19,535 research outputs found
Real-time data mining models for predicting length of stay in intensive care units
Nowadays the efficiency of costs and resources planning in hospitals embody a critical role in the
management of these units. Length Of Stay (LOS) is a good metric when the goal is to decrease costs and to
optimize resources. In Intensive Care Units (ICU) optimization assumes even a greater importance derived
from the high costs associated to inpatients. This study presents two data mining approaches to predict LOS
in an ICU. The first approach considered the admission variables and some other physiologic variables
collected during the first 24 hours of inpatient. The second approach considered admission data and
supplementary clinical data of the patient (vital signs and laboratory results) collected in real-time. The
results achieved in the first approach are very poor (accuracy of 73 %). However, when the prediction is
made using the data collected in real-time, the results are very interesting (sensitivity of 96.104%). The
models induced in second experiment are sensitive to the patient clinical situation and can predict LOS
according to the monitored variables. Models for predicting LOS at admission are not suited to the ICU
particularities. Alternatively, they should be induced in real-time, using online-learning and considering the
most recent patient condition when the model is induced.(undefined
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU
Machine learning approaches have been effective in predicting adverse
outcomes in different clinical settings. These models are often developed and
evaluated on datasets with heterogeneous patient populations. However, good
predictive performance on the aggregate population does not imply good
performance for specific groups.
In this work, we present a two-step framework to 1) learn relevant patient
subgroups, and 2) predict an outcome for separate patient populations in a
multi-task framework, where each population is a separate task. We demonstrate
how to discover relevant groups in an unsupervised way with a
sequence-to-sequence autoencoder. We show that using these groups in a
multi-task framework leads to better predictive performance of in-hospital
mortality both across groups and overall. We also highlight the need for more
granular evaluation of performance when dealing with heterogeneous populations.Comment: KDD 201
Early hospital mortality prediction using vital signals
Early hospital mortality prediction is critical as intensivists strive to
make efficient medical decisions about the severely ill patients staying in
intensive care units. As a result, various methods have been developed to
address this problem based on clinical records. However, some of the laboratory
test results are time-consuming and need to be processed. In this paper, we
propose a novel method to predict mortality using features extracted from the
heart signals of patients within the first hour of ICU admission. In order to
predict the risk, quantitative features have been computed based on the heart
rate signals of ICU patients. Each signal is described in terms of 12
statistical and signal-based features. The extracted features are fed into
eight classifiers: decision tree, linear discriminant, logistic regression,
support vector machine (SVM), random forest, boosted trees, Gaussian SVM, and
K-nearest neighborhood (K-NN). To derive insight into the performance of the
proposed method, several experiments have been conducted using the well-known
clinical dataset named Medical Information Mart for Intensive Care III
(MIMIC-III). The experimental results demonstrate the capability of the
proposed method in terms of precision, recall, F1-score, and area under the
receiver operating characteristic curve (AUC). The decision tree classifier
satisfies both accuracy and interpretability better than the other classifiers,
producing an F1-score and AUC equal to 0.91 and 0.93, respectively. It
indicates that heart rate signals can be used for predicting mortality in
patients in the ICU, achieving a comparable performance with existing
predictions that rely on high dimensional features from clinical records which
need to be processed and may contain missing information.Comment: 11 pages, 5 figures, preprint of accepted paper in IEEE&ACM CHASE
2018 and published in Smart Health journa
Predictive analytics for hospital discharge flow determination
In recent years, hospitals around the world are faced with large patient flows, which negatively affect the quality of patient care and become a crucial factor to consider in inpatient management. The main objective of this management is to maximize the number of available beds, using efficient planning. Intensive Care Units (ICU) are hospital units with a higher monetary consumption, and the importance of indicators that allow the achievement of useful information for a correct management is critical. This study allowed the prediction of the Length of Stay (LOS) based on their demographic data, information collected at the time of admission and clinical conditions, which can help health professionals in conducting a more assertive planning and a better quality service. The results obtained show that Machine Learning (ML) models, using demographic information simultaneously with the patient's pathway, as well as clinical data, drugs, tests and analysis, introduce a greater predictive ability for LOS.FCT -Fundação para a Ciência e a Tecnologia(DSAIPA/DS/0084/2018
- …