Building longitudinal medication dose data using medication information extracted from clinical notes in electronic health records

ObjectiveTo develop an algorithm for building longitudinal medication dose datasets using information extracted from clinical notes in electronic health records (EHRs).Materials and MethodsWe developed an algorithm that converts medication information extracted using natural language processing (NLP) into a usable format and builds longitudinal medication dose datasets. We evaluated the algorithm on 2 medications extracted from clinical notes of Vanderbilt’s EHR and externally validated the algorithm using clinical notes from the MIMIC-III clinical care database.ResultsFor the evaluation using Vanderbilt’s EHR data, the performance of our algorithm was excellent; F1-measures were ≥0.98 for both dose intake and daily dose. For the external validation using MIMIC-III, the algorithm achieved F1-measures ≥0.85 for dose intake and ≥0.82 for daily dose.DiscussionOur algorithm addresses the challenge of building longitudinal medication dose data using information extracted from clinical notes. Overall performance was excellent, but the algorithm can perform poorly when incorrect information is extracted by NLP systems. Although it performed reasonably well when applied to the external data source, its performance was worse due to differences in the way the drug information was written. The algorithm is implemented in the R package, “EHR,” and the extracted data from Vanderbilt’s EHRs along with the gold standards are provided so that users can reproduce the results and help improve the algorithm.ConclusionOur algorithm for building longitudinal dose data provides a straightforward way to use EHR data for medication-based studies. The external validation results suggest its potential for applicability to other systems.     

Leadership qualities emerging in an online social support group intervention

Technology-delivered interventions addressing a broad range of problems for which clients present for therapy are proliferating. However, little is known of leadership dynamics that emerge in online group interventions. The purpose of this study was to assess the types of leadership qualities that would emerge in an online social support group intervention to improve medication adherence for men with HIV, and to characterize the demographic and psychosocial profiles of leaders. Written posts (n = 616) from 66 men were coded using an adapted version of the Full Range Model of leadership. Results showed that 10% (n = 64) of posts reflected one of five leadership types, the most common of which was mentoring/providing feedback (40% of leadership posts). The next most common leadership style was instances in which encouragement was offered (30% of leadership posts). Leaders appeared to have lived with HIV longer and have higher Internet knowledge scores than non-leaders. Results indicate that online group interventions potentially may be useful to supplement traditional face-to-face treatment by providing an additional venue for group members to mentor and provide emotional support to each other. However, additional research is needed to more fully understand leadership qualities and group dynamics in other online group intervention settings.     

联合干预治疗剖宫产产后出血的临床疗效分析

目的分析宫腔填纱配合米索前列醇局部给药治疗剖宫产产后出血临床疗效。方法选取我院收治的90例剖宫产产后出血患者的资料,其入院时间为2018年1月—2019年6月。按照其入院时间的先后来进行分组—对照组(n=45,常规治疗干预)和观察组(n=45,采取宫腔填纱配合米索前列醇局部给药治疗)。对两组患者不同时间点的出血状况进行统计,且观察其血红蛋白水平治疗前后的变化。结果在两组产后2 h、24 h、48 h出血量上比较,观察组低于对照组,差异具有统计学意义(P<0.05);在治疗前,两组患者的血红蛋白水平差异无统计学意义(P>0.05);在治疗后,观察组优于对照组(P<0.05)。结论宫腔填纱配合米索前列醇局部给药治疗剖宫产产后出血的疗效显著。     

Integrating multiple-domain rules for disease classification

In psychiatry, clinicians use criteria sets from the Diagnostic and Statistical Manual of Mental Disorders to diagnose mental disorders. Most criteria sets have several symptom domains, and in order to be diagnosed, an individual must meet the minimum number of symptoms required by each domain. Some efforts are now focused on adding biomarkers to these symptom domains to facilitate the detection of and highlight the neurobiological basis of psychiatric disorders. Thus, a new criteria set may consist of both clinical symptom counts in several domains and continuous biomarkers. In this paper, we propose a method to integrate classification rules from multiple data sources to estimate an optimal criteria set. Each domain-specific rule can be counts of symptoms, a linear function of symptoms, or even nonparametric. The overall classification rule is the intersection of these domain-specific rules. Based on examining the expected population loss function, we propose two iterative algorithms using either support vector machines or logistic regression to fit intersection rules consistent with the Diagnostic and Statistical Manual of Mental Disorders. In simulation studies, these proposed methods are comparable with the true decision rule. The methods are applied to the motivating study to construct a criteria set for complicated grief. The developed criteria set shows a substantial improvement in sensitivity and specificity compared to the current standards on an independent validation study.