58 research outputs found
ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data
Sepsis is a deadly condition affecting many patients in the hospital. Recent
studies have shown that patients diagnosed with sepsis have significant
mortality and morbidity, resulting from the body's dysfunctional host response
to infection. Clinicians often rely on the use of Sequential Organ Failure
Assessment (SOFA), Systemic Inflammatory Response Syndrome (SIRS), and the
Modified Early Warning Score (MEWS) to identify early signs of clinical
deterioration requiring further work-up and treatment. However, many of these
tools are manually computed and were not designed for automated computation.
There have been different methods used for developing sepsis onset models, but
many of these models must be trained on a sufficient number of patient
observations in order to form accurate sepsis predictions. Additionally, the
accurate annotation of patients with sepsis is a major ongoing challenge. In
this paper, we propose the use of Active Learning Recurrent Neural Networks
(ALRts) for short temporal horizons to improve the prediction of irregularly
sampled temporal events such as sepsis. We show that an active learning RNN
model trained on limited data can form robust sepsis predictions comparable to
models using the entire training dataset.Comment: 11 pages, 5 figures, 2 table
Explainable artificial intelligence model to predict acute critical illness from electronic health records
We developed an explainable artificial intelligence (AI) early warning score
(xAI-EWS) system for early detection of acute critical illness. While
maintaining a high predictive performance, our system explains to the clinician
on which relevant electronic health records (EHRs) data the prediction is
grounded. Acute critical illness is often preceded by deterioration of
routinely measured clinical parameters, e.g., blood pressure and heart rate.
Early clinical prediction is typically based on manually calculated screening
metrics that simply weigh these parameters, such as Early Warning Scores (EWS).
The predictive performance of EWSs yields a tradeoff between sensitivity and
specificity that can lead to negative outcomes for the patient. Previous work
on EHR-trained AI systems offers promising results with high levels of
predictive performance in relation to the early, real-time prediction of acute
critical illness. However, without insight into the complex decisions by such
system, clinical translation is hindered. In this letter, we present our
xAI-EWS system, which potentiates clinical translation by accompanying a
prediction with information on the EHR data explaining it
Imputation and classification of time series with missing data using machine learning
This work is about classifying time series with missing data with the help of imputation and selected machine learning algorithms and methods. The author has used imputation to replace missing values in two data sets, one containing surgical site infection (SSI) data of 11 types of blood samples of patients over 20 days, and another data set called uwave which contain 3D accelerometer data of several patterns made by a subset of people, where two patterns were selected. The SSI data set is known to possess informative missingness. For the uwave data, missing data was simulated by removing data points in an informative (not random) way to simulate missing data. The DTW and Euclidean distances were computed for each imputed data set to make distance grid matrices, and used to performed classification on the data using the K Nearest Neighbour (KNN) classifier and the Support Vector Machine (SVM) classifier. Furthermore the data set features were augmented by adding masks that indicate the presence of missing data and counters of consecutive spells of missing data to help exploit informative missingness. The augmented dataset was used to classify the data using the same classifiers and distance methods mentioned earlier, in addition to a newer classifier called the Temporal Convolution Network (TCN), which used the augmented data in combination with imputation of the original data. It was found that applying Dynamic Time Warping (DTW) was unnecessary for the KNN classifier, and that Euclidean distance was sufficient. Augmenting the data was found to improve the overall results for the SVM and KNN classifier. The TCN was found to need more work due to giving unstable test results with much lower values than the validation would imply
Temporal convolution attention model for sepsis clinical assistant diagnosis prediction
Sepsis is an organ failure disease caused by an infection acquired in an intensive care unit (ICU), which leads to a high mortality rate. Developing intelligent monitoring and early warning systems for sepsis is a key research area in the field of smart healthcare. Early and accurate identification of patients at high risk of sepsis can help doctors make the best clinical decisions and reduce the mortality rate of patients with sepsis. However, the scientific understanding of sepsis remains inadequate, leading to slow progress in sepsis research. With the accumulation of electronic medical records (EMRs) in hospitals, data mining technologies that can identify patient risk patterns from the vast amount of sepsis-related EMRs and the development of smart surveillance and early warning models show promise in reducing mortality. Based on the Medical Information Mart for Intensive Care â…˘, a massive dataset of ICU EMRs published by MIT and Beth Israel Deaconess Medical Center, we propose a Temporal Convolution Attention Model for Sepsis Clinical Assistant Diagnosis Prediction (TCASP) to predict the incidence of sepsis infection in ICU patients. First, sepsis patient data is extracted from the EMRs. Then, the incidence of sepsis is predicted based on various physiological features of sepsis patients in the ICU. Finally, the TCASP model is utilized to predict the time of the first sepsis infection in ICU patients. The experiments show that the proposed model achieves an area under the receiver operating characteristic curve (AUROC) score of 86.9% (an improvement of 6.4% ) and an area under the precision-recall curve (AUPRC) score of 63.9% (an improvement of 3.9% ) compared to five state-of-the-art models
Intelligent Biosignal Processing in Wearable and Implantable Sensors
This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine
NPRL: Nightly Profile Representation Learning for Early Sepsis Onset Prediction in ICU Trauma Patients
Sepsis is a syndrome that develops in response to the presence of infection.
