scholarly journals Generating Clinical Queries from Patient Narratives

Author(s):  
Bevan Koopman ◽  
Liam Cripwell ◽  
Guido Zuccon
Author(s):  
Elin M. Aasen ◽  
Halvard K. Nilsen ◽  
Elisabeth Dahlborg ◽  
Lindis K. Helberget ◽  
Marianne Kjelsvik

Author(s):  
Roxana Damiescu ◽  
Mita Banerjee ◽  
David Y. W. Lee ◽  
Norbert W. Paul ◽  
Thomas Efferth

Opioid abuse and misuse have led to an epidemic which is currently spreading worldwide. Since the number of opioid overdoses is still increasing, it is becoming obvious that current rather unsystematic approaches to tackle this health problem are not effective. This review suggests that fighting the opioid epidemic requires a structured public health approach. Therefore, it is important to consider not only scientific and biomedical perspectives, but societal implications and the lived experience of groups at risk as well. Hence, this review evaluates the risk factors associated with opioid overdoses and investigates the rates of chronic opioid misuse, particularly in the context of chronic pain as well as post-surgery treatments, as the entrance of opioids in people’s lives. Linking pharmaceutical biology to narrative analysis is essential to understand the modulations of the usual themes of addiction and abuse present in the opioid crisis. This paper shows that patient narratives can be an important resource in understanding the complexity of opioid abuse and addiction. In particular, the relationship between chronic pain and social inequality must be considered. The main goal of this review is to demonstrate how a deeper transdisciplinary-enriched understanding can lead to more precise strategies of prevention or treatment of opioid abuse.


2015 ◽  
Vol 25 (9) ◽  
pp. 1241-1250 ◽  
Author(s):  
Mary Adams ◽  
Glenn Robert ◽  
Jill Maben
Keyword(s):  

Author(s):  
Susan M. Bradley

Introduction – This investigation sought to determine whether the methodological search filters in place as Clinical Queries limits in OvidSP EMBASE and OvidSP MEDLINE had been modified from those written by Haynes et al. and whether the translations of these in PubMed and EBSCO MEDLINE were reliable. The translated National Library of Medicine (NLM) Systematic Reviews hedges in place in OvidSP MEDLINE and EBSCO MEDLINE were also examined. Methods – Search queries were run using the Clinical Queries and Systematic Reviews hedges incorporated into OvidSP EMBASE, OvidSP MEDLINE, PubMed, and EBSCO MEDLINE to determine the reliability of these limits in comparison with the published hedge search strings. Results – Five of the OvidSP EMBASE Clinical Queries hedges produced results that were different from the published search strings. Three of the EBSCO MEDLINE and five of the PubMed translated Clinical Queries hedges yielded markedly different results (>10% difference) than those obtained using the OvidSP MEDLINE hedge counterparts. The OvidSP MEDLINE Systematic Reviews subject subset hedge was found to have a major error, which has been corrected. Discussion – Translations of hedges to appropriate syntax for other database platforms may result in significantly different search results. The platform searched should ideally be the one for which the hedges were written and tested. Regardless, the hedges in place may not be the same as the published hedge search strings. Quality control testing is needed to ensure that the hedges in place as limits are the same as those that have been published.


2020 ◽  
Author(s):  
Iain J Marshall ◽  
Benjamin Nye ◽  
Joël Kuiper ◽  
Anna Noel-Storr ◽  
Rachel Marshall ◽  
...  

Objective Randomized controlled trials (RCTs) are the gold standard method for evaluating whether a treatment works in healthcare, but can be difficult to find and make use of. We describe the development and evaluation of a system to automatically find and categorize all new RCT reports. Materials and Methods Trialstreamer, continuously monitors PubMed and the WHO International Clinical Trials Registry Platform (ICTRP), looking for new RCTs in humans using a validated classifier. We combine machine learning and rule-based methods to extract information from the RCT abstracts, including free-text descriptions of trial populations, interventions and outcomes (the 'PICO') and map these snippets to normalised MeSH vocabulary terms. We additionally identify sample sizes, predict the risk of bias, and extract text conveying key findings. We store all extracted data in a database which we make freely available for download, and via a search portal, which allows users to enter structured clinical queries. Results are ranked automatically to prioritize larger and higher-quality studies. Results As of May 2020, we have indexed 669,895 publications of RCTs, of which 18,485 were published in the first four months of 2020 (144/day). We additionally include 303,319 trial registrations from ICTRP. The median trial sample size in the RCTs was 66. Conclusions We present an automated system for finding and categorising RCTs. This yields a novel resource: A database of structured information automatically extracted for all published RCTs in humans. We make daily updates of this database available on our website (trialstreamer.robotreviewer.net).


2021 ◽  
Author(s):  
Daisy Massey ◽  
Anna D Baker ◽  
Diana Zicklin Berrent ◽  
Nick Güthe ◽  
Suzanne Pincus Shidlovsky ◽  
...  

AbstractTo introduce the perspective of patients who have PASC with vibrations and tremors as a prominent component, we leveraged the efforts by Survivor Corps, a grassroots COVID-19 patient advocacy group, to gather information from people in their Facebook group suffering from vibrations and tremors. Survivor Corps collected 140 emails and 450 Facebook comments from members. From the emails, we identified 22 themes and 7 broader domains based on common coding techniques for qualitative data and the constant comparative method of qualitative data analysis. Facebook comments were analyzed using Word Clouds to visualize frequency of terms. The respondents’ emails reflected 7 domains that formed the basis of characterizing their experience with vibrations and tremors. These domains were: (1) symptom experience, description, and anatomic location; (2) initial symptom onset; (3) symptom timing; (4) symptom triggers or alleviators; (5) change from baseline health status; (6) experience with medical establishment; and (7) impact on people’s lives and livelihood. There were 22 themes total, each corresponding to one of the broader domains. The Facebook comments Word Cloud revealed that the 10 most common words used in comments were: tremors (64), covid (55), pain (51), vibrations (43), months (36), burning (29), feet (24), hands (22), legs (21), back (20). Overall, these patient narratives described intense suffering, and there is still no diagnosis or treatment available.


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