34 research outputs found
IHE cross-enterprise document sharing for imaging: interoperability testing software
<p>Abstract</p> <p>Background</p> <p>With the deployments of Electronic Health Records (EHR), interoperability testing in healthcare is becoming crucial. EHR enables access to prior diagnostic information in order to assist in health decisions. It is a virtual system that results from the cooperation of several heterogeneous distributed systems. Interoperability between peers is therefore essential. Achieving interoperability requires various types of testing. Implementations need to be tested using software that simulates communication partners, and that provides test data and test plans.</p> <p>Results</p> <p>In this paper we describe a software that is used to test systems that are involved in sharing medical images within the EHR. Our software is used as part of the Integrating the Healthcare Enterprise (IHE) testing process to test the Cross Enterprise Document Sharing for imaging (XDS-I) integration profile. We describe its architecture and functionalities; we also expose the challenges encountered and discuss the elected design solutions.</p> <p>Conclusions</p> <p>EHR is being deployed in several countries. The EHR infrastructure will be continuously evolving to embrace advances in the information technology domain. Our software is built on a web framework to allow for an easy evolution with web technology. The testing software is publicly available; it can be used by system implementers to test their implementations. It can also be used by site integrators to verify and test the interoperability of systems, or by developers to understand specifications ambiguities, or to resolve implementations difficulties.</p
Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation
The rapid progress in clinical data management systems and artificial
intelligence approaches enable the era of personalized medicine. Intensive care
units (ICUs) are the ideal clinical research environment for such development
because they collect many clinical data and are highly computerized
environments. We designed a retrospective clinical study on a prospective ICU
database using clinical natural language to help in the early diagnosis of
heart failure in critically ill children. The methodology consisted of
empirical experiments of a learning algorithm to learn the hidden
interpretation and presentation of the French clinical note data. This study
included 1386 patients' clinical notes with 5444 single lines of notes. There
were 1941 positive cases (36 % of total) and 3503 negative cases classified by
two independent physicians using a standardized approach. The multilayer
perceptron neural network outperforms other discriminative and generative
classifiers. Consequently, the proposed framework yields an overall
classification performance with 89 % accuracy, 88 % recall, and 89 % precision.
Furthermore, a generative autoencoder learning algorithm was proposed to
leverage the sparsity reduction that achieved 91% accuracy, 91% recall, and 91%
precision. This study successfully applied learning representation and machine
learning algorithms to detect heart failure from clinical natural language in a
single French institution. Further work is needed to use the same methodology
in other institutions and other languages.Comment: Submitting to IEEE Transactions on Biomedical Engineering. arXiv
admin note: text overlap with arXiv:2104.0393
Label Propagation Techniques for Artifact Detection in Imbalanced Classes using Photoplethysmogram Signals
Photoplethysmogram (PPG) signals are widely used in healthcare for monitoring
vital signs, but they are susceptible to motion artifacts that can lead to
inaccurate interpretations. In this study, the use of label propagation
techniques to propagate labels among PPG samples is explored, particularly in
imbalanced class scenarios where clean PPG samples are significantly
outnumbered by artifact-contaminated samples. With a precision of 91%, a recall
of 90% and an F1 score of 90% for the class without artifacts, the results
demonstrate its effectiveness in labeling a medical dataset, even when clean
samples are rare. For the classification of artifacts our study compares
supervised classifiers such as conventional classifiers and neural networks
(MLP, Transformers, FCN) with the semi-supervised label propagation algorithm.
