Objectives: ZZThe aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-basedmedical records. Methods: ZZElectronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes withthree diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independentvalidation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF)and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vectorclassification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find aneffective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results: ZZFive-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis(accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final workingDL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showedslightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions ZZThe current results suggest that the vectorization may have more impact on the performance of classification thanthe machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category,and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machinelearning models.

Objectives: ZZThe aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-basedmedical records. Methods: ZZElectronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes withthree diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independentvalidation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF)and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vectorclassification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find aneffective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results: ZZFive-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis(accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final workingDL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showedslightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions ZZThe current results suggest that the vectorization may have more impact on the performance of classification thanthe machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category,and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machinelearning models.

Objectives: Assessing the risks of youth suicide in educational and clinical settings is crucial.Therefore, this study developed a machine learning model to predict suicide attempts using the Korean Youth Risk Behavior Web-based Survey (KYRBWS). Methods: KYRBWS is conducted annually on Korean middle and high school students to assess their health-related behaviors. The KYRBWS data for 2021, which showed 1206 adolescents reporting suicide attempts out of 54848, was split into the training (n=43878) and test (n=10970) datasets. Thirty-nine features were selected from the KYRBWS questionnaire. The balanced accuracy of the model was employed as a metric to select the best model. Independent validations were conducted with the test dataset of 2021 KYRBWS (n=10970) and the external dataset of 2020 KYRBWS (n=54948). The clinical implication of the prediction by the selected model was measured for sensitivity, specificity, true prediction rate (TPR), and false prediction rate (FPR). Results: Balanced bag of histogram gradient boosting model has shown the best performance (balanced accuracy=0.803). This model shows 76.23% sensitivity, 83.08% specificity, 10.03% TPR, and 99.30% FPR for the test dataset as well as 77.25% sensitivity, 84.62% specificity, 9.31% TPR, and 99.45% FPR for the external dataset, respectively. Conclusion These results suggest that a specific machine learning model can predict suicide attempts among adolescents with high accuracy.

Objective: Suicidal ideation is one of the strongest predictors of suicide, and its relevance to impulsivity in depressed patients has been accumulated. Furthermore, high impulsivity patients show the attenuation of the Nogo amplitude in the GoNogo event-related potential (ERP). The purpose of the current study is to determine the correlation of Nogo ERP to the suicidal ideation depending on the condition of its presence or absence in major depressive disorder (MDD) patients. Methods: A total 162 participants (104 patients with suicidal ideation, 31 patients without suicidal ideation, and 27 healthy controls) were recruited, and performed GoNogo tasks during the electroencephalogram measurement. Depression, anxiety, suicidal ideation and impulsivity were assessed by self-rating scales. The clinical measures, behavioral data and Nogo ERP were compared among groups. Results: The MDD with suicidal ideation (SI) group showed significantly decreased Nogo P3 amplitudes compared to MDD without SI (Fz and Cz electrodes) and control group (all electrodes). The MDD with SI group also had significantly low accuracy of both Go and Nogo trails, compared to the MDD without group. The Nogo P3 amplitudes showed the negative relation to the scores of impulsivity, depression, anxiety and SI. Conclusion Our results concluded that the Nogo P3 ERP amplitude was decreased in MDD patients with SI compared to MDD patients without SI and controls. These findings suggest that the decreased Nogo P3 amplitude is the one of the candidate biomarker for impulsivity in MDD patients to evaluating SI.

