Predicting low density lipoprotein cholesterol target attainment using machine learning in patients with coronary artery disease receiving moderate-dose statin therapy.

Low-density lipoprotein cholesterol (LDL-C) is an important factor in the development of cardiovascular disease, making its management a key aspect of cardiovascular health. While high-dose statin therapy is often recommended for LDL-C reduction, careful consideration is needed due to patient-specific factors and potential side effects. This study aimed to develop a machine learning (ML) model to estimate the likelihood of achieving target LDL-C levels in patients hospitalized for coronary artery disease and treated with moderate-dose statins.

Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: machine learning model development and evaluation.

Background: Predicting the length of stay in advance will not only benefit the hospitals both clinically and financially but enable healthcare providers to better decision-making for improved quality of care. More importantly, understanding the length of stay of severe patients who require general anesthesia is key to enhancing health outcomes.

Objective: Here, we aim to discover how machine learning can support resource allocation management and decision-making resulting from the length of stay prediction.

Time series forecasting of weight for diuretic dose adjustment using bidirectional long short-term memory.

Loop diuretics are prevailing drugs to manage fluid overload in heart failure. However, adjusting to loop diuretic doses is strenuous due to the lack of a diuretic guideline. Accordingly, we developed a novel clinician decision support system for adjusting loop diuretics dosage with a Long Short-Term Memory (LSTM) algorithm using time-series EMRs.

Astrocytic autophagy plasticity modulates Aβ clearance and cognitive function in Alzheimer's disease.

Background: Astrocytes, one of the most resilient cells in the brain, transform into reactive astrocytes in response to toxic proteins such as amyloid beta (Aβ) in Alzheimer's disease (AD). However, reactive astrocyte-mediated non-cell autonomous neuropathological mechanism is not fully understood yet. We aimed our study to find out whether Aβ-induced proteotoxic stress affects the expression of autophagy genes and the modulation of autophagic flux in astrocytes, and if yes, how Aβ-induced autophagy-associated genes are involved Aβ clearance in astrocytes of animal model of AD.

Forecasting Hospital Room and Ward Occupancy Using Static and Dynamic Information Concurrently: Retrospective Single-Center Cohort Study.

Background: Predicting the bed occupancy rate (BOR) is essential for efficient hospital resource management, long-term budget planning, and patient care planning. Although macro-level BOR prediction for the entire hospital is crucial, predicting occupancy at a detailed level, such as specific wards and rooms, is more practical and useful for hospital scheduling.

Objective: The aim of this study was to develop a web-based support tool that allows hospital administrators to grasp the BOR for each ward and room according to different time periods.

Are polypharmacy side effects predicted by public data still valid in real-world data?

Background And Objective: Although interest in predicting drug-drug interactions is growing, many predictions are not verified by real-world data. This study aimed to confirm whether predicted polypharmacy side effects using public data also occur in data from actual patients.

Methods: We utilized a deep learning-based polypharmacy side effects prediction model to identify cefpodoxime-chlorpheniramine-lung edema combination with a high prediction score and a significant patient population.

Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea.

As warfarin has a narrow therapeutic window and obvious response variability among individuals, it is difficult to rapidly determine personalized warfarin dosage. Adverse drug events(ADE) resulting from warfarin overdose can be critical, so that typically physicians adjust the warfarin dosage through the INR monitoring twice a week when starting warfarin. Our study aimed to develop machine learning (ML) models that predicts the discharge dosage of warfarin as the initial warfarin dosage using clinical data derived from electronic medical records within 2 days of hospitalization.

LDP-GAN : Generative adversarial networks with local differential privacy for patient medical records synthesis.

Electronic medical records(EMR) have considerable potential to advance healthcare technologies, including medical AI. Nevertheless, due to the privacy issues associated with the sharing of patient's personal information, it is difficult to sufficiently utilize them. Generative models based on deep learning can solve this problem by creating synthetic data similar to real patient data.

Heterogeneous graph construction and HinSAGE learning from electronic medical records.

Graph representation learning is a method for introducing how to effectively construct and learn patient embeddings using electronic medical records. Adapting the integration will support and advance the previous methods to predict the prognosis of patients in network models. This study aims to address the challenge of implementing a complex and highly heterogeneous dataset, including the following: (1) demonstrating how to build a multi-attributed and multi-relational graph model (2) and applying a downstream disease prediction task of a patient's prognosis using the HinSAGE algorithm.

