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Discovering hidden information in biosignals from patients by artificial intelligence
Yoon Dukyong,Jang Jong-Hwan,Choi Byung Jin,Kim Tae Young,Han Chang Ho 대한마취통증의학회 2020 Korean Journal of Anesthesiology Vol.73 No.4
Biosignals such as electrocardiogram or photoplethysmogram are widely used for determining and monitoring the medical condition of patients. It was recently discovered that more information could be gathered from biosignals by applying artificial intelligence (AI). At present, one of the most impactful advancements in AI is deep learning. Deep learning-based models can extract important features from raw data without feature engineering by humans, provided the amount of data is sufficient. This AI-enabled feature presents opportunities to obtain latent information that may be used as a digital biomarker for detecting or predicting a clinical outcome or event without further invasive evaluation. However, the black box model of deep learning is difficult to understand for clinicians familiar with a conventional method of analysis of biosignals. A basic knowledge of AI and machine learning is required for the clinicians to properly interpret the extracted information and to adopt it in clinical practice. This review covers the basics of AI and machine learning, and the feasibility of their application to real-life situations by clinicians in the near future.
Yoon, Dukyong,Ahn, Eun Kyoung,Park, Man Young,Cho, Soo Yeon,Ryan, Patrick,Schuemie, Martijn J.,Shin, Dahye,Park, Hojun,Park, Rae Woong Korean Society of Medical Informatics 2016 Healthcare Informatics Research Vol.22 No.1
<P><B>Objectives</B></P><P>A distributed research network (DRN) has the advantages of improved statistical power, and it can reveal more significant relationships by increasing sample size. However, differences in data structure constitute a major barrier to integrating data among DRN partners. We describe our experience converting Electronic Health Records (EHR) to the Observational Health Data Sciences and Informatics (OHDSI) Common Data Model (CDM).</P><P><B>Methods</B></P><P>We transformed the EHR of a hospital into Observational Medical Outcomes Partnership (OMOP) CDM ver. 4.0 used in OHDSI. All EHR codes were mapped and converted into the standard vocabulary of the CDM. All data required by the CDM were extracted, transformed, and loaded (ETL) into the CDM structure. To validate and improve the quality of the transformed dataset, the open-source data characterization program ACHILLES was run on the converted data.</P><P><B>Results</B></P><P>Patient, drug, condition, procedure, and visit data from 2.07 million patients who visited the subject hospital from July 1994 to November 2014 were transformed into the CDM. The transformed dataset was named the AUSOM. ACHILLES revealed 36 errors and 13 warnings in the AUSOM. We reviewed and corrected 28 errors. The summarized results of the AUSOM processed with ACHILLES are available at http://ami.ajou.ac.kr:8080/.</P><P><B>Conclusions</B></P><P>We successfully converted our EHRs to a CDM and were able to participate as a data partner in an international DRN. Converting local records in this manner will provide various opportunities for researchers and data holders.</P>
Yoon, Dukyong,Lim, Hong Seok,Jeong, Jong Cheol,Kim, Tae Young,Choi, Jung-gu,Jang, Jong-Hwan,Jeong, Eugene,Park, Chan Min Hindawi 2018 BioMed research international Vol.2018 No.-
<P><B>Background</B></P><P> Proper management of hyperkalemia that leads to fatal cardiac arrhythmia has become more important because of the increased prevalence of hyperkalemia-prone diseases. Although T-wave changes in hyperkalemia are well known, their usefulness is debatable. We evaluated how well T-wave-based features of electrocardiograms (ECGs) are correlated with estimated serum potassium levels using ECG data from real-world clinical practice.</P><P><B> Methods</B></P><P> We collected ECGs from a local ECG repository (MUSE™) from 1994 to 2017 and extracted the ECG waveforms. Of about 1 million reports, 124,238 were conducted within 5 minutes before or after blood collection for serum potassium estimation. We randomly selected 500 ECGs and two evaluators measured the amplitude (T-amp) and right slope of the T-wave (T-right slope) on five lead waveforms (V3, V4, V5, V6, and II). Linear correlations of T-amp, T-right slope, and their normalized feature (T-norm) with serum potassium levels were evaluated using Pearson correlation coefficient analysis.