Identifying Disease of Interest With Deep Learning Using Diagnosis Code.

BACKGROUND: Autoencoder (AE) is one of the deep learning techniques that uses an artificial neural network to reconstruct its input data in the output layer. We constructed a novel supervised AE model and tested its performance in the prediction of a co-existence of the disease of interest only using diagnostic codes. METHODS: Diagnostic codes of one million randomly sampled patients listed in the Korean National Health Information Database in 2019 were used to train, validate, and test the prediction model. The first used AE solely for a feature engineering tool for an input of a classifier. Supervised Multi-Layer Perceptron (sMLP) was added to train a classifier to predict a binary level with latent representation as an input (AE + sMLP). The second model simultaneously updated the parameters in the AE and the connected MLP classifier during the learning process (End-to-End Supervised AE [EEsAE]). We tested the performances of these two models against baseline models, eXtreme Gradient Boosting (XGB) and naïve Bayes, in the prediction of co-existing gastric cancer diagnosis. RESULTS: The proposed EEsAE model yielded the highest F1-score and highest area under the curve (0.86). The EEsAE and AE + sMLP gave the highest recalls. XGB yielded the highest precision. Ablation study revealed that iron deficiency anemia, gastroesophageal reflux disease, essential hypertension, gastric ulcers, benign prostate hyperplasia, and shoulder lesion were the top 6 most influential diagnoses on performance. CONCLUSION: A novel EEsAE model showed promising performance in the prediction of a disease of interest.

Academic cardiac electrophysiologists' perspectives on sleep apnea care.

PURPOSE: Obstructive sleep apnea syndrome (OSAS) is an important, modifiable risk factor in the pathophysiology of arrhythmias including atrial fibrillation (AF). The purpose of the study was to evaluate cardiac electrophysiologists' (EPs) perception of OSAS. METHODS: We designed a 27-item online Likert scale-based survey instrument entailing several domains: (1) relevance of OSAS in EP practice, (2) OSAS screening and diagnosis, (3) perception on treatments for OSAS, (4) opinion on the OSAS care model. The survey was distributed to 89 academic EP programs in the USA and Canada. While the survey instrument questions refer to the term sleep apnea (SA), our discussion of the diagnosis, management, and research on the sleep disorder is more accurately described with the term OSAS. RESULTS: A total of 105 cardiac electrophysiologists from 49 institutions responded over a 9-month period. The majority of respondents agreed that sleep apnea (SA) is a major concern in their practice (94%). However, 42% reported insufficient education on SA during training. Many (58%) agreed that they would be comfortable managing SA themselves with proper training and education and 66% agreed cardiac electrophysiologists should become more involved in management. Half of EPs (53%) were not satisfied with the sleep specialist referral process. Additionally, a majority (86%) agreed that trained advanced practice providers should be able to assess and manage SA. Time constraints, lack of knowledge, and the referral process are identified as major barriers to EPs becoming more involved in SA care. CONCLUSIONS: We found that OSAS is widely recognized as a major concern for EP. However, incorporation of OSAS care in training and routine practice lags. Barriers to increased involvement include time constraints and education. This study can serve as an impetus for innovation in the cardiology OSAS care model.

Nationwide Results of COVID-19 Contact Tracing in South Korea: Individual Participant Data From an Epidemiological Survey.

BACKGROUND: Evidence regarding the effectiveness of contact tracing of COVID-19 and the related social distancing is limited and inconclusive. OBJECTIVE: This study aims to investigate the epidemiological characteristics of SARS-CoV-2 transmission in South Korea and evaluate whether a social distancing campaign is effective in mitigating the spread of COVID-19. METHODS: We used contract tracing data to investigate the epidemic characteristics of SARS-CoV-2 transmission in South Korea and evaluate whether a social distancing campaign was effective in mitigating the spread of COVID-19. We calculated the mortality rate for COVID-19 by infection type (cluster vs noncluster) and tested whether new confirmed COVID-19 trends changed after a social distancing campaign. RESULTS: There were 2537 patients with confirmed COVID-19 who completed the epidemiologic survey: 1305 (51.4%) cluster cases and 1232 (48.6%) noncluster cases. The mortality rate was significantly higher in cluster cases linked to medical facilities (11/143, 7.70% vs 5/1232, 0.41%; adjusted percentage difference 7.99%; 95% CI 5.83 to 10.14) and long-term care facilities (19/221, 8.60% vs 5/1232, 0.41%; adjusted percentage difference 7.56%; 95% CI 5.66 to 9.47) than in noncluster cases. The change in trends of newly confirmed COVID-19 cases before and after the social distancing campaign was significantly negative in the entire cohort (adjusted trend difference -2.28; 95% CI -3.88 to -0.68) and the cluster infection group (adjusted trend difference -0.96; 95% CI -1.83 to -0.09). CONCLUSIONS: In a nationwide contact tracing study in South Korea, COVID-19 linked to medical and long-term care facilities significantly increased the risk of mortality compared to noncluster COVID-19. A social distancing campaign decreased the spread of COVID-19 in South Korea and differentially affected cluster infections of SARS-CoV-2.

Deep regression neural networks for collateral imaging from dynamic susceptibility contrast-enhanced magnetic resonance perfusion in acute ischemic stroke.

PURPOSE: Acute ischemic stroke is one of the primary causes of death worldwide. Recent studies have shown that the assessment of collateral status could aid in improving the treatment for patients with acute ischemic stroke. We present a 3D deep regression neural network to automatically generate the collateral images from dynamic susceptibility contrast-enhanced magnetic resonance perfusion (DSC-MRP) in acute ischemic stroke. METHODS: This retrospective study includes 144 subjects with acute ischemic stroke (stroke cases) and 201 subjects without acute ischemic stroke (controls). DSC-MRP images of these subjects were manually inspected for collateral assessment in arterial, capillary, early and late venous, and delay phases. The proposed network was trained on 205 subjects, and the optimal model was chosen using the validation set of 64 subjects. The predictive power of the network was assessed on the test set of 76 subjects using the squared correlation coefficient (R-squared), mean absolute error (MAE), Tanimoto measure (TM), and structural similarity index (SSIM). RESULTS: The proposed network was able to predict the five phase maps with high accuracy. On average, 0.897 R-squared, 0.581 × 10-1 MAE, 0.946 TM, and 0.846 SSIM were achieved for the five phase maps. No statistically significant difference was, in general, found between controls and stroke cases. The performance of the proposed network was lower in the arterial and venous phases than the other three phases. CONCLUSION: The results suggested that the proposed network performs equally well for both control and acute ischemic stroke groups. The proposed network could help automate the assessment of collateral status in an efficient and effective manner and improve the quality and yield of diagnosis of acute ischemic stroke. The follow-up study will entail the clinical evaluation of the collateral images that are generated by the proposed network.

Association between employment status and self-rated health: Korean working conditions survey.

BACKGROUND: This research was conducted with an aim of determining the association between employment status and self-rated health. METHODS: Using the data from the Third Korean Working Conditions Survey conducted in 2011, We included data from 34,783 respondents, excluding employers, self-employed workers, unpaid family workers, others. Self-rated health was compared according to employment status and a logistic regression analysis was performed. RESULTS: Among the 34,783 workers, the number of permanent and non-permanent workers was 27,564 (79.2 %) and 7,219 (20.8 %). The risk that the self-rated health of non-permanent workers was poor was 1.20 times higher when both socio-demographic factors, work environment and work hazards were corrected. CONCLUSIONS: In this study, perceived health was found to be worse in the non-permanent workers than permanent workers. Additional research should investigate whether other factors mediate the relationship between employment status and perceived health.