Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Deep learning in medical imaging: FMRI big data analysis via convolutional neural networks

This paper aims at implementing novel biomarkers extracted from functional magnetic resonance imaging (fMRI) images taken at resting-state using convolutional neural networks (CNN) to predict relapse in heavy smoker subjects. In this regard, two classes of subjects were studied. The first class contains 19 subjects that took the drug N-acetylcysteine (NAC), and the second class contains 20 subjects that took a placebo. The subjects underwent a double-blind smoking cessation treatment. The resting-state fMRI of the subjects' brains were recorded through 200 snapshots before and after the treatment. The relapse data was assessed after 6 months past the treatment. The data was pre-processed and an undercomplete autoencoder along with various similarity metrics was developed to extract salient features that could differentiate the pre and post treatment images. Finally, the extracted feature matrix was fed into robust classification algorithms to classify the subjects in terms of relapse and non-relapse. The XGBoost algorithm with 0.86 precision and an AUC of 0.92 outperformed the other classification methods in prediction of relapse in subjects

Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification

This paper aims at developing new theory-driven biomarkers by implementing and evaluating novel techniques from resting-state scans that can be used in relapse prediction for nicotine-dependent patients and future treatment efficacy. Two classes of patients were studied. One class took the drug N-acetylcysteine and the other class took a placebo. Then, the patients underwent a double-blind smoking cessation treatment and the resting-state fMRI scans of their brains before and after treatment were recorded. The scientific research goal of this study was to interpret the fMRI connectivity maps based on machine learning algorithms to predict the patient who will relapse and the one who will not. In this regard, the feature matrix was extracted from the image slices of brain employing voxel selection schemes and data reduction algorithms. Then, the feature matrix was fed into the machine learning classifiers including optimized CART decision tree and Naive-Bayes classifier with standard and optimized implementation employing 10-fold cross-validation. Out of all the data reduction techniques and the machine learning algorithms employed, the best accuracy was obtained using the singular value decomposition along with the optimized Naive-Bayes classifier. This gave an accuracy of 93% with sensitivity-specificity of 99% which suggests that the relapse in nicotine-dependent patients can be predicted based on the resting-state fMRI images. The use of these approaches may result in clinical applications in the future

Effectiveness of eHealth interventions in improving medication adherence for patients with chronic obstructive pulmonary disease or asthma: Systematic review

Background: Poor treatment adherence in patients with chronic obstructive pulmonary disease (COPD) or asthma is a global public health concern with severe consequences in terms of patient health and societal costs. A potentially promising tool for addressing poor compliance is eHealth. Objective: This review investigates the effects of eHealth interventions on medication adherence in patients with COPD or asthma. Methods: A systematic literature search was conducted in the databases of Cochrane Library, PsycINFO, PubMed, and Embase for studies with publication dates between January 1, 2000, and October 29, 2020. We selected randomized controlled trials targeting adult patients with COPD or asthma, which evaluated the effectiveness of an eHealth intervention on medication adherence. The risk of bias in the included studies was examined using the Cochrane Collaboration's risk of bias tool. The results were narratively reviewed. Results: In total, six studies focusing on COPD and seven focusing on asthma were analyzed. Interventions were mostly internet-based or telephone-based, and could entail telemonitoring of symptoms and medication adherence, education, counseling, consultations, and self-support modules. Control groups mostly comprised usual care conditions, whereas a small number of studies used a face-to-face intervention or waiting list as the control condition. For COPD, the majority of eHealth interventions were investigated as an add-on to usual care (5/6 studies), whereas for asthma the majority of interventions were investigated as a standalone intervention (5/7 studies). Regarding eHealth interventions targeting medication adherence for COPD, two studies reported nonsignificant effects, one study found a significant effect in comparison to usual care, and three reported mixed results. Of the seven studies that investigated eHealth interventions targeting medication adherence in asthma, three studies found significant effects, two reported nonsignificant effects, and two reported mixed effects. Conclusions: The mixed results on the effectiveness of eHealth interventions in improving treatment adherence for asthma and COPD are presumably related to the type, context, and intensity of the interventions, as well as to differences in the operationalization and measurement of adherence outcomes. Much remains to be learned about the potential of eHealth to optimize treatment adherence in COPD and asthma

The effectiveness of a mobile intervention to reduce young adults’ alcohol consumption to not exceed low-risk drinking guidelines

Background: Young adults’ drinking habits often exceed low-risk drinking guidelines. As young adults show increased access, use, and interest in personalized content related to physical and mental well-being, mobile applications might be a suitable tool to reach this target group. This study investigates the effectiveness of “Boozebuster”, a self-guided mobile application incorporating various therapeutic principles to reduce young adults’ alcohol consumption to not exceeding low-risk drinking guideline levels, compared to an educational website condition. Method: Young adults aged 18–30 wanting to reduce their alcohol consumption entered a two-arm, parallel-group RCT. There were no minimum drinking severity inclusion criteria. Primary outcomes included alcohol consumption quantity and frequency. Secondary outcomes included binge drinking frequency and alcohol-related problem severity. Baseline, 6-week postbaseline, and 3-month post-baseline assessments were analyzed using linear mixed model analyses. Sex, treatment adherence, experienced engagement and motivation to change alcohol use behavior were investigated as moderators. Sub-group analyses contained problem drinkers and binge drinkers. Results: 503 participants were randomized to the intervention or control condition. Results showed no intervention effects on primary or secondary outcomes compared to the control group. Both groups showed within-group reductions on all outcomes. Sub-group analyses in problem drinkers or binge drinkers showed similar results. Motivation to change drinking behavior and experienced engagement with the application significantly moderated the intervention effect regarding the quantity or frequency of alcohol consumption, respectively. Exploratory analyses showed that participants who indicated they wanted to change their drinking patterns during the initial PNF/MI module showed a significantly greater reduction in drinking quantity compared to those who indicated not wanting to change their drinking patterns. Conclusion: The intervention group did not show a greater reduction in alcohol-related outcomes compared to the control group, but both groups showed a similar decrease. Potential explanations include similar effectiveness of both condition due to using a minimal active control in participants predominantly in the action stage of motivation to change. Future research should further explore the effectiveness of using mobile application to reduce young adults’ drinking behavior to not exceed low-risk drinking guideline levels and identify factors that motivate participants to engage with such an intervention

