Estimating the prevalence of diabetic retinopathy in electronic health records with massive missing labels.

Objective: The paper aims to address the problem of massive unlabeled patients in electronic health records (EHR) who potentially have undiagnosed diabetic retinopathy (DR). It is desired to estimate the actual DR prevalence in EHR with 96 % missing labels.

Materials And Methods: The Cerner Health Facts data are used in the study, with 3749 labeled DR patients and 97,876 unlabeled diabetic patients.

Multi-Branching Temporal Convolutional Network With Tensor Data Completion for Diabetic Retinopathy Prediction.

Diabetic retinopathy (DR), a microvascular complication of diabetes, is the leading cause of vision loss among working-aged adults. However, due to the low compliance rate of DR screening and expensive medical devices for ophthalmic exams, many DR patients did not seek proper medical attention until DR develops to irreversible stages (i.e.

Pingxiao capsule inhibits lung metastasis of triple-negative breast cancer and sensitizes breast cancer to radiotherapy.

Objectives: To investigate the anticancer effect of Pingxiao capsule (, PXC) on the treatment of breast cancer and .

Methods: The inhibition of PXC on cell viability and proliferation was determined by cell counting kit-8, EdU assay and colony formation assay, respectively. The effect of PXC on cell apoptosis was detected by using flow cytometry.

BAYESIAN ANALYSIS FOR IMBALANCED POSITIVE-UNLABELLED DIAGNOSIS CODES IN ELECTRONIC HEALTH RECORDS.

With the increasing availability of electronic health records (EHR), significant progress has been made on developing predictive inference and algorithms by health data analysts and researchers. However, the EHR data are notoriously noisy due to missing and inaccurate inputs despite the information is abundant. One serious problem is that only a small portion of patients in the database has confirmatory diagnoses while many other patients remain undiagnosed because they did not comply with the recommended examinations.

The EBS-A* algorithm: An improved A* algorithm for path planning.

Path planning plays an essential role in mobile robot navigation, and the A* algorithm is one of the best-known path planning algorithms. However, the traditional A* algorithm has some limitations, such as slow planning speed, close to obstacles. In this paper, we propose an improved A*-based algorithm, called the EBS-A* algorithm, that introduces expansion distance, bidirectional search, and smoothing into path planning.

Derivation and Validation of Essential Predictors and Risk Index for Early Detection of Diabetic Retinopathy Using Electronic Health Records.

Diabetic retinopathy (DR) is a leading cause for blindness among working-aged adults. The growing prevalence of diabetes urges for cost-effective tools to improve the compliance of eye examinations for early detection of DR. The objective of this research is to identify essential predictors and develop predictive technologies for DR using electronic health records.

Jiaolong capsule protects SD rats against 2,4,6-trinitrobenzene sulfonic acid induced colitis.

Ethnopharmacological Relevance: Jiaolong capsule (JLC) was approved for the therapy of gastrointestinal diseases by the State Food and Drug Administration (SFDA) of China. It has a satisfactory curative effect in the treatment of patients with inflammatory bowel disease, however, the mechanism remains to be elucidated.

Aim Of The Study: In current study, the effects and possible mechanisms of JLC on 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced colitis were investigated.

Gentiopicroside ameliorates ethanol-induced gastritis via regulating MMP-10 and pERK1/2 signaling.

Background: Excessive ethanol consumption results in gastric mucosa damage, which could further develop into chronic gastritis, peptic ulcer, and gastric cancer in humans. Gentiopicroside (GPS), a major active component of Gentianae Macrophyllae radix, was reported to play a critical role in anti-inflammation. In the study, we aimed to investigate the functional role and underlying mechanism of GPS in ethanol-induced gastritis.

Utilizing time series data embedded in electronic health records to develop continuous mortality risk prediction models using hidden Markov models: A sepsis case study.

Continuous mortality risk monitoring is instrumental to manage a patient's care and to efficiently utilize the limited hospital resources. Due to incompleteness and irregularities of electronic health records (EHR), developing continuous mortality risk prediction using EHR data is a challenge. In this study, we propose a framework to continuously monitor mortality risk, and apply it to the real-world EHR data.

Clinical decision support system to assess the risk of sepsis using Tree Augmented Bayesian networks and electronic medical record data.

Early and accurate diagnoses of sepsis enable practitioners to take timely preventive actions. The existing diagnostic criteria suffer from deficiencies, such as triggering false alarms or leaving conditions undiagnosed. This study aims to develop a clinical decision support system to predict the risk of sepsis using tree augmented naive Bayesian network by identifying the optimal set of biomarkers.

Correction: Using Statistical and Machine Learning Methods to Evaluate the Prognostic Accuracy of SIRS and qSOFA.

[This corrects the article on p. 139 in vol. 24, PMID: 29770247.

Using Statistical and Machine Learning Methods to Evaluate the Prognostic Accuracy of SIRS and qSOFA.

Objectives: The objective of this study was to compare the performance of two popularly used early sepsis diagnostic criteria, systemic inflammatory response syndrome (SIRS) and quick Sepsis-related Organ Failure Assessment (qSOFA), using statistical and machine learning approaches.

Methods: This retrospective study examined patient visits in Emergency Department (ED) with sepsis related diagnosis. The outcome was 28-day in-hospital mortality.