Interpretable disease prediction using heterogeneous patient records with self-attentive fusion encoder

ObjectiveWe propose an interpretable disease prediction model that efficiently fuses multiple types of patient records using a self-attentive fusion encoder. We assessed the model performance in predicting cardiovascular disease events, given the records of a general patient population.Materials and MethodsWe extracted 798111 ses and 67 623 controls from the sample cohort database and nationwide healthcare claims data of South Korea. Among the information provided, our model used the sequential records of medical codes and patient characteristics, such as demographic profiles and the most recent health examination results. These two types of patient records were combined in our self-attentive fusion module, whereas previously dominant methods aggregated them using a simple concatenation. The prediction performance was compared to state-of-the-art recurrent neural network-based approaches and other widely used machine learning approaches.ResultsOur model outperformed all the other compared methods in predicting cardiovascular disease events. It achieved an area under the curve of 0.839, while the other compared methods achieved between 0.74111 d 0.830. Moreover, our model consistently outperformed the other methods in a more challenging setting in which we tested the model’s ability to draw an inference from more nonobvious, diverse factors.DiscussionWe also interpreted the attention weights provided by our model as the relative importance of each time step in the sequence. We showed that our model reveals the informative parts of the patients’ history by measuring the attention weights.ConclusionWe suggest an interpretable disease prediction model that efficiently fuses heterogeneous patient records and demonstrates superior disease prediction performance.     

Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU

ObjectiveGlycemic control is an important component of critical care. We present a data-driven method for predicting intensive care unit (ICU) patient response to glycemic control protocols while accounting for patient heterogeneity and variations in care.Materials and MethodsUsing electronic medical records (EMRs) of 18 961 ICU admissions from the MIMIC-III dataset, including 318 574 blood glucose measurements, we train and validate a gradient boosted tree machine learning (ML) algorithm to forecast patient blood glucose and a 95% prediction interval at 2-hour intervals. The model uses as inputs irregular multivariate time series data relating to recent in-patient medical history and glycemic control, including previous blood glucose, nutrition, and insulin dosing.ResultsOur forecasting model using routinely collected EMRs achieves performance comparable to previous models developed in planned research studies using continuous blood glucose monitoring. Model error, expressed as mean absolute percentage error is 16.5%–16.8%, with Clarke error grid analysis demonstrating that 97% of predictions would be clinically acceptable. The 95% prediction intervals achieve near intended coverage at 93%–94%.DiscussionML algorithms built on observational data sources, such as EMRs, present a promising approach for personalization and automation of glycemic control in critical care. Future research may benefit from applying a combination of methodologies and data sources to develop robust methodologies that account for the variations seen in ICU patients and difficultly in detecting the extremes of observed blood glucose values.ConclusionWe demonstrate that EMRs can be used to train ML algorithms that may be suitable for incorporation into ICU decision support systems.     

PAN-cODE: COVID-19 forecasting using conditional latent ODEs

The coronavirus disease 2019 (COVID-19) pandemic has caused millions of deaths around the world and revealed the need for data-driven models of pandemic spread. Accurate pandemic caseload forecasting allows informed policy decisions on the adoption of non-pharmaceutical interventions (NPIs) to reduce disease transmission. Using COVID-19 as an example, we present Pandemic conditional Ordinary Differential Equation (PAN-cODE), a deep learning method to forecast daily increases in pandemic infections and deaths. By using a deep conditional latent variable model, PAN-cODE can generate alternative caseload trajectories based on alternate adoptions of NPIs, allowing stakeholders to make policy decisions in an informed manner. PAN-cODE also allows caseload estimation for regions that are unseen during model training. We demonstrate that, despite using less detailed data and having fully automated training, PAN-cODE’s performance is comparable to state-of-the-art methods on 4-week-ahead and 6-week-ahead forecasting. Finally, we highlight the ability of PAN-cODE to generate realistic alternative outcome trajectories on select US regions.     

