random Forest to select the top 35 features. We performed data preprocessing, including cleaning, imputation, standardization, and applied SMOTE for oversampling to address imbalance, resulting in 4,683 patients, with admission counts of 17,429. We compared the performance of Random Forest, Gradient Boosting, Logistic Regression, SVM, and KNN models. Results: The Random Forest model was the most effective in predicting sepsis-related in-hospital mortality. It outperformed other models, achieving an accuracy of 0.90 and an AUROC of 0.97, significantly better than the existing literature. Our meticulous feature selection contributed to the model's precision and identified critical determinants of sepsis mortality. These results underscore the pivotal role of data-driven ML in healthcare, especially for predicting in-hospital mortality due to sepsis. Conclusion: This study represents a significant advancement in predicting in-hospital sepsis mortality, highlighting the potential of ML in healthcare. The implications are profound, offering a data-driven approach that enhances decision-making in patient care and reduces in-hospital mortality.

Related papers

• Predicting Mortality and Functional Status Scores of Traumatic Brain Injury Patients using Supervised Machine Learning [0.0]
  Traumatic brain injury (TBI) presents a significant public health challenge, often resulting in mortality or lasting disability. Predicting outcomes such as mortality and Functional Status Scale (FSS) scores can enhance treatment strategies and inform clinical decision-making. This study applies supervised machine learning (ML) methods to predict mortality and FSS scores using a real-world dataset of 300 pediatric TBI patients.
  arXiv  Detail & Related papers  (2024-10-27T00:44:45Z)
• Optimizing Mortality Prediction for ICU Heart Failure Patients: Leveraging XGBoost and Advanced Machine Learning with the MIMIC-III Database [1.5186937600119894]
  Heart failure affects millions of people worldwide, significantly reducing quality of life and leading to high mortality rates. Despite extensive research, the relationship between heart failure and mortality rates among ICU patients is not fully understood. This study analyzed data from 1,177 patients over 18 years old from the MIMIC-III database, identified using ICD-9 codes.
  arXiv  Detail & Related papers  (2024-09-03T07:57:08Z)
• SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
  Sepsis is the leading cause of in-hospital mortality in the USA. Existing predictive models are usually trained on high-quality data with few missing information. For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
  arXiv  Detail & Related papers  (2024-07-24T04:47:36Z)
• Advanced Meta-Ensemble Machine Learning Models for Early and Accurate Sepsis Prediction to Improve Patient Outcomes [0.0]
  This paper examines the limitations of traditional sepsis screening tools like Systemic Inflammatory Response Syndrome, Modified Early Warning Score, and Quick Sequential Organ Failure Assessment. We propose using machine learning techniques - Random Forest, Extreme Gradient Boosting, and Decision Tree models - to predict sepsis onset. Our study evaluates these models individually and in a combined meta-ensemble approach using key metrics such as Accuracy, Precision, Recall, F1 score, and Area Under the Receiver Operating Characteristic Curve.
  arXiv  Detail & Related papers  (2024-07-11T00:51:32Z)
• Clinical Deterioration Prediction in Brazilian Hospitals Based on Artificial Neural Networks and Tree Decision Models [56.93322937189087]
  An extremely boosted neural network (XBNet) is used to predict clinical deterioration (CD) The XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data.
  arXiv  Detail & Related papers  (2022-12-17T23:29:14Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score [59.75318183140857]
  Estimating causal effects from randomized experiments is central to clinical research. Most methods for historical borrowing achieve reductions in variance by sacrificing strict type-I error rate control.
  arXiv  Detail & Related papers  (2020-12-17T21:10:10Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• Individualized Prediction of COVID-19 Adverse outcomes with MLHO [9.197411456718708]
  We developed an end-to-end Machine Learning framework that leverages iterative feature and algorithm selection to predict Health outcomes. We modeled the four adverse outcomes utilizing about 600 features representing patients' pre-COVID health records and demographics. Our results demonstrated that while demographic variables are important predictors of adverse outcomes after a COVID-19 infection, the incorporation of the past clinical records are vital for a reliable prediction model.
  arXiv  Detail & Related papers  (2020-08-10T02:44:52Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.