</P><P><B> Results</B></P><P> Pearson correlation coefficients for T-wave-based features with serum potassium between the two evaluators were 0.99 for T-amp and 0.97 for T-right slope. The coefficient for the association between T-amp, T-right slope, and T-norm, and serum potassium ranged from -0.22 to 0.02. In the normal ECG subgroup (normal ECG or otherwise normal ECG), there was no correlation between T-wave-based features and serum potassium level.</P><P><B> Conclusions</B></P><P> T-wave-based features were not correlated with serum potassium level, and their use in real clinical practice is currently limited.</P>
백색포틀랜드시멘트에 대한 이해와 응용 분야에 대한 연구
이덕용(Dukyong Lee),나현엽(Hyeonyeob Na),박수현(Suhyeon Park),엄주일(Juil Eom) 한국세라믹학회 2023 세라미스트 Vol.26 No.4
Research direction of Cement manufacturers is that the trend of high-performance clinker production, which has increased the content of Alite (C3S) that dominate the initial strength of cement is intensifying. The content of Ferrite(C4AF) mineral of White Portland Cement is less than 1%. In the case of White Portland Cement, when applied to concrete, it is free to choose the color of the concrete structure and exhibits excellent aesthetics and reflectance, so it is highly valuable as an architectural and civil engineering material, but it has a disadvantage of being more expensive than Ordinary Portland Cement, and there is a limit to using the circulating resources used in Cement clinker firing process to express white. The characteristics and color differences of cement differ depending on the difference between the raw material and the manufacturi ng met hod. When manufacturing whi t e cement, fuel used to impl ement whi t e and mat erials wi th low heavy metals such as Fe, Cr, and Mn in the raw material are used, cooled to a reducing atmosphere and the cooling method produces white cement clinker by water cooling. Even in process facilities, more careful selection is required when selecting materials for grinding facilities and refractories in the firing process.
Yoon, Dukyong,Sheen, Seung Soo,Lee, Sukhyang,Choi, Yong Jun,Park, Rae Woong,Lim, Hong-Seok Wolters Kluwer Health 2016 Medicine Vol.95 No.46
<▼1><P>Supplemental Digital Content is available in the text</P></▼1><▼2><P><B>Abstract</B></P><P>Although concern regarding the increased risk for new-onset diabetes mellitus (NODM) after statin treatment has been raised, there has been a lack of evidence in real-world clinical practice, particularly in East Asians. We investigated whether statin use is associated with risk for NODM in Koreans. We conducted a retrospective cohort study using the clinical research database from electronic health records. The study cohort consisted of 8265 statin-exposed and 33,060 matched nonexposed patients between January 1996 and August 2013. Matching at a 1:4 ratio was performed using a propensity score based on age, gender, baseline glucose levels (mg/dL), and hypertension. The comparative risks for NODM with various statins (atorvastatin, fluvastatin, pitavastatin, pravastatin, rosuvastatin, and simvastatin) were estimated by both statin exposure versus matched nonexposed and within-class comparisons. The incidence of NODM among the statin-exposed group (6.000 per 1000 patient-years [PY]) was higher than that of the nonexposed group (3.244 per 1000 PY). The hazard ratio (HR) of NODM after statin exposure was 1.872 (95% confidence interval [CI], 1.432–2.445). Male gender (HR, 1.944; 95% CI, 1.497–2.523), baseline glucose per mg/dL (HR, 1.014; 95% CI, 1.013–1.016), hypertension (HR, 2.232; 95% CI, 1.515–3.288), and thiazide use (HR, 1.337; 95% CI, 1.081–1.655) showed an increased risk for NODM, while angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker showed a decreased risk (HR, 0.774; 95% CI, 0.668–0.897). Atorvastatin-exposed patients showed a higher risk for NODM than their matched nonexposed counterparts (HR, 1.939; 95% CI, 1.278–2.943). However, the risk for NODM was not significantly different among statins in within-class comparisons. In conclusion, an increased risk for NODM was observed among statin users in a practical healthcare setting in Korea.</P></▼2>