轻型多尺度黑色素瘤目标检测网络模型的建立：基于注意力机制调控

目的 提出一种融入坐标注意力和高效通道注意力机制的深度学习目标检测模型AM-YOLO。方法 运用Mosaic图像增强与MixUp混类增强对图像进行预处理,采用One-Stage结构的目标检测模型YOLOv5s,并对该模型的骨干网络与颈部网络进行改进。在该模型的骨干网络中把空间金字塔的最大池化层替换成二维最大池化层,接着将坐标注意力机制和高效通道注意力机制分别融入到YOLOv5s模型的C3模块与该模型的骨干网络中。将改进后的模型与未改进的YOLOv5s模型,YOLOv3模型,YOLOv3-SPP模型,YOLOv3-tiny模型进行相关算法指标的对比实验。结果 融入了坐标注意力和高效通道注意力机制的AM-YOLO模型能够有效提升对黑色素瘤的识别率,同时也减少了模型权重的大小。AM-YOLO模型在准确率,召回率以及平均精度均值上都要明显优于其他模型,并且对于早期和晚期黑色素瘤的平均精度均值分别达92.8%和87.1%。结论 本文采用的深度学习目标检测算法模型能够应用于黑色素瘤目标的识别中。     

Importance-aware personalized learning for early risk prediction using static and dynamic health data

ObjectiveAccurate risk prediction is important for evaluating early medical treatment effects and improving health care quality. Existing methods are usually designed for dynamic medical data, which require long-term observations. Meanwhile, important personalized static information is ignored due to the underlying uncertainty and unquantifiable ambiguity. It is urgent to develop an early risk prediction method that can adaptively integrate both static and dynamic health data.Materials and MethodsData were from 6367 patients with Peptic Ulcer Bleeding between 2007 and 2016. This article develops a novel End-to-end Importance-Aware Personalized Deep Learning Approach (eiPDLA) to achieve accurate early clinical risk prediction. Specifically, eiPDLA introduces a long short-term memory with temporal attention to learn sequential dependencies from time-stamped records and simultaneously incorporating a residual network with correlation attention to capture their influencing relationship with static medical data. Furthermore, a new multi-residual multi-scale network with the importance-aware mechanism is designed to adaptively fuse the learned multisource features, automatically assigning larger weights to important features while weakening the influence of less important features.ResultsExtensive experimental results on a real-world dataset illustrate that our method significantly outperforms the state-of-the-arts for early risk prediction under various settings (eg, achieving an AUC score of 0.944 at 1 year ahead of risk prediction). Case studies indicate that the achieved prediction results are highly interpretable.ConclusionThese results reflect the importance of combining static and dynamic health data, mining their influencing relationship, and incorporating the importance-aware mechanism to automatically identify important features. The achieved accurate early risk prediction results save precious time for doctors to timely design effective treatments and improve clinical outcomes.     

网络环境下学习行为的实证研究——在线英语写作教学案例分析

以第二军医大学网络英语写作课程学习者作为研究对象,通过对学习者在线学习行为进行实证分析,得出了网络环境下学习行为基本特征的相关量化数据,并结合目前网络教育的一些现状,探讨了在培养和提高远程学习者网络学习行为能力方面的相关问题,为进一步完善网络教育环境及评价体系建设提供参考依据。     

Real-time electronic health record mortality prediction during the COVID-19 pandemic: a prospective cohort study

ObjectiveTo rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon sequential organ failure assessment (SOFA) for decision support for a Crisis Standards of Care team.Materials and MethodsWe developed, verified, and deployed a stacked generalization model to predict mortality using data available in the electronic health record (EHR) by combining 5 previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We verified the model with prospectively collected data from 12 hospitals in Colorado between March 2020 and July 2020. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index.ResultsThe prospective cohort included 27 296 encounters, of which 1358 (5.0%) were positive for SARS-CoV-2, 4494 (16.5%) required intensive care unit care, 1480 (5.4%) required mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94. In the subset of patients with COVID-19, the stacked model predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85.DiscussionStacked regression allows a flexible, updatable, live-implementable, ethically defensible predictive analytics tool for decision support that begins with validated models and includes only novel information that improves prediction.ConclusionWe developed and validated an accurate in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model that improved upon SOFA.     

