5 research outputs found
Modeling Rare Interactions in Time Series Data Through Qualitative Change: Application to Outcome Prediction in Intensive Care Units
Many areas of research are characterised by the deluge of large-scale
highly-dimensional time-series data. However, using the data available for
prediction and decision making is hampered by the current lag in our ability to
uncover and quantify true interactions that explain the outcomes.We are
interested in areas such as intensive care medicine, which are characterised by
i) continuous monitoring of multivariate variables and non-uniform sampling of
data streams, ii) the outcomes are generally governed by interactions between a
small set of rare events, iii) these interactions are not necessarily definable
by specific values (or value ranges) of a given group of variables, but rather,
by the deviations of these values from the normal state recorded over time, iv)
the need to explain the predictions made by the model. Here, while numerous
data mining models have been formulated for outcome prediction, they are unable
to explain their predictions.
We present a model for uncovering interactions with the highest likelihood of
generating the outcomes seen from highly-dimensional time series data.
Interactions among variables are represented by a relational graph structure,
which relies on qualitative abstractions to overcome non-uniform sampling and
to capture the semantics of the interactions corresponding to the changes and
deviations from normality of variables of interest over time. Using the
assumption that similar templates of small interactions are responsible for the
outcomes (as prevalent in the medical domains), we reformulate the discovery
task to retrieve the most-likely templates from the data.Comment: 8 pages, 3 figures. Accepted for publication in the European
Conference of Artificial Intelligence (ECAI 2020
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