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Current mortality prediction models and scoring systems for intensive care unit patients are generally usable only after at least 24 or 48 h of admission, as some parameters are unclear at admission. However, some of the most relevant measurements are available shortly following admission. It is hypothesized that outcome prediction may be made using information available in the earliest phase of intensive care unit admission. This study aims to investigate how early hospital mortality can be predicted for intensive care unit patients. We conducted a thorough time-series analysis on the performance of different data mining methods during the first 48 h of intensive care unit admission. The results showed that the discrimination power of the machine-learning classification methods after 6 h of admission outperformed the main scoring systems used in intensive care medicine (Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score and Sequential Organ Failure Assessment) after 48 h of admission.

Background: The National Early Warning Score (NEWS/NEWS 2) has been adopted across the National Health Service (NHS) in the U.K. as a method of escalating care for deteriorating patients. Intensive Care Unit (ICU) resources are limited and in high demand, with patient discharge a focal point for managing resources effectively. There are currently no universally accepted methods for assessing discharge of patients from an ICU, which can cause premature discharges and put patients at risk of subsequent deterioration, readmission to ICU or death., Methods: We tested the ability of the NEWS to discriminate patients within 24h of admission to an ICU in a U.S. hospital during 2001-2012, by their end discharge location: home; hospital ward; nursing facility; hospice and death. The NEWS performance was compared across five different ICU specialties, using the area under the receiver operating characteristic (AUROC) curve and a large vital signs database (n=2,723,055) collected from 28,523 critical care admissions., Results: The NEWS AUROC (95% CI) at 24h following admission: all patients 0.727 (0.709-0.745); Coronary Care Unit (CCU) 0.829 (0.821-0.837); Cardiac Surgery Recovery Unit (CSRU) 0.844 (0.838-0.850); Medical Intensive Care Unit (MICU) 0.778 (0.767-0.791); Surgical Intensive Care Unit (SICU) 0.775 (0.762-0.788); Trauma Surgical Intensive Care Unit (TSICU) 0.765 (0.751-0.773)., Conclusions: The NEWS has reasonable discrimination for any ICU patient's discharge location. The NEWS has greater ability to discriminate patients in the Coronary Care Unit (CCU) and Cardiac Surgery Recovery Unit (CSRU) compared to other ICU specialties. The NEWS has the real potential to be applied within a universal discharge planning tool for ICU, improving patient safety at the point of discharge.

The class imbalance issue has been a persistent problem in machine learning that hinders the accurate predictive analysis of data in many real-world applications. The class imbalance problem exists when the number of instances present in a class (or classes) is significantly fewer than the number of instances belonging to another class (or classes). Sufficiently recognizing the minority class during classification is a problem as most algorithms employed to learn from data input are biased toward the majority class. The underlying issue is made more complex with the presence of data difficult factors embedded in such data input. This paper presents a novel and effective ensemble-based method for dealing with the class imbalance problem. This paper is motivated by the idea of moving the oversampling from the data level to the algorithm level, instead of increasing the minority instances in the data sets, the algorithms in this paper aims to "oversample the classification ensemble" by increasing the number of classifiers that represent the minority class in the ensemble, i.e., random forest. The proposed biased random forest algorithm employs the nearest neighbor algorithm to identify the critical areas in a given data set. The standard random forest is then fed with more random trees generated based on the critical areas. The results show that the proposed algorithm is very effective in dealing with the class imbalance problem.

Background: Mortality prediction of hospitalized patients is an important problem. Over the past few decades, several severity scoring systems and machine learning mortality prediction models have been developed for predicting hospital mortality. By contrast, early mortality prediction for intensive care unit patients remains an open challenge. Most research has focused on severity of illness scoring systems or data mining (DM) models designed for risk estimation at least 24 or 48h after ICU admission., Objectives: This study highlights the main data challenges in early mortality prediction in ICU patients and introduces a new machine learning based framework for Early Mortality Prediction for Intensive Care Unit patients (EMPICU)., Materials and Methods: The proposed method is evaluated on the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database. Mortality prediction models are developed for patients at the age of 16 or above in Medical ICU (MICU), Surgical ICU (SICU) or Cardiac Surgery Recovery Unit (CSRU). We employ the ensemble learning Random Forest (RF), the predictive Decision Trees (DT), the probabilistic Naive Bayes (NB) and the rule-based Projective Adaptive Resonance Theory (PART) models. The primary outcome was hospital mortality. The explanatory variables included demographic, physiological, vital signs and laboratory test variables. Performance measures were calculated using cross-validated area under the receiver operating characteristic curve (AUROC) to minimize bias. 11,722 patients with single ICU stays are considered. Only patients at the age of 16 years old and above in Medical ICU (MICU), Surgical ICU (SICU) or Cardiac Surgery Recovery Unit (CSRU) are considered in this study., Results: The proposed EMPICU framework outperformed standard scoring systems (SOFA, SAPS-I, APACHE-II, NEWS and qSOFA) in terms of AUROC and time (i.e. at 6h compared to 48h or more after admission)., Discussion and Conclusion: The results show that although there are many values missing in the first few hour of ICU admission, there is enough signal to effectively predict mortality during the first 6h of admission. The proposed framework, in particular the one that uses the ensemble learning approach - EMPICU Random Forest (EMPICU-RF) offers a base to construct an effective and novel mortality prediction model in the early hours of an ICU patient admission, with an improved performance profile., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Over the past few years, there has been increased interest in data mining and machine learning methods to improve hospital performance, in particular hospitals want to improve their intensive care unit statistics by reducing the number of patients dying inside the intensive care unit. Research has focused on prediction of measurable outcomes, including risk of complications, mortality and length of hospital stay. The length of stay is an important metric both for healthcare providers and patients, influenced by numerous factors. In particular, the length of stay in critical care is of great significance, both to patient experience and the cost of care, and is influenced by factors specific to the highly complex environment of the intensive care unit. The length of stay is often used as a surrogate for other outcomes, where those outcomes cannot be measured; for example as a surrogate for hospital or intensive care unit mortality. The length of stay is also a parameter, which has been used to identify the severity of illnesses and healthcare resource utilisation. This paper examines a range of length of stay and mortality prediction applications in acute medicine and the critical care unit. It also focuses on the methods of analysing length of stay and mortality prediction. Moreover, the paper provides a classification and evaluation for the analytical methods of the length of stay and mortality prediction associated with a grouping of relevant research papers published in the years 1984 to 2016 related to the domain of survival analysis. In addition, the paper highlights some of the gaps and challenges of the domain.