Background: This study aimed to identify the clinical characteristics of multidrug-resistant/ rifampicin-resistant tuberculosis (MDR/RR-TB) in the Republic of Korea. Methods: Data of notified people with tuberculosis between July 2018 and December 2021 were retrieved from the Korea Tuberculosis Cohort database. MDR/RR-TB was further categorized according to isoniazid susceptibility as follows: multidrug-resistant tuberculosis (MDR-TB), rifampicin-monoresistant tuberculosis (RMR-TB), and RR-TB if susceptibility to isoniazid was unknown. Multivariable logistic regression analysis was conducted to identify the factors associated with MDR/RR-TB. Results: Between 2018 and 2021, the proportion of MDR/RR-TB cases among all TB cases and TB cases with known drug susceptibility test results was 2.1% (502/24,447). The proportions of MDR/RR-TB and MDR-TB cases among TB cases with known drug susceptibility test results were 3.3% (502/15,071) and 1.9% (292/15,071), respectively. Among all cases of rifampicin resistance, 31.7% (159/502) were RMR-TB and 10.2% (51/502) were RR-TB. Multivariable logistic regression analyses revealed that younger age, foreigners, and prior tuberculosis history were significantly associated with MDR/ RR-TB. Conclusion Rapid identification of rifampicin resistance targeting the high-risk populations, such as younger generations, foreign-born individuals, and previously treated patients are necessary for patient-centered care.

Background: This study aimed to identify the clinical characteristics of multidrug-resistant/ rifampicin-resistant tuberculosis (MDR/RR-TB) in the Republic of Korea. Methods: Data of notified people with tuberculosis between July 2018 and December 2021 were retrieved from the Korea Tuberculosis Cohort database. MDR/RR-TB was further categorized according to isoniazid susceptibility as follows: multidrug-resistant tuberculosis (MDR-TB), rifampicin-monoresistant tuberculosis (RMR-TB), and RR-TB if susceptibility to isoniazid was unknown. Multivariable logistic regression analysis was conducted to identify the factors associated with MDR/RR-TB. Results: Between 2018 and 2021, the proportion of MDR/RR-TB cases among all TB cases and TB cases with known drug susceptibility test results was 2.1% (502/24,447). The proportions of MDR/RR-TB and MDR-TB cases among TB cases with known drug susceptibility test results were 3.3% (502/15,071) and 1.9% (292/15,071), respectively. Among all cases of rifampicin resistance, 31.7% (159/502) were RMR-TB and 10.2% (51/502) were RR-TB. Multivariable logistic regression analyses revealed that younger age, foreigners, and prior tuberculosis history were significantly associated with MDR/ RR-TB. Conclusion Rapid identification of rifampicin resistance targeting the high-risk populations, such as younger generations, foreign-born individuals, and previously treated patients are necessary for patient-centered care.

Background: This study aimed to identify the clinical characteristics of multidrug-resistant/ rifampicin-resistant tuberculosis (MDR/RR-TB) in the Republic of Korea. Methods: Data of notified people with tuberculosis between July 2018 and December 2021 were retrieved from the Korea Tuberculosis Cohort database. MDR/RR-TB was further categorized according to isoniazid susceptibility as follows: multidrug-resistant tuberculosis (MDR-TB), rifampicin-monoresistant tuberculosis (RMR-TB), and RR-TB if susceptibility to isoniazid was unknown. Multivariable logistic regression analysis was conducted to identify the factors associated with MDR/RR-TB. Results: Between 2018 and 2021, the proportion of MDR/RR-TB cases among all TB cases and TB cases with known drug susceptibility test results was 2.1% (502/24,447). The proportions of MDR/RR-TB and MDR-TB cases among TB cases with known drug susceptibility test results were 3.3% (502/15,071) and 1.9% (292/15,071), respectively. Among all cases of rifampicin resistance, 31.7% (159/502) were RMR-TB and 10.2% (51/502) were RR-TB. Multivariable logistic regression analyses revealed that younger age, foreigners, and prior tuberculosis history were significantly associated with MDR/ RR-TB. Conclusion Rapid identification of rifampicin resistance targeting the high-risk populations, such as younger generations, foreign-born individuals, and previously treated patients are necessary for patient-centered care.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: This study aimed to identify the clinical characteristics of multidrug-resistant/ rifampicin-resistant tuberculosis (MDR/RR-TB) in the Republic of Korea. Methods: Data of notified people with tuberculosis between July 2018 and December 2021 were retrieved from the Korea Tuberculosis Cohort database. MDR/RR-TB was further categorized according to isoniazid susceptibility as follows: multidrug-resistant tuberculosis (MDR-TB), rifampicin-monoresistant tuberculosis (RMR-TB), and RR-TB if susceptibility to isoniazid was unknown. Multivariable logistic regression analysis was conducted to identify the factors associated with MDR/RR-TB. Results: Between 2018 and 2021, the proportion of MDR/RR-TB cases among all TB cases and TB cases with known drug susceptibility test results was 2.1% (502/24,447). The proportions of MDR/RR-TB and MDR-TB cases among TB cases with known drug susceptibility test results were 3.3% (502/15,071) and 1.9% (292/15,071), respectively. Among all cases of rifampicin resistance, 31.7% (159/502) were RMR-TB and 10.2% (51/502) were RR-TB. Multivariable logistic regression analyses revealed that younger age, foreigners, and prior tuberculosis history were significantly associated with MDR/ RR-TB. Conclusion Rapid identification of rifampicin resistance targeting the high-risk populations, such as younger generations, foreign-born individuals, and previously treated patients are necessary for patient-centered care.