scholarly journals A2A: a platform for research in biomedical literature search

2020 ◽  
Vol 21 (S19) ◽  
Author(s):  
Maciej Rybinski ◽  
Sarvnaz Karimi ◽  
Vincent Nguyen ◽  
Cecile Paris
Keyword(s):  
Information Retrieval ◽  
Precision Medicine ◽  
Relevant Literature ◽  
Clinical Decision ◽  
Specific Weight ◽  
Biomedical Literature ◽  
Research Activities ◽  
Biomedical Information Retrieval ◽  
Computer Scientists ◽  
Public Datasets

Abstract Background Finding relevant literature is crucial for many biomedical research activities and in the practice of evidence-based medicine. Search engines such as PubMed provide a means to search and retrieve published literature, given a query. However, they are limited in how users can control the processing of queries and articles—or as we call them documents—by the search engine. To give this control to both biomedical researchers and computer scientists working in biomedical information retrieval, we introduce a public online tool for searching over biomedical literature. Our setup is guided by the NIST setup of the relevant TREC evaluation tasks in genomics, clinical decision support, and precision medicine. Results To provide benchmark results for some of the most common biomedical information retrieval strategies, such as querying MeSH subject headings with a specific weight or querying over the title of the articles only, we present our evaluations on public datasets. Our experiments report well-known information retrieval metrics such as precision at a cutoff of ranked documents. Conclusions We introduce the search and benchmarking tool which is publicly available for the researchers who want to explore different search strategies over published biomedical literature. We outline several query formulation strategies and present their evaluations with known human judgements for a large pool of topics, from genomics to precision medicine.

scholarly journals Automatic Identification of High Impact Relevant Articles to Support Clinical Decision Making Using Attention-Based Deep Learning

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1364
Author(s):  
Beomjoo Park ◽  
Muhammad Afzal ◽  
Jamil Hussain ◽  
Asim Abbas ◽  
Sungyoung Lee
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
High Impact ◽  
Biomedical Literature ◽  
Classification Model ◽  
Evidence Based ◽  
Proposed Model

To support evidence-based precision medicine and clinical decision-making, we need to identify accurate, appropriate, and clinically relevant studies from voluminous biomedical literature. To address the issue of accurate identification of high impact relevant articles, we propose a novel approach of attention-based deep learning for finding and ranking relevant studies against a topic of interest. For learning the proposed model, we collect data consisting of 240,324 clinical articles from the 2018 Precision Medicine track in Text REtrieval Conference (TREC) to identify and rank relevant documents matched with the user query. We built a BERT (Bidirectional Encoder Representations from Transformers) based classification model to classify high and low impact articles. We contextualized word embedding to create vectors of the documents, and user queries combined with genetic information to find contextual similarity for determining the relevancy score to rank the articles. We compare our proposed model results with existing approaches and obtain a higher accuracy of 95.44% as compared to 94.57% (the next best performer) and get a higher precision by about 14% at P@5 (precision at 5) and about 12% at P@10 (precision at 10). The contextually viable and competitive outcomes of the proposed model confirm the suitability of our proposed model for use in domains like evidence-based precision medicine.

How to Evaluate Clinically Relevant Literature: Five Simple Rules for the Practicing Clinician in Stroke Neurorehabilitation

2020 ◽  
Author(s):  
Jonathan Sanching Tsay ◽  
Carolee Winstein
Keyword(s):  
Clinical Decision Making ◽  
Relevant Literature ◽  
Clinical Decision ◽  
Balance Sheets ◽  
Neural Connections ◽  
Recovery Processes ◽  
Immediate Recovery ◽  
Simple Rules ◽  
Busy Clinician ◽  
New Generation

