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      The Generation of a Lung Cancer Health Factor Distribution Using Patient Graphs Constructed From Electronic Medical Records: Retrospective Study

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          Abstract

          Background

          Electronic medical records (EMRs) of patients with lung cancer (LC) capture a variety of health factors. Understanding the distribution of these factors will help identify key factors for risk prediction in preventive screening for LC.

          Objective

          We aimed to generate an integrated biomedical graph from EMR data and Unified Medical Language System (UMLS) ontology for LC, and to generate an LC health factor distribution from a hospital EMR of approximately 1 million patients.

          Methods

          The data were collected from 2 sets of 1397 patients with and those without LC. A patient-centered health factor graph was plotted with 108,000 standardized data, and a graph database was generated to integrate the graphs of patient health factors and the UMLS ontology. With the patient graph, we calculated the connection delta ratio (CDR) for each of the health factors to measure the relative strength of the factor’s relationship to LC.

          Results

          The patient graph had 93,000 relations between the 2794 patient nodes and 650 factor nodes. An LC graph with 187 related biomedical concepts and 188 horizontal biomedical relations was plotted and linked to the patient graph. Searching the integrated biomedical graph with any number or category of health factors resulted in graphical representations of relationships between patients and factors, while searches using any patient presented the patient’s health factors from the EMR and the LC knowledge graph (KG) from the UMLS in the same graph. Sorting the health factors by CDR in descending order generated a distribution of health factors for LC. The top 70 CDR-ranked factors of disease, symptom, medical history, observation, and laboratory test categories were verified to be concordant with those found in the literature.

          Conclusions

          By collecting standardized data of thousands of patients with and those without LC from the EMR, it was possible to generate a hospital-wide patient-centered health factor graph for graph search and presentation. The patient graph could be integrated with the UMLS KG for LC and thus enable hospitals to bring continuously updated international standard biomedical KGs from the UMLS for clinical use in hospitals. CDR analysis of the graph of patients with LC generated a CDR-sorted distribution of health factors, in which the top CDR-ranked health factors were concordant with the literature. The resulting distribution of LC health factors can be used to help personalize risk evaluation and preventive screening recommendations.

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          Most cited references31

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          Reduced lung-cancer mortality with low-dose computed tomographic screening.

          (2011)
          The aggressive and heterogeneous nature of lung cancer has thwarted efforts to reduce mortality from this cancer through the use of screening. The advent of low-dose helical computed tomography (CT) altered the landscape of lung-cancer screening, with studies indicating that low-dose CT detects many tumors at early stages. The National Lung Screening Trial (NLST) was conducted to determine whether screening with low-dose CT could reduce mortality from lung cancer. From August 2002 through April 2004, we enrolled 53,454 persons at high risk for lung cancer at 33 U.S. medical centers. Participants were randomly assigned to undergo three annual screenings with either low-dose CT (26,722 participants) or single-view posteroanterior chest radiography (26,732). Data were collected on cases of lung cancer and deaths from lung cancer that occurred through December 31, 2009. The rate of adherence to screening was more than 90%. The rate of positive screening tests was 24.2% with low-dose CT and 6.9% with radiography over all three rounds. A total of 96.4% of the positive screening results in the low-dose CT group and 94.5% in the radiography group were false positive results. The incidence of lung cancer was 645 cases per 100,000 person-years (1060 cancers) in the low-dose CT group, as compared with 572 cases per 100,000 person-years (941 cancers) in the radiography group (rate ratio, 1.13; 95% confidence interval [CI], 1.03 to 1.23). There were 247 deaths from lung cancer per 100,000 person-years in the low-dose CT group and 309 deaths per 100,000 person-years in the radiography group, representing a relative reduction in mortality from lung cancer with low-dose CT screening of 20.0% (95% CI, 6.8 to 26.7; P=0.004). The rate of death from any cause was reduced in the low-dose CT group, as compared with the radiography group, by 6.7% (95% CI, 1.2 to 13.6; P=0.02). Screening with the use of low-dose CT reduces mortality from lung cancer. (Funded by the National Cancer Institute; National Lung Screening Trial ClinicalTrials.gov number, NCT00047385.).
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            The Unified Medical Language System (UMLS): integrating biomedical terminology.

