Prospect of Artificial Intelligence Based on Electronic Medical Records

Lee, Suehyun; Kim, Hun-Sung

doi:10.12997/jla.2021.10.3.282

Cited by 38 publications

(26 citation statements)

References 20 publications

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“…Especially, with the development of artificial intelligence, the use of large-scale medical data and the necessity of individualized customized treatments are being highlighted. Therefore, many researchers are interested in structuring and standardizing data to properly use electronic medical record (EMR) clinically and are trying to develop guidelines or certification standards for its use [20] . However, in KM, researchers face several obstacles in utilizing EMR information of KM because of the difficulty in patient follow-up, non-reimbursable nature of many treatments, and multi-facetedness of KM treatment.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, many researchers are interested in structuring and standardizing data to properly use electronic medical record (EMR) clinically and are trying to develop guidelines or certification standards for its use. [ 20 ] However, in KM, researchers face several obstacles in utilizing EMR information of KM because of the difficulty in patient follow-up, non-reimbursable nature of many treatments, and multi-facetedness of KM treatment. In order to collect KM data, it is necessary to collect patient information with a purpose, such as establishing a registry, to increase precision and usability.…”

Section: Discussionmentioning

confidence: 99%

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Korean medicine registry of herbal medicine for weight loss

Kim

Son

et al. 2022

Medicine

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Introduction: In South Korea, the prevalence of obesity has continuously increased over the last decade, and the burden of social and economic costs has also increased immensely. The purpose of this study is to investigate the clinical characteristics and current status of patients receiving herbal medicine (HM) treatment for weight loss in Korean medicine (KM) by constructing a multi-institutional prospective registry. Methods and analysis: This is a prospective, observational, multi-center registry, including patients visiting the KM clinics in South Korea for weight loss. This study will collaborate with 15 KM clinics and recruit patients into the registry between October 2021 and October 2022. The study population will include patients visiting the KM clinics for weight loss. A total of 1000 eligible patients visiting the KM clinics for weight loss who decide to undergo HM treatment will be enrolled in the registry. After enrollment, we will collect the individual characteristics of each patient, such as body mass index, body composition test, liver and kidney function tests, and information on prescribed HM. We will also record the adverse events at each visit. Discussion: This study is the first prospective, multicenter registry of HM for weight loss in KM clinics. The results of this registry may show the current status of patients who receive HM treatments for weight loss and provide evidence for reasonable decision-making on KM healthcare policy for obese patients in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Korean medicine registry of herbal medicine for weight loss

Kim

Son

et al. 2022

Medicine

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“…EMR data contains disease- and phenotype-related information that can be used as raw data for drug discovery [ 25 , 81 ]. The ability to conduct large-scale follow-up studies related to patient outcomes collected from EMR is an important advantage [ 82 - 84 ]. Medical databases such as EMR and claim data provide health records of millions of individuals, rendering them suitable for discovering new indications for available drugs [ 85 ].…”

Section: Dataset For Drug Repositioningmentioning

confidence: 99%

A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus

2022

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Drug repositioning is a strategy for identifying new applications of an existing drug that has been previously proven to be safe. Based on several examples of drug repositioning, we aimed to determine the methodologies and relevant steps associated with drug repositioning that should be pursued in the future. Reports on drug repositioning, retrieved from PubMed from January 2011 to December 2020, were classified based on an analysis of the methodology and reviewed by experts. Among various drug repositioning methods, the network-based approach was the most common (38.0%, 186/490 cases), followed by machine learning/deep learningbased (34.3%, 168/490 cases), text mining-based (7.1%, 35/490 cases), semantic-based (5.3%, 26/490 cases), and others (15.3%, 75/490 cases). Although drug repositioning offers several advantages, its implementation is curtailed by the need for prior, conclusive clinical proof. This approach requires the construction of various databases, and a deep understanding of the process underlying repositioning is quintessential. An in-depth understanding of drug repositioning could reduce the time, cost, and risks inherent to early drug development, providing reliable scientific evidence. Furthermore, regarding patient safety, drug repurposing might allow the discovery of new relationships between drugs and diseases.

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“…In addition, the biological pathways associated with characteristic cfDNA methylation were analyzed. For the study, EM-seq and a series of data processing methods, including machine learning [ 18 ], were used.…”

Section: Introductionmentioning

confidence: 99%

Differentially hypomethylated cell-free DNA and coronary collateral circulation

Ahn

Heo²,

Ahn

et al. 2022

Clin Epigenet

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Background The factors affecting cardioprotective collateral circulation are still incompletely understood. Recently, characteristics, such as CpG methylation of cell-free DNA (cfDNA), have been reported as markers with clinical utility. The aim of this study was to evaluate whether cfDNA methylation patterns are associated with the grade of coronary collateral circulation (CCC). Result In this case–control study, clinical and angiographic data were obtained from 143 patients (mean age, 58 years, male 71%) with chronic total coronary occlusion. Enzymatic methyl-sequencing (EM-seq) libraries were prepared using the cfDNA extracted from the plasma. Data were processed to obtain the average methylation fraction (AMF) tables of genomic regions from which blacklisted regions were removed. Unsupervised analysis of the obtained AMF values showed that some of the changes in methylation were due to CCC. Through random forest preparation process, 256 differentially methylated region (DMR) candidates showing strong association with CCC were selected. A random forest classifier was then constructed, and the area under the curve of the receiver operating characteristic curve indicated an appropriate predictive function for CCC. Finally, 20 DMRs were identified to have significantly different AMF values between the good and poor CCC groups. Particularly, the good CCC group exhibited hypomethylated DMRs. Pathway analysis revealed five pathways, including TGF-beta signaling, to be associated with good CCC. Conclusion These data have demonstrated that differential hypomethylation was identified in dozens of cfDNA regions in patients with good CCC. Our results support the clinical utility of noninvasively obtained epigenetic signatures for predicting collateral circulation in patients with vascular diseases.

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Prospect of Artificial Intelligence Based on Electronic Medical Records

Cited by 38 publications

References 20 publications

Korean medicine registry of herbal medicine for weight loss

Korean medicine registry of herbal medicine for weight loss

A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus

Differentially hypomethylated cell-free DNA and coronary collateral circulation

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