Contribution of high risk groups’unmet needs may be underestimated in epidemic models without risk turnover:A mechanistic modelling analysis

Background:Epidemic models of sexually transmitted infections(STIs)are often used to characterize the contribution of risk groups to overall transmission by projecting the transmission population attributable fraction(tPAF)of unmet prevention and treatment needs within risk groups.However,evidence suggests that STI risk is dynamic over an individual’s sexual life course,which manifests as turnover between risk groups.We sought to examine the mechanisms by which turnover influences modelled projections of the tPAF of high risk groups.Methods:We developed a unifying,data-guided framework to simulate risk group turnover in deterministic,compartmental transmission models.We applied the framework to an illustrative model of an STI and examined the mechanisms by which risk group turnover influenced equilibrium prevalence across risk groups.We then fit a model with and without turnover to the same risk-stratified STI prevalence targets and compared the inferred level of risk heterogeneity and tPAF of the highest risk group projected by the two models.Results:The influence of turnover on group-specific prevalence was mediated by three main phenomena:movement of previously high risk individuals with the infection into lower risk groups;changes to herd effect in the highest risk group;and changes in the number of partnerships where transmission can occur.Faster turnover led to a smaller ratio of STI prevalence between the highest and lowest risk groups.Compared to the fitted model without turnover,the fitted model with turnover inferred greater risk heterogeneity and consistently projected a larger tPAF of the highest risk group over time.Implications:If turnover is not captured in epidemic models,the projected contribution of high risk groups,and thus,the potential impact of prioritizing interventions to address their needs,could be underestimated.To aid the next generation of tPAF models,data collection efforts to parameterize risk group turnover should be prioritized.

KTI-RNN:Recognition of Heart Failure from Clinical Notes

Although deep learning methods have recently attracted considerable attention in the medical field,analyzing large-scale electronic health record data is still a difficult task.In particular,the accurate recognition of heart failure is a key technology for doctors to make reasonable treatment decisions.This study uses data from the Medical Information Mart for Intensive Care database.Compared with structured data,unstructured data contain abundant patient information.However,this type of data has unsatisfactory characteristics,e.g.,many colloquial vocabularies and sparse content.To solve these problems,we propose the KTI-RNN model for unstructured data recognition.The proposed model overcomes sparse content and obtains good classification results.The term frequency-inverse word frequency(TF-IWF)model is used to extract the keyword set.The latent dirichlet allocation(LDA)model is adopted to extract the topic word set.These models enable the expansion of the medical record text content.Finally,we embed the global attention mechanism and gating mechanism between the bidirectional recurrent neural network(BiRNN)model and the output layer.We call it gated-attention-BiRNN(GA-BiRNN)and use it to identify heart failure from extensive medical texts.Results show that the F 1 score of the proposed KTI-RNN model is 85.57%,and the accuracy rate of the proposed KTI-RNN model is 85.59%.