25,898 research outputs found
Open Source Infrastructure for Health Care Data Integration and Machine Learning Analyses
PURPOSE We have created a cloud-based machine learning system (CLOBNET) that is an open-source, lean infrastructure for electronic health record (EHR) data integration and is capable of extract, transform, and load (ETL) processing. CLOBNET enables comprehensive analysis and visualization of structured EHR data. We demonstrate the utility of CLOBNET by predicting primary therapy outcomes of patients with high-grade serous ovarian cancer (HGSOC) on the basis of EHR data. MATERIALS AND METHODS CLOBNET is built using open-source software to make data preprocessing, analysis, and model training user friendly. The source code of CLOBNET is available in GitHub. The HGSOC data set was based on a prospective cohort of 208 patients with HGSOC who were treated at Turku University Hospital, Finland, from 2009 to 2019 for whom comprehensive clinical and EHR data were available. RESULTS We trained machine learning (ML) models using clinical data, including a herein developed dissemination score that quantifies the disease burden at the time of diagnosis, to identify patients with progressive disease (PD) or a complete response (CR) on the basis of RECIST (version 1.1). The best performance was achieved with a logistic regression model, which resulted in an area under receiver operating characteristic curve (AUROC) of 0.86, with a specificity of 73% and a sensitivity of 89%, when it classified between patients who experienced PD and CR. CONCLUSION We have developed an open-source computational infrastructure, CLOBNET, that enables effective and rapid analysis of EHR and other clinical data. Our results demonstrate that CLOBNET allows predictions to be made on the basis of EHR data to address clinically relevant questions.Peer reviewe
Health Figures: An Open Source JavaScript Library for Health Data Visualization
The way we look at data has a great impact on how we can understand it,
particularly when the data is related to health and wellness. Due to the
increased use of self-tracking devices and the ongoing shift towards preventive
medicine, better understanding of our health data is an important part of
improving the general welfare of the citizens. Electronic Health Records,
self-tracking devices and mobile applications provide a rich variety of data
but it often becomes difficult to understand. We implemented the hFigures
library inspired on the hGraph visualization with additional improvements. The
purpose of the library is to provide a visual representation of the evolution
of health measurements in a complete and useful manner. We researched the
usefulness and usability of the library by building an application for health
data visualization in a health coaching program. We performed a user evaluation
with Heuristic Evaluation, Controlled User Testing and Usability
Questionnaires. In the Heuristics Evaluation the average response was 6.3 out
of 7 points and the Cognitive Walkthrough done by usability experts indicated
no design or mismatch errors. In the CSUQ usability test the system obtained an
average score of 6.13 out of 7, and in the ASQ usability test the overall
satisfaction score was 6.64 out of 7. We developed hFigures, an open source
library for visualizing a complete, accurate and normalized graphical
representation of health data. The idea is based on the concept of the hGraph
but it provides additional key features, including a comparison of multiple
health measurements over time. We conducted a usability evaluation of the
library as a key component of an application for health and wellness
monitoring. The results indicate that the data visualization library was
helpful in assisting users in understanding health data and its evolution over
time.Comment: BMC Medical Informatics and Decision Making 16.1 (2016
Assessment of technology acceptance in intensive care units
The process of deploy a technology in critical services need to be very careful planned and
processed. As an example it is the Intensive Care Unit (ICU). In the ICU the patients are in
critically ill condit ions and there aren’t available time to make experiences or to develop
incomplete systems. With the objective to improve the implementation process, the same
should be accompanied in order to understand the environment and user behaviour. In this case
and with the goal to evaluate the implementation process, an assessment model was applied to
a real system called INTCare.
INTCare is a Pervasive Intelligent Decision Support System (PIDSS). It was deployed in the
ICU of Centro Hospitalar do Porto and was evaluated using the Technology Acceptance Model
3 (TAM). This assessment was made using the four constructs proposed by the TAM and a
questionnaire-based approach guided by the Delphi Methodology. The results obtained so far
show that although the users are satisfied with the offered information recognizing this
importance, they demand for a faster system. This work present the main results achieved and
suggest one way to follow when some technology is deployed in an environment like is ICU
Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey
Data quality is the key factor for the development of trustworthy AI in
healthcare. A large volume of curated datasets with controlled confounding
factors can help improve the accuracy, robustness and privacy of downstream AI
algorithms. However, access to good quality datasets is limited by the
technical difficulty of data acquisition and large-scale sharing of healthcare
data is hindered by strict ethical restrictions. Data synthesis algorithms,
which generate data with a similar distribution as real clinical data, can
serve as a potential solution to address the scarcity of good quality data
during the development of trustworthy AI. However, state-of-the-art data
synthesis algorithms, especially deep learning algorithms, focus more on
imaging data while neglecting the synthesis of non-imaging healthcare data,
including clinical measurements, medical signals and waveforms, and electronic
healthcare records (EHRs). Thus, in this paper, we will review the synthesis
algorithms, particularly for non-imaging medical data, with the aim of
providing trustworthy AI in this domain. This tutorial-styled review paper will
provide comprehensive descriptions of non-imaging medical data synthesis on
aspects including algorithms, evaluations, limitations and future research
directions.Comment: 35 pages, Submitted to ACM Computing Survey
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