428 research outputs found
Vital signs prediction and early warning score calculation based on continuous monitoring of hospitalised patients using wearable technology
In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients’ vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration
Towards Better Long-range Time Series Forecasting using Generative Forecasting
Long-range time series forecasting is usually based on one of two existing
forecasting strategies: Direct Forecasting and Iterative Forecasting, where the
former provides low bias, high variance forecasts and the latter leads to low
variance, high bias forecasts. In this paper, we propose a new forecasting
strategy called Generative Forecasting (GenF), which generates synthetic data
for the next few time steps and then makes long-range forecasts based on
generated and observed data. We theoretically prove that GenF is able to better
balance the forecasting variance and bias, leading to a much smaller
forecasting error. We implement GenF via three components: (i) a novel
conditional Wasserstein Generative Adversarial Network (GAN) based generator
for synthetic time series data generation, called CWGAN-TS. (ii) a transformer
based predictor, which makes long-range predictions using both generated and
observed data. (iii) an information theoretic clustering algorithm to improve
the training of both the CWGAN-TS and the transformer based predictor. The
experimental results on five public datasets demonstrate that GenF
significantly outperforms a diverse range of state-of-the-art benchmarks and
classical approaches. Specifically, we find a 5% - 11% improvement in
predictive performance (mean absolute error) while having a 15% - 50% reduction
in parameters compared to the benchmarks. Lastly, we conduct an ablation study
to further explore and demonstrate the effectiveness of the components
comprising GenF.Comment: 14 pages. arXiv admin note: substantial text overlap with
arXiv:2110.0877
A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources
Electronic Health Records (EHRs) are a valuable asset to facilitate clinical
research and point of care applications; however, many challenges such as data
privacy concerns impede its optimal utilization. Deep generative models,
particularly, Generative Adversarial Networks (GANs) show great promise in
generating synthetic EHR data by learning underlying data distributions while
achieving excellent performance and addressing these challenges. This work aims
to review the major developments in various applications of GANs for EHRs and
provides an overview of the proposed methodologies. For this purpose, we
combine perspectives from healthcare applications and machine learning
techniques in terms of source datasets and the fidelity and privacy evaluation
of the generated synthetic datasets. We also compile a list of the metrics and
datasets used by the reviewed works, which can be utilized as benchmarks for
future research in the field. We conclude by discussing challenges in GANs for
EHRs development and proposing recommended practices. We hope that this work
motivates novel research development directions in the intersection of
healthcare and machine learning
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
A survey of generative adversarial networks for synthesizing structured electronic health records
Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and point of care applications; however, many challenges such as data privacy concerns impede its optimal utilization. Deep generative models, particularly, Generative Adversarial Networks (GANs) show great promise in generating synthetic EHR data by learning underlying data distributions while achieving excellent performance and addressing these challenges. This work aims to survey the major developments in various applications of GANs for EHRs and provides an overview of the proposed methodologies. For this purpose, we combine perspectives from healthcare applications and machine learning techniques in terms of source datasets and the fidelity and privacy evaluation of the generated synthetic datasets. We also compile a list of the metrics and datasets used by the reviewed works, which can be utilized as benchmarks for future research in the field. We conclude by discussing challenges in GANs for EHRs development and proposing recommended practices. We hope that this work motivates novel research development directions in the intersection of healthcare and machine learning
Development of Artificial Intelligence Algorithms for Early Diagnosis of Sepsis
Sepsis is a prevalent syndrome that manifests itself through an uncontrolled response
from the body to an infection, that may lead to organ dysfunction. Its diagnosis is urgent
since early treatment can reduce the patients’ chances of having long-term consequences.
Yet, there are many obstacles to achieving this early detection. Some stem from the
syndrome’s pathogenesis, which lacks a characteristic biomarker. The available clinical
detection tools are either too complex or lack sensitivity, in both cases delaying the diagnosis.
Another obstacle relates to modern technology, that when paired with the many
clinical parameters that are monitored to detect sepsis, result in extremely heterogenous
and complex medical records, which constitute a big obstacle for the responsible clinicians,
that are forced to analyse them to diagnose the syndrome.
To help achieve this early diagnosis, as well as understand which parameters are most
relevant to obtain it, an approach based on the use of Artificial Intelligence algorithms is
proposed in this work, with the model being implemented in the alert system of a sepsis
monitoring platform.
This platform uses a Random Forest algorithm, based on supervised machine learning
classification, that is capable of detecting the syndrome in two different scenarios. The
earliest detection can happen if there are only five vital sign parameters available for
measurement, namely heart rate, systolic and diastolic blood pressures, blood oxygen
saturation level, and body temperature, in which case, the model has a score of 83%
precision and 62% sensitivity. If besides the mentioned variables, laboratory analysis
measurements of bilirubin, creatinine, hemoglobin, leukocytes, platelet count, and Creactive
protein levels are available, the platform’s sensitivity increases to 77%. With this,
it has also been found that the blood oxygen saturation level is one of the most important
variables to take into account for the task, in both cases. Once the platform is tested
in real clinical situations, together with an increase in the available clinical data, it is
believed that the platform’s performance will be even better.A sépsis é uma síndrome com elevada incidência a nível global, que se manifesta através
de uma resposta desregulada por parte do organismo a uma infeção, podendo resultar
em disfunções orgânicas generalizadas. O diagnóstico da mesma é urgente, uma vez que
um tratamento precoce pode reduzir as hipóteses de consequências a longo prazo para
os doentes. Apesar desta necessidade, existem vários obstáculos. Alguns deles advêm
da patogenia da síndrome, que carece de um biomarcador específico. As ferramentas
de deteção clínica são demasiado complexas, ou pouco sensíveis, em ambos os casos
atrasando o diagnóstico. Outro obstáculo relaciona-se com os avanços da tecnologia, que,
com os vários parâmetros clínicos que são monitorizados, resulta em registos médicos
heterogéneos e complexos, o que constitui um grande obstáculo para os profissionais de
saúde, que se vêm forçados a analisá-los para diagnosticar a síndrome.
Para atingir este diagnóstico precoce, bem como compreender quais os parâmetros
mais relevantes para o alcançar, é proposta neste trabalho uma abordagem baseada num
algoritmo de Inteligência Artificial, sendo o modelo implementado no sistema de alerta
de uma plataforma de monitorização de sépsis.
Esta plataforma utiliza um classificador Random Forest baseado em aprendizagem automática
supervisionada, capaz de diagnosticar a síndrome de duas formas. Uma deteção
mais precoce pode ocorrer através de cinco parâmetros vitais, nomeadamente frequência
cardíaca, pressão arterial sistólica e diastólica, nível de saturação de oxigénio no sangue
e temperatura corporal, caso em que o modelo atinge valores de 83% de precisão e 62%
de sensibilidade. Se, para além das variáveis mencionadas, estiverem disponíveis análises
laboratoriais de bilirrubina, creatinina, hemoglobina, leucócitos, contagem de plaquetas
e níveis de proteína C-reativa, a sensibilidade da plataforma sobre para 77%. Concluiu-se
que o nível de saturação de oxigénio no sangue é uma das variáveis mais importantes a ter
em conta para o diagnóstico, em ambos os casos. A partir do momento que a plataforma
venha a ser utilizada em situações clínicas reais, com o consequente aumento dos dados
disponíveis, crê-se que o desempenho venha a ser ainda melhor
- …