MED-SE: Medical Entity Definition-based Sentence Embedding

We propose Medical Entity Definition-based Sentence Embedding (MED-SE), a novel unsupervised contrastive learning framework designed for clinical texts, which exploits the definitions of medical entities. To this end, we conduct an extensive analysis of multiple sentence embedding techniques in clinical semantic textual similarity (STS) settings. In the entity-centric setting that we have designed, MED-SE achieves significantly better performance, while the existing unsupervised methods including SimCSE show degraded performance. Our experiments elucidate the inherent discrepancies between the general- and clinical-domain texts, and suggest that entity-centric contrastive approaches may help bridge this gap and lead to a better representation of clinical sentences.Comment: 8 pages, 2 figures, 9 table

Network-Level Structural Abnormalities of Cerebral Cortex in Type 1 Diabetes Mellitus

Type 1 diabetes mellitus (T1DM) usually begins in childhood and adolescence and causes lifelong damage to several major organs including the brain. Despite increasing evidence of T1DM-induced structural deficits in cortical regions implicated in higher cognitive and emotional functions, little is known whether and how the structural connectivity between these regions is altered in the T1DM brain. Using inter-regional covariance of cortical thickness measurements from high-resolution T1-weighted magnetic resonance data, we examined the topological organizations of cortical structural networks in 81 T1DM patients and 38 healthy subjects. We found a relative absence of hierarchically high-level hubs in the prefrontal lobe of T1DM patients, which suggests ineffective top-down control of the prefrontal cortex in T1DM. Furthermore, inter-network connections between the strategic/executive control system and systems subserving other cortical functions including language and mnemonic/emotional processing were also less integrated in T1DM patients than in healthy individuals. The current results provide structural evidence for T1DM-related dysfunctional cortical organization, which specifically underlie the top-down cognitive control of language, memory, and emotion. © 2013 Lyoo et al

Spatiotemporal dissociation of fMRI activity in the caudate nucleus underlies human de novo motor skill learning

© 2020 National Academy of Sciences. Motor skill learning involves a complex process of generating novel movement patterns guided by evaluative feedback, such as a reward. Previous literature has suggested anteroposteriorly separated circuits in the striatum to be implicated in early goaldirected and later automatic stages of motor skill learning, respectively. However, the involvement of these circuits has not been well elucidated in human de novo motor skill learning, which requires learning arbitrary action-outcome associations and valuebased action selection. To investigate this issue, we conducted a human functional MRI (fMRI) experiment in which participants learned to control a computer cursor by manipulating their right fingers. We discovered a double dissociation of fMRI activity in the anterior and posterior caudate nucleus, which was associated with performance in the early and late learning stages. Moreover, cognitive and sensorimotor cortico-caudate interactions predicted individual learning performance. Our results suggest parallel corticocaudate networks operating in different stages of human de novo motor skill learning11sciescopu

Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness

The early prediction of diabetes can facilitate interventions to prevent or delay it. This study proposes a diabetes prediction model based on machine learning (ML) to encourage individuals at risk of diabetes to employ healthy interventions. A total of 38,379 subjects were included. We trained the model on 80% of the subjects and verified its predictive performance on the remaining 20%. Furthermore, the performances of several algorithms were compared, including logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), Cox regression, and XGBoost Survival Embedding (XGBSE). The area under the receiver operating characteristic curve (AUROC) of the XGBoost model was the largest, followed by those of the decision tree, logistic regression, and random forest models. For the survival analysis, XGBSE yielded an AUROC exceeding 0.9 for the 2- to 9-year predictions and a C-index of 0.934, while the Cox regression achieved a C-index of 0.921. After lowering the threshold from 0.5 to 0.25, the sensitivity increased from 0.011 to 0.236 for the 2-year prediction model and from 0.607 to 0.994 for the 9-year prediction model, while the specificity showed negligible changes. We developed a high-performance diabetes prediction model that applied the XGBSE algorithm with threshold adjustment. We plan to use this prediction model in real clinical practice for diabetes prevention after simplifying and validating it externally

Prediction of Cardiovascular Complication in Patients with Newly Diagnosed Type 2 Diabetes Using an XGBoost/GRU-ODE-Bayes-Based Machine-Learning Algorithm

Background Cardiovascular disease is life-threatening yet preventable for patients with type 2 diabetes mellitus (T2DM). Because each patient with T2DM has a different risk of developing cardiovascular complications, the accurate stratification of cardiovascular risk is critical. In this study, we proposed cardiovascular risk engines based on machine-learning algorithms for newly diagnosed T2DM patients in Korea. Methods To develop the machine-learning-based cardiovascular disease engines, we retrospectively analyzed 26,166 newly diagnosed T2DM patients who visited Seoul St. Mary’s Hospital between July 2009 and April 2019. To accurately measure diabetes-related cardiovascular events, we designed a buffer (1 year), an observation (1 year), and an outcome period (5 years). The entire dataset was split into training and testing sets in an 8:2 ratio, and this procedure was repeated 100 times. The area under the receiver operating characteristic curve (AUROC) was calculated by 10-fold cross-validation on the training dataset. Results The machine-learning-based risk engines (AUROC XGBoost=0.781±0.014 and AUROC gated recurrent unit [GRU]-ordinary differential equation [ODE]-Bayes=0.812±0.016) outperformed the conventional regression-based model (AUROC=0.723± 0.036). Conclusion GRU-ODE-Bayes-based cardiovascular risk engine is highly accurate, easily applicable, and can provide valuable information for the individualized treatment of Korean patients with newly diagnosed T2DM

Neurocognitive Changes and Their Neural Correlates in Patients with Type 2 Diabetes Mellitus

As the prevalence and life expectancy of type 2 diabetes mellitus (T2DM) continue to increase, the importance of effective detection and intervention for the complications of T2DM, especially neurocognitive complications including cognitive dysfunction and dementia, is receiving greater attention. T2DM is thought to influence cognitive function through an as yet unclear mechanism that involves multiple factors such as hyperglycemia, hypoglycemia, and vascular disease. Recent developments in neuroimaging methods have led to the identification of potential neural correlates of T2DM-related neurocognitive changes, which extend from structural to functional and metabolite alterations in the brain. The evidence indicates various changes in the T2DM brain, including global and regional atrophy, white matter hyperintensity, altered functional connectivity, and changes in neurometabolite levels. Continued neuroimaging research is expected to further elucidate the underpinnings of cognitive decline in T2DM and allow better diagnosis and treatment of the condition