Objective: The loudness dependence of the auditory evoked potential (LDAEP) is a reliable indicator that is inversely related to central serotonergic activity, and recent studies have suggested an association between LDAEP and suicidal ideation. This study investigated differences in LDAEP between patients with major depressive disorder and high suicidality and those with major depressive disorder and low suicidality compared to healthy controls. Methods: This study included 67 participants: 23 patients with major depressive disorder with high suicidality (9 males, mean age 29.3 ± 15.7 years, total score of SSI-BECK ≥ 15), 22 patients with major depressive disorder with low suicidality (9 males, mean age 42.2 ± 14.4 years, total score of SSI-BECK ≤ 14), and 22 healthy controls (11 males, mean age 31.6 ± 8.7 years). Participants completed the following assessments: Patient Health Questionnaire-9, Beck Depression Inventory-II, Beck Scale for Suicidal ideation, State Anxiety Scale of the State-Trait Anxiety Inventory, Beck Anxiety Inventory, and LDAEP (measured at electrode Cz). Results: There were no sex-related differences among groups (p = 0.821). The high-suicidality group exhibited significantly higher LDAEP compared to the low-suicidality group (0.82 ± 0.79 vs. 0.26 ± 0.36, p = 0.014). No significant differences were found between the control and high-suicidality (p = 0.281) or the control and low-suicidality groups (p = 0.236). Conclusion LDAEP was applied to demonstrate the association between serotonergic activity and suicidal ideation and suicide risk in major depression and may be a candidate of biological marker for preventing suicide in this study.

Objective: The loudness dependence of the auditory evoked potential (LDAEP) is a reliable indicator that is inversely related to central serotonergic activity, and recent studies have suggested an association between LDAEP and suicidal ideation. This study investigated differences in LDAEP between patients with major depressive disorder and high suicidality and those with major depressive disorder and low suicidality compared to healthy controls. Methods: This study included 67 participants: 23 patients with major depressive disorder with high suicidality (9 males, mean age 29.3 ± 15.7 years, total score of SSI-BECK ≥ 15), 22 patients with major depressive disorder with low suicidality (9 males, mean age 42.2 ± 14.4 years, total score of SSI-BECK ≤ 14), and 22 healthy controls (11 males, mean age 31.6 ± 8.7 years). Participants completed the following assessments: Patient Health Questionnaire-9, Beck Depression Inventory-II, Beck Scale for Suicidal ideation, State Anxiety Scale of the State-Trait Anxiety Inventory, Beck Anxiety Inventory, and LDAEP (measured at electrode Cz). Results: There were no sex-related differences among groups (p = 0.821). The high-suicidality group exhibited significantly higher LDAEP compared to the low-suicidality group (0.82 ± 0.79 vs. 0.26 ± 0.36, p = 0.014). No significant differences were found between the control and high-suicidality (p = 0.281) or the control and low-suicidality groups (p = 0.236). Conclusion LDAEP was applied to demonstrate the association between serotonergic activity and suicidal ideation and suicide risk in major depression and may be a candidate of biological marker for preventing suicide in this study.

OBJECTIVES: We aimed to explore the influence of depression on working memory in patients with mild cognitive impairment (MCI) and dementia. METHODS: Clinical and neuropsychological data of 43 subjects with mild cognitive impairment (MCI) (n=17) and dementia (n=26) who had visited Department of Psychiatry at Soonchunhyang University Seoul Hospital, were collected. The subjects were divided into depressed (n=18) and non-depressed (n=25) groups based on the Korean version of Short Geriatric Depression Scale. Two-way analysis of variance test was conducted to evaluate the influence of diagnosis (MCI and dementia), the presence of depression and their interaction on working memory which was measured by digit forward and backward span test. RESULTS: Among the patients with MCI, test score of digit backward span test in depressed group was significantly lower than in non-depressed group. However, among the patients with dementia, there was no significant difference in digit backward span test between depressed and non-depressed groups. CONCLUSION: This study suggests that the depression could deteriorate working memory measured by digit backward span test in patients with MCI, relative to in patients with dementia and it also implicates the diagnostic assessment for depression has clinically importance in patients with MCI.
Dementia* ; Depression* ; Diagnosis ; Humans ; Memory, Short-Term* ; Mild Cognitive Impairment* ; Seoul

