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., vision loss). Fortunately, the widely available electronic health record (EHR) databases provide an unprecedented opportunity to develop cost-effective machine-learning tools for DR detection. This paper proposes a Multi-branching Temporal Convolutional Network with Tensor Data Completion (MB-TCN-TC) model to analyze the longitudinal EHRs collected from diabetic patients for DR prediction. Experimental results demonstrate that the proposed MB-TCN-TC model not only effectively copes with the imbalanced data and missing value issues commonly seen in EHR datasets but also captures the temporal correlation and complicated interactions among medical variables in the longitudinal clinical records, yielding superior prediction performance compared to existing methods. Specifically, our MB-TCN-TC model provides AUROC and AUPRC scores of 0.949 and 0.793 respectively, achieving an improvement of 6.27% on AUROC, 11.85% on AUPRC, and 19.3% on F1 score compared with the traditional TCN model.

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 phenomenon leads to a so-called positive-unlabelled situation and the labels are extremely imbalanced. In this paper, we propose a model-based approach to classify the unlabelled patients by using a Bayesian finite mixture model. We also discuss the label switching issue for the imbalanced data and propose a consensus Monte Carlo approach to address the imbalance issue and improve computational efficiency simultaneously. Simulation studies show that our proposed model-based approach outperforms existing positive-unlabelled learning algorithms. The proposed method is applied on the Cerner EHR for detecting diabetic retinopathy (DR) patients using laboratory measurements. With only 3% confirmatory diagnoses in the EHR database, we estimate the actual DR prevalence to be 25% which coincides with reported findings in the medical literature.

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. The expansion distance means keeping an extra space from obstacles to improve path reliability by avoiding collisions. Bidirectional search is a strategy searching path from the start node and the goal node simultaneously. Smoothing improves path robustness by reducing the number of right-angle turns. In addition, simulation tests for the EBS-A* algorithm are performed, and the effectiveness of the proposed algorithm is verified by transferring it to a robot operating system (ROS). The experimental results show that compared with the traditional A* algorithm, the proposed algorithm improves the path planning efficiency by 278% and reduces the number of critical nodes by 91.89% and the number of right-angle turns by 100%.

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. We conducted a retrospective analysis on a derivation cohort with 3749 DR and 94,127 non-DR diabetic patients. In the analysis, an ensemble predictor selection method was employed to find essential predictors among 26 variables in demographics, duration of diabetes, complications and laboratory results. A predictive model and a risk index were built based on the selected, essential predictors, and then validated using another independent validation cohort with 869 DR and 6448 non-DR diabetic patients. Out of the 26 variables, 10 were identified to be essential for predicting DR. The predictive model achieved a 0.85 AUC on the derivation cohort and a 0.77 AUC on the validation cohort. For the risk index, the AUCs were 0.81 and 0.73 on the derivation and validation cohorts, respectively. The predictive technologies can provide an early warning sign that motivates patients to comply with eye examinations for early screening and potential treatments.

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. METHODS: A model of gastritis was created by ethanol in C57BL/6 mice. Enzyme-linked immunosorbent assay was used to determine the concentration of TNF-α, IL-1ß, IL-8, and IL-10. RESULTS: We found that GPS treatment significantly ameliorated ethanol-induced gastritis in mice, with lower production of pro-inflammatory cytokine TNF-α, IL-1ß, and IL-8 and higher levels of anti-inflammatory cytokine IL-10. The anti-inflammatory effect of GPS was further confirmed in vitro in ethanol-treated human gastric mucosal GES cells. Mechanistically, we demonstrated that GPS regulated matrix metallopeptidase expression and pERK1/2 signaling. Knockdown of matrix metallopeptidase 10 (MMP-10) greatly improved cell survival and suppressed inflammatory response in ethanol-treated GES cells. Moreover, inhibition of pERK1/2 signaling using U0126 decreased the expression of MMP-10 in ethanol-induced gastritis. U0126 treatment also suppressed the expression of TNF-α, IL-1ß, and IL-8, and enhanced IL-10 expression in mice gastric mucosa. CONCLUSIONS: Taken together, our findings suggest that GPS ameliorates ethanol-induced gastritis via regulating MMP-10 and pERK1/2 signaling, which might provide a promising therapeutic drug for ethanol-induced gastritis.

