29,821 research outputs found
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
Estimating what would be an individual's potential response to varying levels
of exposure to a treatment is of high practical relevance for several important
fields, such as healthcare, economics and public policy. However, existing
methods for learning to estimate counterfactual outcomes from observational
data are either focused on estimating average dose-response curves, or limited
to settings with only two treatments that do not have an associated dosage
parameter. Here, we present a novel machine-learning approach towards learning
counterfactual representations for estimating individual dose-response curves
for any number of treatments with continuous dosage parameters with neural
networks. Building on the established potential outcomes framework, we
introduce performance metrics, model selection criteria, model architectures,
and open benchmarks for estimating individual dose-response curves. Our
experiments show that the methods developed in this work set a new
state-of-the-art in estimating individual dose-response
Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission
Background and objective: Critically ill children may suffer from impaired neurocognitive functions years after ICU (intensive care unit) discharge. To assess neurocognitive functions, these children are subjected to a fixed sequence of tests. Undergoing all tests is, however, arduous for former pediatric ICU patients, resulting in interrupted evaluations where several neurocognitive deficiencies remain undetected. As a solution, we propose using machine learning to predict the optimal order of tests for each child, reducing the number of tests required to identify the most severe neurocognitive deficiencies. Methods: We have compared the current clinical approach against several machine learning methods, mainly multi-target regression and label ranking methods. We have also proposed a new method that builds several multi-target predictive models and combines the outputs into a ranking that prioritizes the worse neurocognitive outcomes. We used data available at discharge, from children who participated in the PEPaNIC-RCT trial (ClinicalTrials.gov-NCT01536275), as well as data from a 2-year follow-up study. The institutional review boards at each participating site have also approved this follow-up study (ML8052; NL49708.078; Pro00038098). Results: Our proposed method managed to outperform other machine learning methods and also the current clinical practice. Precisely, our method reaches approximately 80% precision when considering top-4 outcomes, in comparison to 65% and 78% obtained by the current clinical practice and the state-of-the-art method in label ranking, respectively. Conclusions: Our experiments demonstrated that machine learning can be competitive or even superior to the current testing order employed in clinical practice, suggesting that our model can be used to severely reduce the number of tests necessary for each child. Moreover, the results indicate that possible long-term adverse outcomes are already predictable as early as at ICU discharge. Thus, our work can be seen as the first step to allow more personalized follow-up after ICU discharge leading to preventive care rather than curative.</p
Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission
Background and objective: Critically ill children may suffer from impaired neurocognitive functions years after ICU (intensive care unit) discharge. To assess neurocognitive functions, these children are subjected to a fixed sequence of tests. Undergoing all tests is, however, arduous for former pediatric ICU patients, resulting in interrupted evaluations where several neurocognitive deficiencies remain undetected. As a solution, we propose using machine learning to predict the optimal order of tests for each child, reducing the number of tests required to identify the most severe neurocognitive deficiencies. Methods: We have compared the current clinical approach against several machine learning methods, mainly multi-target regression and label ranking methods. We have also proposed a new method that builds several multi-target predictive models and combines the outputs into a ranking that prioritizes the worse neurocognitive outcomes. We used data available at discharge, from children who participated in the PEPaNIC-RCT trial (ClinicalTrials.gov-NCT01536275), as well as data from a 2-year follow-up study. The institutional review boards at each participating site have also approved this follow-up study (ML8052; NL49708.078; Pro00038098). Results: Our proposed method managed to outperform other machine learning methods and also the current clinical practice. Precisely, our method reaches approximately 80% precision when considering top-4 outcomes, in comparison to 65% and 78% obtained by the current clinical practice and the state-of-the-art method in label ranking, respectively. Conclusions: Our experiments demonstrated that machine learning can be competitive or even superior to the current testing order employed in clinical practice, suggesting that our model can be used to severely reduce the number of tests necessary for each child. Moreover, the results indicate that possible long-term adverse outcomes are already predictable as early as at ICU discharge. Thus, our work can be seen as the first step to allow more personalized follow-up after ICU discharge leading to preventive care rather than curative.</p
Semi-supervised Optimal Transport with Self-paced Ensemble for Cross-hospital Sepsis Early Detection
The utilization of computer technology to solve problems in medical scenarios
has attracted considerable attention in recent years, which still has great
potential and space for exploration. Among them, machine learning has been
widely used in the prediction, diagnosis and even treatment of Sepsis. However,
state-of-the-art methods require large amounts of labeled medical data for
supervised learning. In real-world applications, the lack of labeled data will
cause enormous obstacles if one hospital wants to deploy a new Sepsis detection
system. Different from the supervised learning setting, we need to use known
information (e.g., from another hospital with rich labeled data) to help build
a model with acceptable performance, i.e., transfer learning. In this paper, we
propose a semi-supervised optimal transport with self-paced ensemble framework
for Sepsis early detection, called SPSSOT, to transfer knowledge from the other
that has rich labeled data. In SPSSOT, we first extract the same clinical
indicators from the source domain (e.g., hospital with rich labeled data) and
the target domain (e.g., hospital with little labeled data), then we combine
the semi-supervised domain adaptation based on optimal transport theory with
self-paced under-sampling to avoid a negative transfer possibly caused by
covariate shift and class imbalance. On the whole, SPSSOT is an end-to-end
transfer learning method for Sepsis early detection which can automatically
select suitable samples from two domains respectively according to the number
of iterations and align feature space of two domains. Extensive experiments on
two open clinical datasets demonstrate that comparing with other methods, our
proposed SPSSOT, can significantly improve the AUC values with only 1% labeled
data in the target domain in two transfer learning scenarios, MIMIC
Challenge and Challenge MIMIC.Comment: 14 pages, 9 figure
Neural networks for medical condition prediction : an investigation of neonatal respiratory disorder
Neonatal EEG graded for severity of background abnormalities in hypoxic-ischaemic encephalopathy
This report describes a set of neonatal electroencephalogram (EEG) recordings
graded according to the severity of abnormalities in the background pattern.
The dataset consists of 169 hours of multichannel EEG from 53 neonates recorded
in a neonatal intensive care unit. All neonates received a diagnosis of
hypoxic-ischaemic encephalopathy (HIE), the most common cause of brain injury
in full term infants. For each neonate, multiple 1-hour epochs of good quality
EEG were selected and then graded for background abnormalities. The grading
system assesses EEG attributes such as amplitude and frequency, continuity,
sleep--wake cycling, symmetry and synchrony, and abnormal waveforms. Background
severity was then categorised into 4 grades: normal or mildly abnormal EEG,
moderately abnormal EEG, severely abnormal EEG, and inactive EEG. The data can
be used as a reference set of multi-channel EEG for neonates with HIE, for EEG
training purposes, or for developing and evaluating automated grading
algorithms
- …