Decreased FAK activity and focal adhesion dynamics impair proper neurite formation of medium spiny neurons in Huntington's disease.

Huntington's disease (HD) is a neurodegenerative disorder caused by a polyglutamine expansion in the protein huntingtin (HTT) [55]. While the final pathological consequence of HD is the neuronal cell death in the striatum region of the brain, it is still unclear how mutant HTT (mHTT) causes synaptic dysfunctions at the early stage and during the progression of HD. Here, we discovered that the basal activity of focal adhesion kinase (FAK) is severely reduced in a striatal HD cell line, a mouse model of HD, and the human post-mortem brains of HD patients.

RIDAB: Electronic medical record-integrated real world data platform for predicting and summarizing interactions in biomedical research from heterogeneous data resources.

Background And Objective: With the advent of bioinformatics, biological databases have been constructed to computerize data. Biological systems can be described as interactions and relationships between elements constituting the systems, and they are organized in various biomedical open databases. These open databases have been used in approaches to predict functional interactions such as protein-protein interactions (PPI), drug-drug interactions (DDI) and disease-disease relationships (DDR).

Machine Learning-Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study.

Background: Effective resource management in hospitals can improve the quality of medical services by reducing labor-intensive burdens on staff, decreasing inpatient waiting time, and securing the optimal treatment time. The use of hospital processes requires effective bed management; a stay in the hospital that is longer than the optimal treatment time hinders bed management. Therefore, predicting a patient's hospitalization period may support the making of judicious decisions regarding bed management.

Self-Training With Quantile Errors for Multivariate Missing Data Imputation for Regression Problems in Electronic Medical Records: Algorithm Development Study.

Background: When using machine learning in the real world, the missing value problem is the first problem encountered. Methods to impute this missing value include statistical methods such as mean, expectation-maximization, and multiple imputations by chained equations (MICE) as well as machine learning methods such as multilayer perceptron, k-nearest neighbor, and decision tree.

Objective: The objective of this study was to impute numeric medical data such as physical data and laboratory data.

Modulation of SETDB1 activity by APQ ameliorates heterochromatin condensation, motor function, and neuropathology in a Huntington's disease mouse model.

The present study describes evaluation of epigenetic regulation by a small molecule as the therapeutic potential for treatment of Huntington's disease (HD). We identified 5-allyloxy-2-(pyrrolidin-1-yl)quinoline (APQ) as a novel SETDB1/ESET inhibitor using a combined and cell based screening system. APQ reduced SETDB1 activity and H3K9me3 levels in a HD cell line model.

CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases.

Background: Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and patients manage CVDs more effectively. However, to apply AI approaches to CVDs data, it is necessary to establish and curate a specialized database based on electronic health records (EHRs) and include pre-processed unstructured data.

Development of the phenylpyrazolo[3,4-]pyrimidine-based, insulin-like growth factor receptor/Src/AXL-targeting small molecule kinase inhibitor.

The type I insulin-like growth factor receptor (IGF-1R) signaling pathway plays key roles in the development and progression of numerous types of human cancers, and Src and AXL have been found to confer resistance to anti-IGF-1R therapies. Hence, co-targeting Src and AXL may be an effective strategy to overcome resistance to anti-IGF-1R therapies. However, pharmacologic targeting of these three kinases may result in enhanced toxicity.

Free energy measurements by the generalized fluctuation theorems: Theory and numerical study of a model filament.

We measure the free energy of a model filament, which undergoes deformations and structural transitions, as a function of its extension, in silico. We perform Brownian Dynamics (BD) simulations of pulling experiments at various speeds, following a protocol close to experimental ones. The results from the fluctuation theorems are compared with the estimates from Monte Carlo (MC) simulation, where the rugged free energy landscape is produced by the density of states method.

A Versatile Adeno-Associated Viral Vector Cross-Linking Platform Capable of Tuning Cellular Tropisms and Simultaneously Inducing Solid-Phase Gene Delivery.

Developing gene carriers with improved affinities for target cells and the simultaneous diversification of their delivery modes will be pivotal for upgrading gene therapy technologies. In this study, a simple and versatile adeno-associated virus (AAV) conjugation platform using the cross-linker 3,3'-dithiobis(sulfosuccinimidyl propionate) (DTSSP) is proposed. Depending on the quantity of the DTSSP molecules, the AAV-DTSSP complexes could either be linked with the relevant biomolecules for altering cellular tropisms or further form a self-assembled AAV-DTSSP pellet capable of mimicking a polymeric gene delivery system.