It is characterized by severe organ dysfunction and is one of the leading
causes of mortality in Intensive Care Units (ICUs) worldwide. These
complications can be reduced through early application of antibiotics, hence
the ability to anticipate the onset of sepsis early is crucial to the survival
and well-being of patients. Current machine learning algorithms deployed inside
medical infrastructures have demonstrated poor performance and are insufficient
for anticipating sepsis onset early. In recent years, deep learning
methodologies have been proposed to predict sepsis, but some fail to capture
the time of onset (e.g., classifying patients' entire visits as developing
sepsis or not) and others are unrealistic to be deployed into medical
facilities (e.g., creating training instances using a fixed time to onset where
the time of onset needs to be known apriori). Therefore, in this paper, we
first propose a novel but realistic prediction framework that predicts each
morning whether sepsis onset will occur within the next 24 hours with the help
of most recent data collected at night, when patient-provider ratios are higher
due to cross-coverage resulting in limited observation to each patient.
However, as we increase the prediction rate into daily, the number of negative
instances will increase while that of positive ones remain the same.
Thereafter, we have a severe class imbalance problem, making a machine learning
model hard to capture rare sepsis cases. To address this problem, we propose to
do nightly profile representation learning (NPRL) for each patient. We prove
that NPRL can theoretically alleviate the rare event problem. Our empirical
study using data from a level-1 trauma center further demonstrates the
effectiveness of our proposal
Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML
Medical applications of machine learning (ML) have experienced a surge in
popularity in recent years. The intensive care unit (ICU) is a natural habitat
for ML given the abundance of available data from electronic health records.
Models have been proposed to address numerous ICU prediction tasks like the
early detection of complications. While authors frequently report
state-of-the-art performance, it is challenging to verify claims of
superiority. Datasets and code are not always published, and cohort
definitions, preprocessing pipelines, and training setups are difficult to
reproduce. This work introduces Yet Another ICU Benchmark (YAIB), a modular
framework that allows researchers to define reproducible and comparable
clinical ML experiments; we offer an end-to-end solution from cohort definition
to model evaluation. The framework natively supports most open-access ICU
datasets (MIMIC III/IV, eICU, HiRID, AUMCdb) and is easily adaptable to future
ICU datasets. Combined with a transparent preprocessing pipeline and extensible
training code for multiple ML and deep learning models, YAIB enables unified
model development. Our benchmark comes with five predefined established
prediction tasks (mortality, acute kidney injury, sepsis, kidney function, and
length of stay) developed in collaboration with clinicians. Adding further
tasks is straightforward by design. Using YAIB, we demonstrate that the choice
of dataset, cohort definition, and preprocessing have a major impact on the
prediction performance - often more so than model class - indicating an urgent
need for YAIB as a holistic benchmarking tool. We provide our work to the
clinical ML community to accelerate method development and enable real-world
clinical implementations. Software Repository:
https://github.com/rvandewater/YAIB.Comment: Main benchmark: https://github.com/rvandewater/YAIB, Cohort
generation: https://github.com/rvandewater/YAIB-cohorts, Models:
https://github.com/rvandewater/YAIB-model
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