439 research outputs found
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
Automated Mortality Prediction in Critically-ill Patients with Thrombosis using Machine Learning
Venous thromboembolism (VTE) is the third most
common cardiovascular condition. Some high risk patients diagnosed with VTE need immediate treatment and monitoring
in intensive care units (ICU) as the mortality rate is high.
Most of the published predictive models for ICU mortality give
information on in-hospital mortality using data recorded in the
first day of ICU admission. The purpose of the current study is to
predict in-hospital and after-discharge mortality in patients with
VTE admitted to ICU using a machine learning (ML) framework.
We studied 2,468 patients from the Medical Information Mart
for Intensive Care (MIMIC-III) database, admitted to ICU with
a diagnosis of VTE. We formed ML classification tasks for
early and late mortality prediction. In total, 1,471 features were
extracted for each patient, grouped in seven categories each
representing a different type of medical assessment. We used an
automated ML platform, JADBIO, as well as a class balancing
combined with a Random Forest classifier, in order to evaluate the
importance of class imbalance. Both methods showed significant
ability in prediction of early mortality (AUC=0.92). Nevertheless,
the task of predicting late mortality was less efficient (AUC=0.82).
To the best of our knowledge, this is the first study in which
ML is used to predict short-term and long-term mortality for
ICU patients with VTE based on a multitude of clinical features
collected over time
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data
The ability to perform accurate prognosis of patients is crucial for
proactive clinical decision making, informed resource management and
personalised care. Existing outcome prediction models suffer from a low recall
of infrequent positive outcomes. We present a highly-scalable and robust
machine learning framework to automatically predict adversity represented by
mortality and ICU admission from time-series vital signs and laboratory results
obtained within the first 24 hours of hospital admission. The stacked platform
comprises two components: a) an unsupervised LSTM Autoencoder that learns an
optimal representation of the time-series, using it to differentiate the less
frequent patterns which conclude with an adverse event from the majority
patterns that do not, and b) a gradient boosting model, which relies on the
constructed representation to refine prediction, incorporating static features
of demographics, admission details and clinical summaries. The model is used to
assess a patient's risk of adversity over time and provides visual
justifications of its prediction based on the patient's static features and
dynamic signals. Results of three case studies for predicting mortality and ICU
admission show that the model outperforms all existing outcome prediction
models, achieving PR-AUC of 0.891 (95 CI: 0.878 - 0.969) in predicting
mortality in ICU and general ward settings and 0.908 (95 CI: 0.870-0.935) in
predicting ICU admission.Comment: 14 page
Machine Learning Framework for Real-World Electronic Health Records Regarding Missingness, Interpretability, and Fairness
Machine learning (ML) and deep learning (DL) techniques have shown promising results in healthcare applications using Electronic Health Records (EHRs) data. However, their adoption in real-world healthcare settings is hindered by three major challenges. Firstly, real-world EHR data typically contains numerous missing values. Secondly, traditional ML/DL models are typically considered black-boxes, whereas interpretability is required for real-world healthcare applications. Finally, differences in data distributions may lead to unfairness and performance disparities, particularly in subpopulations.
This dissertation proposes methods to address missing data, interpretability, and fairness issues. The first work proposes an ensemble prediction framework for EHR data with large missing rates using multiple subsets with lower missing rates. The second method introduces the integration of medical knowledge graphs and double attention mechanism with the long short-term memory (LSTM) model to enhance interpretability by providing knowledge-based model interpretation. The third method develops an LSTM variant that integrates medical knowledge graphs and additional time-aware gates to handle multi-variable temporal missing issues and interpretability concerns. Finally, a transformer-based model is proposed to learn unbiased and fair representations of diverse subpopulations using domain classifiers and three attention mechanisms
Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults
As global demographics change, ageing is a global phenomenon which is increasingly of interest in our modern and rapidly changing society. Thus, the application of proper prognostic indices in clinical decisions regarding mortality prediction has assumed a significant importance for personalized risk management (i.e., identifying patients who are at high or low risk of death) and to help ensure effective healthcare services to patients. Consequently, prognostic modelling expressed as allâcause mortality prediction is an important step for effective patient management. Machine learning has the potential to transform prognostic modelling. In this paper, results on the development of machine learning models for allâcause mortality prediction in a cohort of healthy older adults are reported. The models are based on features covering anthropometric variables, physical and lab examinations, questionnaires, and lifestyles, as well as wearable data collected in freeâliving settings, obtained for the âHealthy Ageing Initiativeâ study conducted on 2291 recruited participants. Several machine learning techniques including feature engineering, feature selection, data augmentation and resampling were investigated for this purpose. A detailed empirical comparison of the impact of the different techniques is presented and discussed. The achieved performances were also compared with a standard epidemiological model. This investigation showed that, for the dataset under consideration, the best results were achieved with Random Underâ Sampling in conjunction with Random Forest (either with or without probability calibration). However, while including probability calibration slightly reduced the average performance, it increased the model robustness, as indicated by the lower 95% confidence intervals. The analysis showed that machine learning models could provide comparable results to standard epidemiological models while being completely dataâdriven and diseaseâagnostic, thus demonstrating the opportunity for building machine learning models on health records data for research and clinical practice. However, further testing is required to significantly improve the model performance and its robustness
- âŠ