363 research outputs found
Clinical Decision Support for Integrated Cyber-Physical Systems: A Mixed Methods Approach
We describe the design and implementation of a clinical decision support system for assessing risk of cerebral vasospasm in patients who have been treated for aneurysmal subarachnoid hemorrhage. We illustrate the need for such clinical decision support systems in the intensive care environment, and propose a three pronged approach to constructing them, which we believe presents a balanced approach to patient modeling. We illustrate the data collection process, choice and development of models, system architecture, and methodology for user interface design. We close with a description of future work, a proposed evaluation mechanism, and a description of the demo to be presented
Ensuring patients privacy in a cryptographic-based-electronic health records using bio-cryptography
Several recent works have proposed and implemented cryptography as a means to
preserve privacy and security of patients health data. Nevertheless, the
weakest point of electronic health record (EHR) systems that relied on these
cryptographic schemes is key management. Thus, this paper presents the
development of privacy and security system for cryptography-based-EHR by taking
advantage of the uniqueness of fingerprint and iris characteristic features to
secure cryptographic keys in a bio-cryptography framework. The results of the
system evaluation showed significant improvements in terms of time efficiency
of this approach to cryptographic-based-EHR. Both the fuzzy vault and fuzzy
commitment demonstrated false acceptance rate (FAR) of 0%, which reduces the
likelihood of imposters gaining successful access to the keys protecting
patients protected health information. This result also justifies the
feasibility of implementing fuzzy key binding scheme in real applications,
especially fuzzy vault which demonstrated a better performance during key
reconstruction
Temporal expression normalisation in natural language texts
Automatic annotation of temporal expressions is a research challenge of great
interest in the field of information extraction. In this report, I describe a
novel rule-based architecture, built on top of a pre-existing system, which is
able to normalise temporal expressions detected in English texts. Gold standard
temporally-annotated resources are limited in size and this makes research
difficult. The proposed system outperforms the state-of-the-art systems with
respect to TempEval-2 Shared Task (value attribute) and achieves substantially
better results with respect to the pre-existing system on top of which it has
been developed. I will also introduce a new free corpus consisting of 2822
unique annotated temporal expressions. Both the corpus and the system are
freely available on-line.Comment: 7 pages, 1 figure, 5 table
On the Use of Time Series Kernel and Dimensionality Reduction to Identify the Acquisition of Antimicrobial Multidrug Resistance in the Intensive Care Unit
Presentation at the 2021 KDD Workshop on Applied Data Science for Healthcare, 15.08.21 - 16.08.21. https://dshealthkdd.github.io/dshealth-2021/The acquisition of Antimicrobial Multidrug Resistance (AMR) in
patients admitted to the Intensive Care Units (ICU) is a major global
concern. This study analyses data in the form of multivariate time
series (MTS) from 3476 patients recorded at the ICU of University
Hospital of Fuenlabrada (Madrid) from 2004 to 2020. 18% of the
patients acquired AMR during their stay in the ICU. The goal of this
paper is an early prediction of the development of AMR. Towards
that end, we leverage the time-series cluster kernel (TCK) to learn
similarities between MTS. To evaluate the effectiveness of TCK as
a kernel, we applied several dimensionality reduction techniques
for visualization and classification tasks. The experimental results
show that TCK allows identifying a group of patients that acquire
the AMR during the first 48 hours of their ICU stay, and it also
provides good classification capabilities
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