Objectives: Suicide is the leading cause of death among adolescents in South Korea, and depression and personality profiles have been identified as significant risk factors for self-injurious behavior. This study examined the influence of depressive mood and temperament/ character on self-injury in adolescents. Methods: A total of 116 adolescents (aged 12–18 years) with a primary diagnosis of major depressive disorder (MDD) and their parents were enrolled in this study. The participants were divided into three groups based on adolescent’s self-injury frequency, and their Children’s Depression Inventory (CDI), Youth Self-Report (YSR), and Temperament and Character Inventory (TCI) scores were compared. Finally, mediation analysis was conducted to investigate the relationship between suicidal ideation and self-injury. Results: Of study participants, 75.9% answered that they had suicidal ideation, and 55.2% answered that they had engaged in self-injurious behavior in the last six months. There were significant differences in CDI and suicidal ideation among the groups. After adjusting for age and sex, mediation analysis indicated that depressive mood mediated the relationship between suicidal ideation and self-injury. Conclusion This study emphasizes the importance of evaluating and managing depressive mood severity in adolescents with MDD as these factors partially mediate the transition from suicidal ideation to self-injury.

Purpose: Currently, there are multiple options for the pharmacological treatment of asthma. This study aimed to compare the effects of different asthma medications on exacerbation in a real-world setting. Materials and Methods: We retrospectively reviewed electronic medical records of asthma patients who visited the hospital from November 1, 2016 to October 31, 2019. The number of asthma exacerbations requiring administration of systemic steroids was the primary outcome. A time-varying Cox regression analysis was used to reflect the real-world setting: variable usage times, discontinuation, and switching of medication. Results: Among 937 patients with asthma, 228 (24.3%) experienced asthma exacerbation during the study period. Asthma exacerbation was observed in patients using short-acting β 2-agonists (SABA) alone (50.4% vs. 28.6%, p<0.001) as well as in patients not using inhaled corticosteroids (ICS) (58.8% vs. 40.3%, p<0.001), long-acting β 2-agonists (LABA) (54.8% vs. 36.1%, p<0.001), and leukotriene receptor antagonists (71.5% vs. 50.8%, p<0.001). A time-varying Cox regression analysis of asthma exacerbations according to the duration of asthma medication showed that SABA alone increased the risk of asthma exacerbation [hazard ratio (HR), 1.834; 95% confidence interval (CI), 1.299–2.588; p=0.001], whereas ICS-LABA decreased the risk (HR, 0.733; 95% CI, 0.538–0.997; p=0.048). However, in the subgroup analysis according to medication type, specific ingredients showed no significant differences. Conclusion In the real world, asthma medications affect asthma exacerbation variably according to the medication type.

The continuous emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants has provided insights for updating current coronavirus disease 2019 (COVID-19) vaccines. We examined the neutralizing activity of Abs induced by a BA.4/5-containing bivalent mRNA vaccine against Omicron subvariants BN.1 and XBB.1.5. We recruited 40 individuals who had received a monovalent COVID-19 booster dose after a primary series of COVID-19 vaccinations and will be vaccinated with a BA.4/5-containing bivalent vaccine. Sera were collected before vaccination, one month after, and three months after a bivalent booster.Neutralizing Ab (nAb) titers were measured against ancestral SARS-CoV-2 and Omicron subvariants BA.5, BN.1, and XBB.1.5. BA.4/5-containing bivalent vaccination significantly boosted nAb levels against both ancestral SARS-CoV-2 and Omicron subvariants. Participants with a history of SARS-CoV-2 infection had higher nAb titers against all examined strains than the infection-naïve group. NAb titers against BN.1 and XBB.1.5 were lower than those against the ancestral SARS-CoV-2 and BA.5 strains. These results suggest that COVID-19 vaccinations specifically targeting emerging Omicron subvariants, such as XBB.1.5, may be required to ensure better protection against SARS-CoV-2 infection, especially in high-risk groups.