Neurorehabilitation relies on core principles of neuroplasticity to activate and engage latent neural connections, promote detour circuits, and reverse impairments. Clinical interventions incorporating these principles have been shown to promote recovery while demoting compensation. However, many clinicians struggle to find evidence for these principles in our growing but nascent body of literature. Regulatory bodies and organizational balance sheets further discourage evidence-based, methodical, time-intensive, and efficacious interventions because practical needs often outweigh and dominate clinical decision making. Modern neurorehabilitation practices that result from these pressures favor strategies that encourage compensation over those that promote recovery. With a focus on helping the busy clinician evaluate the rapidly growing literature, we put forth five simple rules that direct clinicians toward intervention studies that value more enduring but slower biological recovery processes over the more alluring practical and immediate “recovery” mantra. Filtering emerging literature through this critical lens has the potential to change practice and lead to more durable long-term outcomes. This perspective is meant to serve a new generation of mechanistically minded clinicians, students, and trainees poised to not only advance our field but to also erect policy changes that promote recovery-based care of stroke survivors.

scholarly journals Precision medicine in human heart modeling

2021 ◽  
Author(s):  
M. Peirlinck ◽  
F. Sahli Costabal ◽  
J. Yao ◽  
J. M. Guccione ◽  
S. Tripathy ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Treatment Planning ◽  
Human Heart ◽  
Cardiovascular Health ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Ventricular Assist Devices ◽  
Device Design ◽  
Heart Modeling ◽  
Medical Histories

AbstractPrecision medicine is a new frontier in healthcare that uses scientific methods to customize medical treatment to the individual genes, anatomy, physiology, and lifestyle of each person. In cardiovascular health, precision medicine has emerged as a promising paradigm to enable cost-effective solutions that improve quality of life and reduce mortality rates. However, the exact role in precision medicine for human heart modeling has not yet been fully explored. Here, we discuss the challenges and opportunities for personalized human heart simulations, from diagnosis to device design, treatment planning, and prognosis. With a view toward personalization, we map out the history of anatomic, physical, and constitutive human heart models throughout the past three decades. We illustrate recent human heart modeling in electrophysiology, cardiac mechanics, and fluid dynamics and highlight clinically relevant applications of these models for drug development, pacing lead failure, heart failure, ventricular assist devices, edge-to-edge repair, and annuloplasty. With a view toward translational medicine, we provide a clinical perspective on virtual imaging trials and a regulatory perspective on medical device innovation. We show that precision medicine in human heart modeling does not necessarily require a fully personalized, high-resolution whole heart model with an entire personalized medical history. Instead, we advocate for creating personalized models out of population-based libraries with geometric, biological, physical, and clinical information by morphing between clinical data and medical histories from cohorts of patients using machine learning. We anticipate that this perspective will shape the path toward introducing human heart simulations into precision medicine with the ultimate goals to facilitate clinical decision making, guide treatment planning, and accelerate device design.

scholarly journals Strategies to Integrate Genomic Medicine into Clinical Care: Evidence from the IGNITE Network

2021 ◽  
Vol 11 (7) ◽  
pp. 647
Author(s):  
Nina R. Sperber ◽  
Olivia M. Dong ◽  
Megan C. Roberts ◽  
Paul Dexter ◽  
Amanda R. Elsey ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Program Implementation ◽  
Clinical Care ◽  
Genomic Medicine ◽  
Implementation Strategies ◽  
Clinical Decision ◽  
Implementing Change ◽  
Implementation Outcomes ◽  
Planning Framework

The complexity of genomic medicine can be streamlined by implementing some form of clinical decision support (CDS) to guide clinicians in how to use and interpret personalized data; however, it is not yet clear which strategies are best suited for this purpose. In this study, we used implementation science to identify common strategies for applying provider-based CDS interventions across six genomic medicine clinical research projects funded by an NIH consortium. Each project’s strategies were elicited via a structured survey derived from a typology of implementation strategies, the Expert Recommendations for Implementing Change (ERIC), and follow-up interviews guided by both implementation strategy reporting criteria and a planning framework, RE-AIM, to obtain more detail about implementation strategies and desired outcomes. We found that, on average, the three pharmacogenomics implementation projects used more strategies than the disease-focused projects. Overall, projects had four implementation strategies in common; however, operationalization of each differed in accordance with each study’s implementation outcomes. These four common strategies may be important for precision medicine program implementation, and pharmacogenomics may require more integration into clinical care. Understanding how and why these strategies were successfully employed could be useful for others implementing genomic or precision medicine programs in different contexts.

