ArticleInternational journal of medical informatics2020
Predicting in-hospital mortality of patients with febrile neutropenia using machine learning models.
Article in International journal of medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review.Journal of clinical epidemiology · 2023Pooled it
- Artificial intelligence for early sepsis detection and dynamic prognostication in onco-critical care.World journal of critical care medicine · 2026Review
- Machine Learning for Prediction of High-Risk Infections in Patients With Cancer.Cancer medicine · 2026Article
- In-hospital mortality prognostication for cancer patients with febrile neutropenia.Communications medicine · 2025Article
- Optimizing nursing care for neutropenic patients: a holistic perspective on education, support, and clinical management.Frontiers in medicine · 2025Review
- Classifying early infant feeding status from clinical notes using natural language processing and machine learning.Scientific reports · 2024Article
- Machine Learning Meets Cancer.Cancers · 2024Review
- Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape.Critical care clinics · 2023Review
- Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer Care.International journal of environmental research and public health · 2022Article
- The Prognostic Utility of Lymphocyte-Based Measures and Ratios in Chemotherapy-Induced Febrile Neutropenia Patients following Granulocyte Colony-Stimulating Factor Therapy.Medicina (Kaunas, Lithuania) · 2022Article
- A Computer-Assisted System for Early Mortality Risk Prediction in Patients with Traumatic Brain Injury Using Artificial Intelligence Algorithms in Emergency Room Triage.Brain sciences · 2022Article
- Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.Applied clinical informatics · 2022Review
- Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults.International journal of environmental research and public health · 2021Article
- Using machine learning analysis to assist in differentiating between necrotizing enterocolitis and spontaneous intestinal perforation: A novel predictive analytic tool.Journal of pediatric surgery · 2021Article
- Personalized predictive models for symptomatic COVID-19 patients using basic preconditions: Hospitalizations, mortality, and the need for an ICU or ventilator.International journal of medical informatics · 2020Article
- Personalized Predictive Models for Symptomatic COVID-19 Patients Using Basic Preconditions: Hospitalizations, Mortality, and the Need for an ICU or Ventilator.medRxiv : the preprint server for health sciences · 2020Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundFebrile neutropenia (FN) has been associated with high mortality among adults with cancer. Current systems for early detection of inpatient FN mortality are based on scoring indexes that require intensive physicians' subjective evaluation.
objectiveIn this study, we leveraged machine learning techniques to build a FN mortality risk evaluation tool focused on FN admissions without physicians' subjective evaluation.
methodsWe used the National Inpatient Sample and Nationwide Inpatient Sample (NIS) that included mortality data among adult inpatients who were diagnosed with FN during a hospital admission. Machine learning techniques that we compared included linear models (ridge logistic regression and linear support vector machine) and non-linear models (gradient boosting tree and neural network). The primary outcome for this study was death among individuals with a recorded FN admission. Model comparison was evaluated based on areas under the receiver operating characteristic curve (AUROC) and model performance was estimated using 30 % test set created via stratified split.
resultsOur analysis detected 126,013 adult admissions within the NIS data that were diagnosed with FN, among which 5,856 were declared as deceased (4.6 %). Our machine learning results demonstrate linear models and non-linear models achieved areas under the receiver operating characteristic (AUROC) around 92 % in survival prediction.
conclusionsWe developed machine learning models that do not require physicians' subjective evaluation for FN mortality risk prediction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.