Predictive Modeling with Temporal Graphical Representation on Electronic Health Records.

Deep learning-based predictive models, leveraging Electronic Health Records (EHR), are receiving increasing attention in healthcare. An effective representation of a patient's EHR should hierarchically encompass both the temporal relationships between historical visits and medical events, and the inherent structural information within these elements. Existing patient representation methods can be roughly categorized into sequential representation and graphical representation.

Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis.

Today's AI systems for medical decision support often succeed on benchmark datasets in research papers but fail in real-world deployment. This work focuses on the decision making of sepsis, an acute life-threatening systematic infection that requires an early diagnosis with high uncertainty from the clinician. Our aim is to explore the design requirements for AI systems that can support clinical experts in making better decisions for the early diagnosis of sepsis.

Multimodal risk prediction with physiological signals, medical images and clinical notes.

The broad adoption of electronic health record (EHR) systems brings us a tremendous amount of clinical data and thus provides opportunities to conduct data-based healthcare research to solve various clinical problems in the medical domain. Machine learning and deep learning methods are widely used in the medical informatics and healthcare domain due to their power to mine insights from raw data. When adapting deep learning models for EHR data, it is essential to consider its heterogeneous nature: EHR contains patient records from various sources including medical tests (e.

Stable clinical risk prediction against distribution shift in electronic health records.

The availability of large-scale electronic health record datasets has led to the development of artificial intelligence (AI) methods for clinical risk prediction that help improve patient care. However, existing studies have shown that AI models suffer from severe performance decay after several years of deployment, which might be caused by various temporal dataset shifts. When the shift occurs, we have access to large-scale pre-shift data and small-scale post-shift data that are not enough to train new models in the post-shift environment.

Multimodal Risk Prediction with Physiological Signals, Medical Images and Clinical Notes.

The broad adoption of electronic health records (EHRs) provides great opportunities to conduct healthcare research and solve various clinical problems in medicine. With recent advances and success, methods based on machine learning and deep learning have become increasingly popular in medical informatics. Combining data from multiple modalities may help in predictive tasks.

A fair and interpretable network for clinical risk prediction: a regularized multi-view multi-task learning approach.

In healthcare domain, complication risk profiling which can be seen as multiple clinical risk prediction tasks is challenging due to the complex interaction between heterogeneous clinical entities. With the availability of real-world data, many deep learning methods are proposed for complication risk profiling. However, the existing methods face three open challenges.

Predicting Age-Related Macular Degeneration Progression with Contrastive Attention and Time-Aware LSTM.

Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in developed countries. Identifying patients at high risk of progression to late AMD, the sight-threatening stage, is critical for clinical actions, including medical interventions and timely monitoring. Recently, deep-learning-based models have been developed and achieved superior performance for late AMD prediction.

Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes.

Despite intense efforts in basic and clinical research, an individualized ventilation strategy for critically ill patients remains a major challenge. Recently, dynamic treatment regime (DTR) with reinforcement learning (RL) on electronic health records (EHR) has attracted interest from both the healthcare industry and machine learning research community. However, most learned DTR policies might be biased due to the existence of confounders.

LncRNA Malat-1 From MSCs-Derived Extracellular Vesicles Suppresses Inflammation and Cartilage Degradation in Osteoarthritis.

Extracellular Vesicles (EVs) derived from hMSCs, have the potential to alleviate cartilage damage and inflammation. We aimed to explore the effects of EVs derived from lncRNA malat-1-overexpressing human mesenchymal stem cells (hMSCs) on chondrocytes. hMSCs-derived Extracellular Vesicles (hMSCs-EVs) were identified by transmission electron microscopy and western blot.

Brain Atlas Guided Attention U-Net for White Matter Hyperintensity Segmentation.

White Matter Hyperintensities (WMH) are the most common manifestation of cerebral small vessel disease (cSVD) on the brain MRI. Accurate WMH segmentation algorithms are important to determine cSVD burden and its clinical con-sequences. Most of existing WMH segmentation algorithms require both fluid attenuated inversion recovery (FLAIR) images and T1-weighted images as inputs.

A survival model generalized to regression learning algorithms.

Survival prediction is an important problem that is encountered widely in industry and medicine. Despite the explosion of artificial intelligence technologies, no uniformed method allows the application of any type of regression learning algorithm to a survival prediction problem. Here, we present a statistical modeling method that is generalized to all types of regression learning algorithm, including deep learning.

An interpretable deep-learning model for early prediction of sepsis in the emergency department.

Sepsis is a life-threatening condition with high mortality rates and expensive treatment costs. Early prediction of sepsis improves survival in septic patients. In this paper, we report our top-performing method in the 2019 DII National Data Science Challenge to predict onset of sepsis 4 h before its diagnosis on electronic health records of over 100,000 unique patients in emergency departments.

An interpretable risk prediction model for healthcare with pattern attention.

Background: The availability of massive amount of data enables the possibility of clinical predictive tasks. Deep learning methods have achieved promising performance on the tasks. However, most existing methods suffer from three limitations: (1) There are lots of missing value for real value events, many methods impute the missing value and then train their models based on the imputed values, which may introduce imputation bias.

Study on automatic detection and classification of breast nodule using deep convolutional neural network system.