利用随机森林建立输尿管上段结石预后预测模型

目的:采用机器学习方法建立输尿管上段结石患者不同手术方式预后预测模型,为输尿管上段结石的手术选择提供参考。方法:收集2018年1-8月在中国医科大学附属盛京医院泌尿外科接受手术治疗的输尿管上段结石患者的多种临床变量,使用Weka软件根据信息增益率筛选变量,采用SMOTE算法处理数据不平衡问题,利用随机森林方法构建预后模型,并与利用其他3种常见的机器学习算法（NB、SVM、ANNs）得到的模型进行性能比较。结果:结石横面的长径、短径、手术方式等因素在建模中起重要作用,应用随机森林算法构建的输尿管上段结石患者不同手术方式预后预测模型的预测准确率达87.3%,AUC值高达0.902,与其他算法相比效果最佳。结论:基于输尿管上段结石患者的多种临床信息,通过机器学习方法建立的输尿管上段结石预后预测模型能够达到较好效果,在患者术前手术方式的个性化选择上可以为临床医生提供一定的参考。     

癫痫发作预测模型：斯托克韦尔变换的生成对抗与长短时记忆网络半监督方法

目的 提出一种半监督癫痫发作预测模型（ST-WGAN-GP-Bi-LSTM预测模型）,从脑电（EEG）信号的时频分析、无监督特征模型稳定性以及后端分类器设计三个方面提升发作预测性能。方法 对癫痫EEG信号进行斯托克韦尔变换（ST变换）得到时频输入,通过自适应调节分辨率和保留绝对相位,定位癫痫EEG信号的时频成分;当生成数据分布和真实EEG数据分布无重叠时,为了避免JS散度均为常数而导致特征学习失效的问题,采用Wasserstein生成对抗网络作为特征学习模型,以EM距离结合梯度惩罚策略（WGAN-GP）引导的代价函数,约束模型的无监督训练过程,进而生成高阶特征提取器;构建基于双向长短时记忆网络（Bi-LSTM）的时序预测模型,在获取高阶EEG时频特征间时序相关性基础上提升癫痫分类（预测）性能。利用公开数据集CHB-MIT头皮脑电数据集对本文提出的ST-WGAN-GP-Bi-LSTM预测模型进行评估。结果 本文的ST-WGAN-GP-BiLSTM预测模型在AUC、灵敏度和特异性指标上分别达到90.40%,83.62%和86.69%。与现有半监督方法相比,将原有的性能指标分别提升17.77...     

Deep propensity network using a sparse autoencoder for estimation of treatment effects

ObjectiveDrawing causal estimates from observational data is problematic, because datasets often contain underlying bias (eg, discrimination in treatment assignment). To examine causal effects, it is important to evaluate what-if scenarios—the so-called “counterfactuals.” We propose a novel deep learning architecture for propensity score matching and counterfactual prediction—the deep propensity network using a sparse autoencoder (DPN-SA)—to tackle the problems of high dimensionality, nonlinear/nonparallel treatment assignment, and residual confounding when estimating treatment effects.Materials and MethodsWe used 2 randomized prospective datasets, a semisynthetic one with nonlinear/nonparallel treatment selection bias and simulated counterfactual outcomes from the Infant Health and Development Program and a real-world dataset from the LaLonde’s employment training program. We compared different configurations of the DPN-SA against logistic regression and LASSO as well as deep counterfactual networks with propensity dropout (DCN-PD). Models’ performances were assessed in terms of average treatment effects, mean squared error in precision on effect’s heterogeneity, and average treatment effect on the treated, over multiple training/test runs.ResultsThe DPN-SA outperformed logistic regression and LASSO by 36%–63%, and DCN-PD by 6%–10% across all datasets. All deep learning architectures yielded average treatment effects close to the true ones with low variance. Results were also robust to noise-injection and addition of correlated variables. Code is publicly available at https://github.com/Shantanu48114860/DPN-SAz.Discussion and ConclusionDeep sparse autoencoders are particularly suited for treatment effect estimation studies using electronic health records because they can handle high-dimensional covariate sets, large sample sizes, and complex heterogeneity in treatment assignments.