The effectiveness of a mobile intervention to reduce young adults’ alcohol consumption to not exceed low-risk drinking guidelines

Background: Young adults’ drinking habits often exceed low-risk drinking guidelines. As young adults show increased access, use, and interest in personalized content related to physical and mental well-being, mobile applications might be a suitable tool to reach this target group. This study investigates the effectiveness of “Boozebuster”, a self-guided mobile application incorporating various therapeutic principles to reduce young adults’ alcohol consumption to not exceeding low-risk drinking guideline levels, compared to an educational website condition. Method: Young adults aged 18–30 wanting to reduce their alcohol consumption entered a two-arm, parallel-group RCT. There were no minimum drinking severity inclusion criteria. Primary outcomes included alcohol consumption quantity and frequency. Secondary outcomes included binge drinking frequency and alcohol-related problem severity. Baseline, 6-week postbaseline, and 3-month post-baseline assessments were analyzed using linear mixed model analyses. Sex, treatment adherence, experienced engagement and motivation to change alcohol use behavior were investigated as moderators. Sub-group analyses contained problem drinkers and binge drinkers. Results: 503 participants were randomized to the intervention or control condition. Results showed no intervention effects on primary or secondary outcomes compared to the control group. Both groups showed within-group reductions on all outcomes. Sub-group analyses in problem drinkers or binge drinkers showed similar results. Motivation to change drinking behavior and experienced engagement with the application significantly moderated the intervention effect regarding the quantity or frequency of alcohol consumption, respectively. Exploratory analyses showed that participants who indicated they wanted to change their drinking patterns during the initial PNF/MI module showed a significantly greater reduction in drinking quantity compared to those who indicated not wanting to change their drinking patterns. Conclusion: The intervention group did not show a greater reduction in alcohol-related outcomes compared to the control group, but both groups showed a similar decrease. Potential explanations include similar effectiveness of both condition due to using a minimal active control in participants predominantly in the action stage of motivation to change. Future research should further explore the effectiveness of using mobile application to reduce young adults’ drinking behavior to not exceed low-risk drinking guideline levels and identify factors that motivate participants to engage with such an intervention

The effect of N-acetylcysteine and working memory training on cocaine use, craving and inhibition in regular cocaine users: correspondence of lab assessments and Ecological Momentary Assessment

Effective treatment for cocaine use disorder should dampen hypersensitive cue-induced motivational processes and/or strengthen executive control. Using a randomized, double-blind, placebo-controlled intervention, the primary aim of this study was to investigate the effect of N-Acetylcysteine (NAC) and working memory (WM)-training to reduce cocaine use and craving and to improve inhibition assessed in the laboratory and during Ecological Momentary Assessment (EMA). The second aim was to examine correspondence between laboratory and EMA data. Twenty-four of 38 cocaine-using men completed a 25-day intervention with 2400mg/day NAC or placebo and WM-training as well as two lab-visits assessing cocaine use, craving and inhibition (Stop Signal task). Additionally, cocaine use, craving and cognition (Stroop task) were assessed using EMA during treatment, with 26 participants completing 819 assessments. Cocaine problems according to the Drug Use Disorder Identification Test (DUDIT) decreased more after NAC than after placebo, and the proportion of cocaine-positive urines at lab-visit 2 was lower in the NAC group. No NAC effects were found on craving. For cocaine use and craving, results from the lab data were generally similar to EMA results. NAC also showed some effects on cognitive control: improved inhibition assessed with the Stop Signal task in the lab, and decreased classic Stroop performance during EMA. There were no significant effects of number of completed WM-training sessions. Overall this study revealed mixed findings regarding the treatment of cocaine use disorders with NAC and WM-training. The effect of NAC on inhibition should be further investigate

Recovery of neurocognitive functions following sustained abstinence after substance dependence and implications for treatment

BACKGROUND: Substance Use Disorders (SUDs) have been associated with impaired neurocognitive functioning, which may (partly) improve with sustained abstinence. New treatments are emerging, aimed at improving cognitive functions, and being tested. However, no integrated review is available regarding neurocognitive recovery following sustained abstinence. OBJECTIVES: In this review, results from prospective studies on neurocognitive recovery using neuropsychological assessments before and after sustained abstinence from SUDs are summarized and discussed. RESULTS: Thirty-five prospective studies were selected for this review, including twenty-two alcohol, three cannabis, four cocaine, three (meth)amphetamine, and three opioid studies. Results suggest that some cognitive functions (partially) recover after sustained abstinence, and that there are predictors of an unfavorable course such as poly-substance use and number of previous detoxifications. CONCLUSIONS: Prospective studies indicate that sustained abstinence after SUDs generally results in (partial) neurocognitive recovery. However, a final answer regarding full recovery awaits prospective studies with neurocognitive assessments before, during, and after sustained abstinence from SUDs. New interventions that might enhance neurocognitive recovery after abstinence are discussed, including neurocognitive training, medication and neuromodulation