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. MATERIALS AND METHODS: Sulfasalazine and JLC were administrated orally and initialized 6 h after TNBS enema, once a day for seven consecutive days. The effect of JLC on intestinal microbial populations and LPS/TLR-4/NF-κB pathway was observed and assessed. Thirty female SD rats were distributed into six groups randomly and equally, namely, control, TNBS, TNBS + sulfasalazine (625 mg/kg), and TNBS + three different doses of JLC (25, 50, and 100 mg/kg) groups. RESULTS: The effect of JLC on restoring normal structures of colorectum and repairing colonic damage were superior to that of sulfasalazine. JLC showed a positive effect in re-balancing intestinal bacteria population of colitis, and suppressed the activation of LPS/TLR-4/NF-κB pathway. CONCLUSION: The results suggest that JLC demonstrated a beneficial effect on treating colitis in a rat model. The possible mechanisms may be through the regulatory effect of intestinal commensal bacteria and down-regulation of LPS/TLR-4/NF-κB pathway.

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. The proposed method employs hidden Markov models (temporal technique) that take account of both the previous state of patient's health and the current value of clinical signs. Following the Sepsis-3 definition, we selected 3898 encounters of patients with suspected infection to compare the performance of temporal and non-temporal methods (Decision Tree (DT), Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM)). The area under receiver operating characteristics (AUROC) curve, sensitivity, specificity and G-mean were used as performance measures. On the selected data, the AUROC of the proposed temporal framework (0.87) is 9-12% greater than the nontemporal methods (DT: 0.78, NB: 0.79, SVM: 0.79, LR: 0.80 and RF: 0.80). The results also show that our model (G-mean=0.78) provides a better balance between sensitivity and specificity compared to clinically acceptable bed-side criteria (G-mean=0.71). The proposed framework leverages the longitudinal data available in EHR and performs better than the non-temporal methods. The proposed method facilitates information related to the time of change of the patient's health that may help practitioners to plan early and develop effective treatment strategies.

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. The key feature of our approach is that we captured the dynamics among biomarkers. With an area under receiver operating characteristic of 0.84, the proposed model outperformed the competing diagnostic criteria (systemic inflammatory response syndrome = 0.59, quick sepsis-related organ failure assessment = 0.65, modified early warning system = 0.75, sepsis-related organ failure assessment = 0.80). The richness of our proposed model is measured not only by achieving high accuracy, but also by utilizing fewer biomarkers. We also propose a left-center-right imputation method suitable for electronic medical record data. This method uses the individual patient's visit, instead of aggregated (mean or median) value, to impute the missing data.

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. Using odds ratio (OR) and modeling methods (decision tree [DT], multivariate logistic regression [LR], and naïve Bayes [NB]), the relationships between diagnostic criteria and mortality were examined. Results: Of 132,704 eligible patient visits, 14% died within 28 days of ED admission. The association of qSOFA ≥2 with mortality (OR = 3.06; 95% confidence interval [CI], 2.96-3.17) greater than the association of SIRS ≥2 with mortality (OR = 1.22; 95% CI, 1.18-1.26). The area under the ROC curve for qSOFA (AUROC = 0.70) was significantly greater than for SIRS (AUROC = 0.63). For qSOFA, the sensitivity and specificity were DT = 0.39, LR = 0.64, NB = 0.62 and DT = 0.82, [corrected] LR = 0.63, NB = 0.66, respectively. For SIRS, the sensitivity and specificity were DT = 0.46, LR = 0.62, NB = 0.62 and DT = 0.70, LR = 0.59, NB = 0.58, respectively. Conclusions: The evidences suggest that qSOFA is a better diagnostic criteria than SIRS. The low sensitivity of qSOFA can be improved by carefully selecting the threshold to translate the predicted probabilities into labels. These findings can guide healthcare providers in selecting risk-stratification measures for patients presenting to an ED with sepsis.