            The Unified Medical Language System (http://umlsks.nlm.nih.gov) is a repository of biomedical vocabularies developed by the US National Library of Medicine. The UMLS integrates over 2 million names for some 900,000 concepts from more than 60 families of biomedical vocabularies, as well as 12 million relations among these concepts. Vocabularies integrated in the UMLS Metathesaurus include the NCBI taxonomy, Gene Ontology, the Medical Subject Headings (MeSH), OMIM and the Digital Anatomist Symbolic Knowledge Base. UMLS concepts are not only inter-related, but may also be linked to external resources such as GenBank. In addition to data, the UMLS includes tools for customizing the Metathesaurus (MetamorphoSys), for generating lexical variants of concept names (lvg) and for extracting UMLS concepts from text (MetaMap). The UMLS knowledge sources are updated quarterly. All vocabularies are available at no fee for research purposes within an institution, but UMLS users are required to sign a license agreement. The UMLS knowledge sources are distributed on CD-ROM and by FTP.
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              Non-small-cell lung cancer

              Lung cancer is one of the most frequently diagnosed cancers and is the leading cause of cancer-related death worldwide. Non-small-cell lung cancer (NSCLC), a heterogeneous class of tumours, represents approximately 85% of all new lung cancer diagnoses. Tobacco smoking remains the main risk factor for developing this disease, but radon exposure and air pollution also have a role. Most patients are diagnosed with advanced-stage disease owing to inadequate screening programmes and late onset of clinical symptoms; consequently, patients have a very poor prognosis. Several diagnostic approaches can be used for NSCLC, including X-ray, CT and PET imaging, and histological examination of tumour biopsies. Accurate staging of the cancer is required to determine the optimal management strategy, which includes surgery, radiochemotherapy, immunotherapy and targeted approaches with anti-angiogenic monoclonal antibodies or tyrosine kinase inhibitors if tumours harbour oncogene mutations. Several of these driver mutations have been identified (for example, in epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK)), and therapy continues to advance to tackle acquired resistance problems. Also, palliative care has a central role in patient management and greatly improves quality of life. For an illustrated summary of this Primer, visit: http://go.nature.com/rWYFgg.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                November 2022
                25 November 2022
                : 24
                : 11
                : e40361
                Affiliations
                [1 ] Institutes for System Genetics West China Hospital Chengdu China
                [2 ] iHealthd Shanghai Inc Shanghai China
                [3 ] Guilin Medical University Affiliateted Hospital Guilin China
                [4 ] Guilin Medical University Guilin China
                Author notes
                Corresponding Author: Bairong Shen bairong.shen@ 123456scu.edu.cn
                Author information
                https://orcid.org/0000-0003-4209-8301
                https://orcid.org/0000-0001-6421-3361
                https://orcid.org/0000-0002-2363-3738
                https://orcid.org/0000-0001-5932-9829
                https://orcid.org/0000-0002-6370-3964
                https://orcid.org/0000-0002-4547-8513
                https://orcid.org/0000-0003-3112-3807
                https://orcid.org/0000-0003-2899-1531
                Article
                v24i11e40361
                10.2196/40361
                9736747
                36427233
                35172bb2-ba6d-4943-b82d-022b323149f5
                ©Anjun Chen, Ran Huang, Erman Wu, Ruobing Han, Jian Wen, Qinghua Li, Zhiyong Zhang, Bairong Shen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.11.2022.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 21 June 2022
                : 30 August 2022
                : 9 September 2022
                : 25 October 2022
                Categories
                Original Paper
                Original Paper

                Medicine
                lung cancer,risk factor,patient graph,umls knowledge graph,unified medical language system,connection delta ratio,emr,electronic health record,ehr,cancer

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