With a precision of 89%, a recall of 95% and an F1 score of 92%, the KNN
supervised model gives good results, but the semi-supervised algorithm performs
better in detecting artifacts. The findings suggest that the semi-supervised
algorithm label propagation hold promise for artifact detection in PPG signals,
which can enhance the reliability of PPG-based health monitoring systems in
real-world applications.Comment: Under preparation to submit to IEEE for possible publication
Data representation structure to support clinical decision-making in the pediatric intensive care unit: Interview study and preliminary decision support interface design
ABSTRACT: Background: Clinical decision-making is a complex cognitive process that relies on the interpretation of a large variety of data from different sources and involves the use of knowledge bases and scientific recommendations. The representation of clinical data plays a key role in the speed and efficiency of its interpretation. In addition, the increasing use of clinical decision support systems (CDSSs) provides assistance to clinicians in their practice, allowing them to improve patient outcomes. In the pediatric intensive care unit (PICU), clinicians must process high volumes of data and deal with ever-growing workloads. As they use multiple systems daily to assess patientsâ status and to adjust the health care plan, including electronic health records (EHR), clinical systems (eg, laboratory, imaging and pharmacy), and connected devices (eg, bedside monitors, mechanical ventilators, intravenous pumps, and syringes), clinicians rely mostly on their judgment and ability to trace relevant data for decision-making. In these circumstances, the lack of optimal data structure and adapted visual representation hinder clinicianâs cognitive processes and clinical decision-making skills. Objective: In this study, we designed a prototype to optimize the representation of clinical data collected from existing sources (eg, EHR, clinical systems, and devices) via a structure that supports the integration of a home-developed CDSS in the PICU. This study was based on analyzing end user needs and their clinical workflow. Methods: First, we observed clinical activities in a PICU to secure a better understanding of the workflow in terms of staff tasks and their use of EHR on a typical work shift. Second, we conducted interviews with 11 clinicians from different staff categories (eg, intensivists, fellows, nurses, and nurse practitioners) to compile their needs for decision support. Third, we structured the data to design a prototype that illustrates the proposed representation. We used a brain injury care scenario to validate the relevance of integrated data and the utility of main functionalities in a clinical context. Fourth, we held design meetings with 5 clinicians to present, revise, and adapt the prototype to meet their needs. Results: We created a structure with 3 levels of abstractionâunit level, patient level, and system levelâto optimize clinical data representation and display for efficient patient assessment and to provide a flexible platform to host the internally developed CDSS. Subsequently, we designed a preliminary prototype based on this structure. Conclusions: The data representation structure allows prioritizing patients via criticality indicators, assessing their conditions using a personalized dashboard, and monitoring their courses based on the evolution of clinical values. Further research is required to define and model the concepts of criticality, problem recognition, and evolution. Furthermore, feasibility tests will be conducted to ensure user satisfaction
Label Propagation Techniques for Artifact Detection in Imbalanced Classes Using Photoplethysmogram Signals
peer reviewedThis study aimed to investigate the application of label propagation techniques to propagate labels among photoplethysmogram (PPG) signals, particularly in imbalanced class scenarios and limited data availability scenarios, where clean PPG samples are significantly outnumbered by artifact-contaminated samples. We investigated a dataset comprising PPG recordings from 1571 patients, wherein approximately 82% of the samples were identified as clean, while the remaining 18% were contaminated by artifacts. Our research compares the performance of supervised classifiers, such as conventional classifiers and neural networks (Multi-Layer Perceptron (MLP), Transformers, Fully Convolutional Network (FCN)), with the semi-supervised Label Propagation (LP) algorithm for artifact classification in PPG signals. The results indicate that the LP algorithm achieves a precision of 91%, a recall of 90%, and an F1 score of 90% for the 'artifacts' class, showcasing its effectiveness in annotating a medical dataset, even in cases where clean samples are rare. Although the K-Nearest Neighbors (KNN) supervised model demonstrated good results with a precision of 89%, a recall of 95%, and an F1 score of 92%, the semi-supervised algorithm excels in artifact detection. In the case of imbalanced and limited pediatric intensive care environment data, the semi-supervised LP algorithm is promising for artifact detection in PPG signals. The results of this study are important for improving the accuracy of PPG-based health monitoring, particularly in situations in which motion artifacts pose challenges to data interpretation