Development of an openEHR Template for COVID-19 Based on Clinical Guidelines

Li, Mengyang; Leslie, H.A.; Qi, Bin; Nan, Shan; Feng, Hongshuo; Cai, Hailing; Lü, Xudong; Duan, Huilong

doi:10.2196/20239

Cited by 27 publications

(19 citation statements)

References 5 publications

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“…There have been significant recent advances in modeling clinical data on electronic health records (EHRs) [ 17 , 18 ] and specifically in the capability of machine learning techniques to predict mortality [ 19 - 21 ]. In view of this, our study proposes an artificial neural network (ANN) that analyzes a set of patient features including demographics, comorbidities, lifestyle factors, and presenting symptoms and predicts patient-specific mortality risk during the current hospital admission.…”

Section: Introductionmentioning

confidence: 99%

Prognostic Modeling of COVID-19 Using Artificial Intelligence in the United Kingdom: Model Development and Validation

Abdulaal¹,

Patel²,

Charani³

et al. 2020

J Med Internet Res

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Background The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak is a public health emergency and the case fatality rate in the United Kingdom is significant. Although there appear to be several early predictors of outcome, there are no currently validated prognostic models or scoring systems applicable specifically to patients with confirmed SARS-CoV-2. Objective We aim to create a point-of-admission mortality risk scoring system using an artificial neural network (ANN). Methods We present an ANN that can provide a patient-specific, point-of-admission mortality risk prediction to inform clinical management decisions at the earliest opportunity. The ANN analyzes a set of patient features including demographics, comorbidities, smoking history, and presenting symptoms and predicts patient-specific mortality risk during the current hospital admission. The model was trained and validated on data extracted from 398 patients admitted to hospital with a positive real-time reverse transcription polymerase chain reaction (RT-PCR) test for SARS-CoV-2. Results Patient-specific mortality was predicted with 86.25% accuracy, with a sensitivity of 87.50% (95% CI 61.65%-98.45%) and specificity of 85.94% (95% CI 74.98%-93.36%). The positive predictive value was 60.87% (95% CI 45.23%-74.56%), and the negative predictive value was 96.49% (95% CI 88.23%-99.02%). The area under the receiver operating characteristic curve was 90.12%. Conclusions This analysis demonstrates an adaptive ANN trained on data at a single site, which demonstrates the early utility of deep learning approaches in a rapidly evolving pandemic with no established or validated prognostic scoring systems.

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Section: Introductionmentioning

confidence: 99%

Prognostic Modeling of COVID-19 Using Artificial Intelligence in the United Kingdom: Model Development and Validation

Abdulaal¹,

Patel²,

Charani³

et al. 2020

J Med Internet Res

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“…An openEHR template is developed to organize these archetypes. The detailed approach of openEHR template modeling is described elsewhere [ 32 ]. In the clinical algorithm editing step, another group of informaticists translates the relationship table into GDL rules.…”

Section: Methodsmentioning

confidence: 99%

“…A group of archetypes in the format of archetype definition language (ADL) files were exported from the CKM. The detailed archetype searching and template development process is reported elsewhere [ 32 ]. Ten days were required for the domain concept modeling because the selection of proper archetypes required confirmation by the external openEHR expert.…”

Section: Methodsmentioning

confidence: 99%

A Computer-Interpretable Guideline for COVID-19: Rapid Development and Dissemination

Nan¹,

Tang²,

Feng³

et al. 2020

JMIR Med Inform

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Background COVID-19 is a global pandemic that is affecting more than 200 countries worldwide. Efficient diagnosis and treatment are crucial to combat the disease. Computer-interpretable guidelines (CIGs) can aid the broad global adoption of evidence-based diagnosis and treatment knowledge. However, currently, no internationally shareable CIG exists. Objective The aim of this study was to establish a rapid CIG development and dissemination approach and apply it to develop a shareable CIG for COVID-19. Methods A 6-step rapid CIG development and dissemination approach was designed and applied. Processes, roles, and deliverable artifacts were specified in this approach to eliminate ambiguities during development of the CIG. The Guideline Definition Language (GDL) was used to capture the clinical rules. A CIG for COVID-19 was developed by translating, interpreting, annotating, extracting, and formalizing the Chinese COVID-19 diagnosis and treatment guideline. A prototype application was implemented to validate the CIG. Results We used 27 archetypes for the COVID-19 guideline. We developed 18 GDL rules to cover the diagnosis and treatment suggestion algorithms in the narrative guideline. The CIG was further translated to object data model and Drools rules to facilitate its use by people who do not employ the non-openEHR archetype. The prototype application validated the correctness of the CIG with a public data set. Both the GDL rules and Drools rules have been disseminated on GitHub. Conclusions Our rapid CIG development and dissemination approach accelerated the pace of COVID-19 CIG development. A validated COVID-19 CIG is now available to the public.

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“…In the era of the Internet of Things (IoT), a software based telehealth system is not a new concept. In certain instances, video cameras or other acquisition panels are required as additional equipment in telehealth software [ 39 , 40 ]. During the COVID-19 pandemic, such type of telehealth system can play a significant role in faster treatment [ 41 ].…”

Section: Telehealth Systemsmentioning

confidence: 99%