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

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Peter Brown ◽  
Aik-Choon Tan ◽  
Mohamed A El-Esawi ◽  
Thomas Liehr ◽  
Oliver Blanck ◽  
...  
Keyword(s):  
Literature Search ◽  
Relevant Literature ◽  
Biomedical Literature ◽  
Medical Subject Headings ◽  
Document Similarity ◽  
Inverse Document Frequency ◽  
Research Fields ◽  
Experience Levels ◽  
Document Frequency ◽  
Systematic Biases

Abstract 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.

Improve Biomedical Information Retrieval Using Modified Learning to Rank Methods

2018 ◽  
Vol 15 (6) ◽  
pp. 1797-1809 ◽  
Author(s):  
Bo Xu ◽  
Hongfei Lin ◽  
Yuan Lin ◽  
Yunlong Ma ◽  
Liang Yang ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Learning To Rank ◽  
Biomedical Information Retrieval

The Johns Hopkins Molecular Tumor Board Precision Oncology elective for Medical Oncology fellows.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 11035-11035
Author(s):  
Kristen Marrone ◽  
Jessica Tao ◽  
Jenna VanLiere Canzoniero ◽  
Paola Ghanem ◽  
Emily Nizialek ◽  
...  
Keyword(s):  
Decision Making ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Cancer Genomics ◽  
Medical Oncology ◽  
Clinical Decision ◽  
Tumor Board ◽  
Precision Oncology ◽  
Adaptive Expertise ◽  
Molecular Tumor Board

11035 Background: The accelerated impact of next generation sequencing (NGS) in clinical decision making requires the integration of cancer genomics and precision oncology focused training into medical oncology education. The Johns Hopkins Molecular Tumor Board (JH MTB) is a multi-disciplinary effort focused on integration of NGS findings with critical evidence interpretation to generate personalized recommendations tailored to the genetic footprint of individual patients. Methods: The JH MTB and the Medical Oncology Fellowship Program have developed a 3-month precision oncology elective for fellows in their research years. Commencing fall of 2020, the goals of this elective are to enhance the understanding of NGS platforms and findings, advance the interpretation and characterization of molecular assay outputs by use of mutation annotators and knowledgebases and ultimately master the art of matching NGS findings with available therapies. Fellow integration into the MTB focuses on mentored case-based learning in mutation characterization and ranking by levels of evidence for actionability, with culmination in form of verbal presentations and written summary reports of final MTB recommendations. A mixed methods questionnaire was administered to evaluate progress since elective initiation. Results: Three learners who have participated as of February 2021 were included. Of the two who had completed the MTB elective, each have presented at least 10 cases, with at least 1 scholarly publication planned. All indicated strong agreement that MTB elective had increased their comfort with interpreting clinical NGS reports as well as the use of knowledgebases and variant annotators. Exposure to experts in the field of molecular precision oncology, identification of resources necessary to interpret clinical NGS reports, development of ability to critically assess various NGS platforms, and gained familiarity with computational analyses relevant to clinical decision making were noted as strengths of the MTB elective. Areas of improvement included ongoing initiatives that involve streamlining variant annotation and transcription of information for written reports. Conclusions: A longitudinal elective in the JHU MTB has been found to be preliminarily effective in promoting knowledge mastery and creating academic opportunities related to the clinical application of precision medicine. Future directions will include leveraging of the MTB infrastructure for research projects, learner integration into computational laboratory meetings, and expansion of the MTB curriculum to include different levels of learners from multiple medical education programs. Continued elective participation will be key to understanding how best to facilitate adaptive expertise in assigning clinical relevance to genomic findings, ultimately improving precision medicine delivery in patient care and trial development.

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