Dataset Information

A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification.

ABSTRACT:

Objective

With COVID-19, there was a need for a rapidly scalable annotation system that facilitated real-time integration with clinical decision support systems (CDS). Current annotation systems suffer from a high-resource utilization and poor scalability limiting real-world integration with CDS. A potential solution to mitigate these issues is to use the rule-based gazetteer developed at our institution.

Materials and methods

Performance, resource utilization, and runtime of the rule-based gazetteer were compared with five annotation systems: BioMedICUS, cTAKES, MetaMap, CLAMP, and MedTagger.

Results

This rule-based gazetteer was the fastest, had a low resource footprint, and similar performance for weighted microaverage and macroaverage measures of precision, recall, and f1-score compared to other annotation systems.

Discussion

Opportunities to increase its performance include fine-tuning lexical rules for symptom identification. Additionally, it could run on multiple compute nodes for faster runtime.

Conclusion

This rule-based gazetteer overcame key technical limitations facilitating real-time symptomatology identification for COVID-19 and integration of unstructured data elements into our CDS. It is ideal for large-scale deployment across a wide variety of healthcare settings for surveillance of acute COVID-19 symptoms for integration into prognostic modeling. Such a system is currently being leveraged for monitoring of postacute sequelae of COVID-19 (PASC) progression in COVID-19 survivors. This study conducted the first in-depth analysis and developed a rule-based gazetteer for COVID-19 symptom extraction with the following key features: low processor and memory utilization, faster runtime, and similar weighted microaverage and macroaverage measures for precision, recall, and f1-score compared to industry-standard annotation systems.

SUBMITTER: Sahoo HS

PROVIDER: S-EPMC8374371 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Publications

A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification.

Sahoo Himanshu S HS Silverman Greg M GM Ingraham Nicholas E NE Lupei Monica I MI Puskarich Michael A MA Finzel Raymond L RL Sartori John J Zhang Rui R Knoll Benjamin C BC Liu Sijia S Liu Hongfang H Melton Genevieve B GB Tignanelli Christopher J CJ Pakhomov Serguei V S SVS

JAMIA open 20210701 3

<h4>Objective</h4>With COVID-19, there was a need for a rapidly scalable annotation system that facilitated real-time integration with clinical decision support systems (CDS). Current annotation systems suffer from a high-resource utilization and poor scalability limiting real-world integration with CDS. A potential solution to mitigate these issues is to use the rule-based gazetteer developed at our institution.<h4>Materials and methods</h4>Performance, resource utilization, and runtime of the ...[more]

PMID: 34423261

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Project description:BackgroundThe COVID-19 pandemic is a significant public health crisis that is hitting hard on people's health, well-being, and freedom of movement, and affecting the global economy. Scientists worldwide are competing to develop therapeutics and vaccines; currently, three drugs and two vaccine candidates have been given emergency authorization use. However, there are still questions of efficacy with regard to specific subgroups of patients and the vaccine's scalability to the general public. Under such circumstances, understanding COVID-19 symptoms is vital in initial triage; it is crucial to distinguish the severity of cases for effective management and treatment. This study aimed to discover symptom patterns and overall symptom rules, including rules disaggregated by age, sex, chronic condition, and mortality status, among COVID-19 patients.MethodsThis study was a retrospective analysis of COVID-19 patient data made available online by the Wolfram Data Repository through May 27, 2020. We applied a widely used rule-based machine learning technique called association rule mining to identify frequent symptoms and define patterns in the rules discovered.ResultIn total, 1,560 patients with COVID-19 were included in the study, with a median age of 52 years. The most frequently occurring symptom was fever (67%), followed by cough (37%), malaise/body soreness (11%), pneumonia (11%), and sore throat (8%). Myocardial infarction, heart failure, and renal disease were present in less than 1% of patients. The top ten significant symptom rules (out of 71 generated) showed cough, septic shock, and respiratory distress syndrome as frequent consequents. If a patient had a breathing problem and sputum production, then, there was higher confidence of that patient having a cough; if cardiac disease, renal disease, or pneumonia was present, then there was a higher confidence of septic shock or respiratory distress syndrome. Symptom rules differed between younger and older patients and between male and female patients. Patients who had chronic conditions or died of COVID-19 had more severe symptom rules than those patients who did not have chronic conditions or survived of COVID-19. Concerning chronic condition rules among 147 patients, if a patient had diabetes, prerenal azotemia, and coronary bypass surgery, there was a certainty of hypertension.ConclusionThe most frequently reported symptoms in patients with COVID-19 were fever, cough, pneumonia, and sore throat; while 1% had severe symptoms, such as septic shock, respiratory distress syndrome, and respiratory failure. Symptom rules differed by age and sex. Patients with chronic disease and patients who died of COVID-19 had severe symptom rules more specifically, cardiovascular-related symptoms accompanied by pneumonia, fever, and cough as consequents.

Project description:During the COVID-19 pandemic, the world saw an exponential surge in the production of Personal Protective Equipment (PPE) kits, which eventually got discarded in the biomedical waste stream. In this study, thirteen different polymer samples from the PPE kit were collected and characterized using Fourier transform infrared spectrometer, thermogravimetric analysis, and analytical pyrolysis-gas chromatograph/mass spectrometry. The characterization data showed that about 94 % by mass of components were made of only three polymers, viz. polypropylene (PP, 75.6 wt %), polyethylene terephthalate (PET, 12.5 wt %), and polycarbonate (PC, 6 wt %). The analytical pyrolysis of the PPE coverall suit (PP) yielded mainly alkenes containing 2,4-dimethyl-1-heptene as the major compound with 17 wt % yield at 600 °C. The pyrolysates from face shield (PET) were rich in benzoic acid (5.8 wt %) and acetophenone (4.8 wt %), while those from safety goggles (PC) were rich in phenol (17.6 wt %) and p-cresol (12.4 wt %) at 600 °C. HZSM-5 and HY zeolites were used for the catalytic upgradation of pyrolysates especially from PP, PET and PC. The temperature and feed-to-catalyst ratio were optimized by performing catalytic fast pyrolysis experiments at 500 °C, 600 °C and 700 °C with different feed-to-catalyst ratios 1:2, 1:4, and 1:6 (w/w). The yield of aromatic hydrocarbons, viz., BTEX (benzene, toluene, ethylbenzene, xylenes) and naphthalene, was maximum (∼25.7 wt %) from PP coverall when HY catalyst was used at 600 °C and 1:6 (w/w) loading. In the case of PET face shield, the total yield of BTEX, naphthalene and biphenyl was maximum (27.9 wt %) at 600 °C and 1:4 (w/w) of HZSM-5, while in the case of PC goggles, it was maximum (18.6 wt %) at 700 °C and 1:4 (w/w) of HY. This study shows that the entire PPE kit can be valorized via catalytic fast pyrolysis to generate petrochemical products and platform molecules like monoaromatic hydrocarbons at high selectivities.

Dataset Information

A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification.

Objective

Materials and methods

Results

Discussion

Conclusion

Publications

A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification.

Similar Datasets

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