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Development of a Methodological PubMed Search Filter for Finding Studies on Measurement Properties of Measurement Instruments
Objectives For the measurement of patient-reported outcomes, such as (health-related) quality of life, often many measurement instruments exist that intend to measure the same construct. To facilitate instrument selection, our aim was to develop a highly sensitive search filter for finding studies on measurement properties of measurement instruments in PubMed and a more precise search filter that needs less abstracts to be screened, but at a higher risk of missing relevant studies. Methods A random sample of 10,000 PubMed records (01-01-1990 to 31-12-2006) was used as a gold standard. Studies on measurement properties were identified using an exclusion filter and hand searching. Search terms were selected from the relevant records in the gold standard as well as from 100 systematic reviews of measurement properties and combined based on sensitivity and precision. The performance of the filters was tested in the gold standard as well as in two validation sets, by calculating sensitivity, precision, specificity, and number needed to read. Results We identified 116 studies on measurement properties in the gold standard. The sensitive search filter was able to retrieve 113 of these 116 studies (sensitivity 97.4%, precision 4.4%). The precise search filter had a sensitivity of 93.1% and a precision of 9.4%. Both filters performed very well in the validation sets. Conclusion The use of these search filters will contribute to evidence-based selection of measurement instruments in all medical fields.
Journal Article
Dual computer monitors to increase efficiency of conducting systematic reviews
Systematic reviews (SRs) are the cornerstone of evidence-based medicine. In this study, we evaluated the effectiveness of using two computer screens on the efficiency of conducting SRs.
A cohort of reviewers before and after using dual monitors were compared with a control group that did not use dual monitors. The outcomes were time spent for abstract screening, full-text screening and data extraction, and inter-rater agreement. We adopted multivariate difference-in-differences linear regression models.
A total of 60 SRs conducted by 54 reviewers were included in this analysis. We found a significant reduction of 23.81 minutes per article in data extraction in the intervention group relative to the control group (95% confidence interval: −46.03, −1.58, P = 0.04), which was a 36.85% reduction in time. There was no significant difference in time spent on abstract screening, full-text screening, or inter-rater agreement between the two groups.
Using dual monitors when conducting SRs is associated with significant reduction of time spent on data extraction. No significant difference was observed on time spent on abstract screening or full-text screening. Using dual monitors is one strategy that may improve the efficiency of conducting SRs.
Journal Article
Integrative single-cell analysis
The recent maturation of single-cell RNA sequencing (scRNA-seq) technologies has coincided with transformative new methods to profile genetic, epigenetic, spatial, proteomic and lineage information in individual cells. This provides unique opportunities, alongside computational challenges, for integrative methods that can jointly learn across multiple types of data. Integrated analysis can discover relationships across cellular modalities, learn a holistic representation of the cell state, and enable the pooling of data sets produced across individuals and technologies. In this Review, we discuss the recent advances in the collection and integration of different data types at single-cell resolution with a focus on the integration of gene expression data with other types of single-cell measurement.The functional interpretation of single-cell RNA sequencing (scRNA-seq) data can be enhanced by integrating additional data types beyond RNA-based gene expression. In this Review, Stuart and Satija discuss diverse approaches for integrative single-cell analysis, including experimental methods for profiling multiple omics types from the same cells, analytical approaches for extracting additional layers of information directly from scRNA-seq data and computational integration of omics data collected across different cell samples.
Journal Article
A systematic review of data mining and machine learning for air pollution epidemiology
Background
Data measuring airborne pollutants, public health and environmental factors are increasingly being stored and merged. These big datasets offer great potential, but also challenge traditional epidemiological methods. This has motivated the exploration of alternative methods to make predictions, find patterns and extract information. To this end, data mining and machine learning algorithms are increasingly being applied to air pollution epidemiology.
Methods
We conducted a systematic literature review on the application of data mining and machine learning methods in air pollution epidemiology. We carried out our search process in PubMed, the MEDLINE database and Google Scholar. Research articles applying data mining and machine learning methods to air pollution epidemiology were queried and reviewed.
Results
Our search queries resulted in 400 research articles. Our fine-grained analysis employed our inclusion/exclusion criteria to reduce the results to 47 articles, which we separate into three primary areas of interest: 1) source apportionment; 2) forecasting/prediction of air pollution/quality or exposure; and 3) generating hypotheses. Early applications had a preference for artificial neural networks. In more recent work, decision trees, support vector machines, k-means clustering and the APRIORI algorithm have been widely applied. Our survey shows that the majority of the research has been conducted in Europe, China and the USA, and that data mining is becoming an increasingly common tool in environmental health. For potential new directions, we have identified that deep learning and geo-spacial pattern mining are two burgeoning areas of data mining that have good potential for future applications in air pollution epidemiology.
Conclusions
We carried out a systematic review identifying the current trends, challenges and new directions to explore in the application of data mining methods to air pollution epidemiology. This work shows that data mining is increasingly being applied in air pollution epidemiology.
The potential to support air pollution epidemiology continues to grow with advancements in data mining related to temporal and geo-spacial mining, and deep learning. This is further supported by new sensors and storage mediums that enable larger, better quality data. This suggests that many more fruitful applications can be expected in the future.
Journal Article
Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter
Introduction
Prescription medication overdose is the fastest growing drug-related problem in the USA. The growing nature of this problem necessitates the implementation of improved monitoring strategies for investigating the prevalence and patterns of abuse of specific medications.
Objectives
Our primary aims were to assess the possibility of utilizing social media as a resource for automatic monitoring of prescription medication abuse and to devise an automatic classification technique that can identify potentially abuse-indicating user posts.
Methods
We collected Twitter user posts (tweets) associated with three commonly abused medications (Adderall
®
, oxycodone, and quetiapine). We manually annotated 6400 tweets mentioning these three medications and a control medication (metformin) that is not the subject of abuse due to its mechanism of action. We performed quantitative and qualitative analyses of the annotated data to determine whether posts on Twitter contain signals of prescription medication abuse. Finally, we designed an automatic supervised classification technique to distinguish posts containing signals of medication abuse from those that do not and assessed the utility of Twitter in investigating patterns of abuse over time.
Results
Our analyses show that clear signals of medication abuse can be drawn from Twitter posts and the percentage of tweets containing abuse signals are significantly higher for the three case medications (Adderall
®
: 23 %, quetiapine: 5.0 %, oxycodone: 12 %) than the proportion for the control medication (metformin: 0.3 %). Our automatic classification approach achieves 82 % accuracy overall (medication abuse class recall: 0.51, precision: 0.41,
F
measure: 0.46). To illustrate the utility of automatic classification, we show how the classification data can be used to analyze abuse patterns over time.
Conclusion
Our study indicates that social media can be a crucial resource for obtaining abuse-related information for medications, and that automatic approaches involving supervised classification and natural language processing hold promises for essential future monitoring and intervention tasks.
Journal Article