Backgrounds: Conventional ultrasound manual scanning and artificial diagnosis approaches in breast are considered to be operator-dependence, slight slow and error-prone. In this study, we used Automated Breast Ultrasound (ABUS) machine for the scanning, and deep convolutional neural network (CNN) technology, a kind of Deep Learning (DL) algorithm, for the detection and classification of breast nodules, aiming to achieve the automatic and accurate diagnosis of breast nodules.

Methods: Two hundred and ninety-three lesions from 194 patients with definite pathological diagnosis results (117 benign and 176 malignancy) were recruited as case group.

Combining structured and unstructured data for predictive models: a deep learning approach.

Background: The broad adoption of electronic health records (EHRs) provides great opportunities to conduct health care research and solve various clinical problems in medicine. With recent advances and success, methods based on machine learning and deep learning have become increasingly popular in medical informatics. However, while many research studies utilize temporal structured data on predictive modeling, they typically neglect potentially valuable information in unstructured clinical notes.

Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach.

Background: Deep learning models have attracted significant interest from health care researchers during the last few decades. There have been many studies that apply deep learning to medical applications and achieve promising results. However, there are three limitations to the existing models: (1) most clinicians are unable to interpret the results from the existing models, (2) existing models cannot incorporate complicated medical domain knowledge (eg, a disease causes another disease), and (3) most existing models lack visual exploration and interaction.

[FOXO4 maintains senescence in human umbilical cord mesenchymal stem cells by repressing apoptosis].

The purpose of the present study was to investigate the effects of forkhead box O4 (FOXO4) on the senescence of human umbilical cord-derived mesenchymal stem cells (hUC-MSCs). The hUC-MSCs were induced to senescence by natural passage, and FOXO4 expression was inhibited by lentiviral shRNA transfection. The hallmark of cell senescence was analyzed by β-galactosidase staining, and the cell viability was assayed by CCK-8 method.

Different effects of total flavonoids from Arachniodes exilis on human umbilical cord mesenchymal stem cells in vitro.

Traditional Chinese medicines are used in promotion of fractured bone healing and bone diseases. Some studies reported total flavonoids from plant can be used as an auxiliary source of exogenous.Use different methods to identify and verify effects of total flavonoids from Arachniodes exilis (TFAE) on human umbilical cord mesenchymal stem cells (HUCMSCs) in vitro.

The bone marrow-derived mesenchymal stem cells (BMSCs) alleviate diabetic peripheral neuropathy induced by STZ via activating GSK-3β/β-catenin signaling pathway.

Background: Diabetic peripheral neuropathy, a common complication of diabetic mellitus, has brought a threaten on patients' health. The bone marrow-derived mesenchymal stem cells (BMSCs) were reported to play an important role in diverse diseases. Nevertheless, the specific function of BMSCs in diabetic peripheral neuropathy remained uncharacterized.

Silencing of estrogen receptor β promotes the invasion and migration of osteosarcoma cells through activating Wnt signaling pathway.

Purpose: This study aimed to evaluate the specific roles of estrogen receptor β (ERβ) on the invasion and migration of osteosarcoma (OS) cells and explore the regulatory mechanisms relating with Wnt signaling pathway.

Methods: The expression of ERβ was detected in human OS tissues by quantitative real-time PCR and immunohistochemistry. U2-OS cells were transfected with siRNA-ERβ (si-ERβ) to downregulate ERβ and treated with FH535 to inhibit Wnt signaling.

Overexpression of ERβ inhibits the proliferation through regulating TNG-β signaling pathway in osteosarcoma.

The present study aimed to explore the potential anti-tumor effect of ERβ overexpression and investigate its related mechanism in osteosarcoma. Cell cycle and apoptosis rates were measured by flow cytometry. Cell proliferation and formation of autophagosome were assessed by 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay and dansylcadaverine (MDC) staining assay.

17β-estradiol protects nucleus pulposus cells from serum deprivation-induced apoptosis and regulates expression of MMP-3 and MMP-13 through promotion of autophagy.

Serum deprivation is a likely contributor to intervertebral disc (IVD) degeneration (IVDD).17β-estradiol (E2) have been noted to protect nucleus pulposus cells (NPCs) against apoptosis. Autophagy and apoptosis play a paramount role in maintaining the homeostasis of IVD.

Autophagy Protects Advanced Glycation End Product-Induced Apoptosis and Expression of MMP-3 and MMP-13 in Rat Chondrocytes.

Aging is one of the most prominent risk factors for the pathological progression of osteoarthritis (OA). One feature of age-related changes in OA is advanced glycation end products (AGEs) accumulation in articular cartilage. Autophagy plays a cellular housekeeping role by removing dysfunctional cellular organelles and proteins.

[Effect of Total Ravonoids of Herba Epimedium on BMP-2/RunX2/OSX Signaling Pathway during Osteogenic Differentiation of Bone Marrow Mesenchymal Stem Cells].

Objective: To explore the effect of total flavonoids of Herba Epimedium (FHE) on BMP-2/RunX2/OSX signaling pathway in promoting osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs).

Methods: Passage 3 BMSCs were randomly divided into the control group, the experimental group, and the inhibitor group. BMSCs in the control group were cultured in 0.