19 research outputs found
Temporal-spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit
In recent years, medical information technology has made it possible for
electronic health record (EHR) to store fairly complete clinical data. This has
brought health care into the era of "big data". However, medical data are often
sparse and strongly correlated, which means that medical problems cannot be
solved effectively. With the rapid development of deep learning in recent
years, it has provided opportunities for the use of big data in healthcare. In
this paper, we propose a temporal-saptial correlation attention network (TSCAN)
to handle some clinical characteristic prediction problems, such as predicting
death, predicting length of stay, detecting physiologic decline, and
classifying phenotypes. Based on the design of the attention mechanism model,
our approach can effectively remove irrelevant items in clinical data and
irrelevant nodes in time according to different tasks, so as to obtain more
accurate prediction results. Our method can also find key clinical indicators
of important outcomes that can be used to improve treatment options. Our
experiments use information from the Medical Information Mart for Intensive
Care (MIMIC-IV) database, which is open to the public. Finally, we have
achieved significant performance benefits of 2.0\% (metric) compared to other
SOTA prediction methods. We achieved a staggering 90.7\% on mortality rate,
45.1\% on length of stay. The source code can be find:
\url{https://github.com/yuyuheintju/TSCAN}
Expanding Access to Justice: Alternatives to Full Representation in New York State
https://digitalcommons.nyls.edu/impact_center/1009/thumbnail.jp
An Optimal Policy for Patient Laboratory Tests in Intensive Care Units
Laboratory testing is an integral tool in the management of patient care in
hospitals, particularly in intensive care units (ICUs). There exists an
inherent trade-off in the selection and timing of lab tests between
considerations of the expected utility in clinical decision-making of a given
test at a specific time, and the associated cost or risk it poses to the
patient. In this work, we introduce a framework that learns policies for
ordering lab tests which optimizes for this trade-off. Our approach uses batch
off-policy reinforcement learning with a composite reward function based on
clinical imperatives, applied to data that include examples of clinicians
ordering labs for patients. To this end, we develop and extend principles of
Pareto optimality to improve the selection of actions based on multiple reward
function components while respecting typical procedural considerations and
prioritization of clinical goals in the ICU. Our experiments show that we can
estimate a policy that reduces the frequency of lab tests and optimizes timing
to minimize information redundancy. We also find that the estimated policies
typically suggest ordering lab tests well ahead of critical onsets--such as
mechanical ventilation or dialysis--that depend on the lab results. We evaluate
our approach by quantifying how these policies may initiate earlier onset of
treatment.Comment: The first two authors contributed equally to this work. Preprint of
an article submitted for consideration in Pacific Symposium on Biocomputing
copyright 2018 [copyright World Scientific Publishing Company]
[https://psb.stanford.edu/
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III
Robust machine learning relies on access to data that can be used with
standardized frameworks in important tasks and the ability to develop models
whose performance can be reasonably reproduced. In machine learning for
healthcare, the community faces reproducibility challenges due to a lack of
publicly accessible data and a lack of standardized data processing frameworks.
We present MIMIC-Extract, an open-source pipeline for transforming raw
electronic health record (EHR) data for critical care patients contained in the
publicly-available MIMIC-III database into dataframes that are directly usable
in common machine learning pipelines. MIMIC-Extract addresses three primary
challenges in making complex health records data accessible to the broader
machine learning community. First, it provides standardized data processing
functions, including unit conversion, outlier detection, and aggregating
semantically equivalent features, thus accounting for duplication and reducing
missingness. Second, it preserves the time series nature of clinical data and
can be easily integrated into clinically actionable prediction tasks in machine
learning for health. Finally, it is highly extensible so that other researchers
with related questions can easily use the same pipeline. We demonstrate the
utility of this pipeline by showcasing several benchmark tasks and baseline
results