Answering clinical questions with knowledge-based and statistical techniques

The combination of recent developments in question-answering research and the availability of unparalleled resources developed specifically for automatic semantic processing of text in the medical domain provides a unique opportunity to explore complex question answering in the domain of clinical medicine. This article presents a system designed to satisfy the information needs of physicians practicing evidence-based medicine. We have developed a series of knowledge extractors, which employ a combination of knowledge-based and statistical techniques, for automatically identifying clinically relevant aspects of MEDLINE abstracts. These extracted elements serve as the input to an algorithm that scores the relevance of citations with respect to structured representations of information needs, in accordance with the principles of evidencebased medicine. Starting with an initial list of citations retrieved by PubMed, our system can bring relevant abstracts into higher ranking positions, and from these abstracts generate responses that directly answer physicians ’ questions. We describe three separate evaluations: one focused on the accuracy of the knowledge extractors, one conceptualized as a document reranking task, and finally, an evaluation of answers by two physicians. Experiments on a collection of real-world clinical questions show that our approach significantly outperforms the already competitive PubMed baseline. 1

Improving Healthcare Quality with Evidence-Based Medicine

Physicians in recent years felt increasing pressure to work for long hours and to see more patients even though they have few hours in their normal routine hours to dedicate to self and continuing education. Physician usually makes their clinical decisions based on their own practical experience. Although these ways may be time efficient but it might not provide the best care decision when it comes to the patient. In EBM, physicians seeking additional information pose questions that guide research for ideal literature. During a normal single day, a physician can generate as many as 8 clinical questions, finding the most suitable answer which could then alter the physician way of management. Physicians need to practice EBM correctly but lack of time is a major issue as reported in the literature that an average practicing clinician spend less than one hour weekly reading evidence based information. Therefore busy physicians need to learn new technologies that would make practicing EBM more feasible

Towards identifying intervention arms in randomized controlled trials: Extracting coordinating constructions

AbstractBackground: Large numbers of reports of randomized controlled trials (RCTs) are published each year, and it is becoming increasingly difficult for clinicians practicing evidence-based medicine to find answers to clinical questions. The automatic machine extraction of RCT experimental details, including design methodology and outcomes, could help clinicians and reviewers locate relevant studies more rapidly and easily. Aim: This paper investigates how the comparison of interventions is documented in the abstracts of published RCTs. The ultimate goal is to use automated text mining to locate each intervention arm of a trial. This preliminary work aims to identify coordinating constructions, which are prevalent in the expression of intervention comparisons. Methods and results: An analysis of the types of constructs that describe the allocation of intervention arms is conducted, revealing that the compared interventions are predominantly embedded in coordinating constructions. A method is developed for identifying the descriptions of the assignment of treatment arms in clinical trials, using a full sentence parser to locate coordinating constructions and a statistical classifier for labeling positive examples. Predicate-argument structures are used along with other linguistic features with a maximum entropy classifier. An F-score of 0.78 is obtained for labeling relevant coordinating constructions in an independent test set. Conclusions: The intervention arms of a randomized controlled trials can be identified by machine extraction incorporating syntactic features derived from full sentence parsing

PubMed and Beyond: Recent Advances and Best Practices in Biomedical Literature Search

Biomedical research yields a wealth of information, much of which is only accessible through the literature. Consequently, literature search is an essential tool for building on prior knowledge in clinical and biomedical research. Although recent improvements in artificial intelligence have expanded functionality beyond keyword-based search, these advances may be unfamiliar to clinicians and researchers. In response, we present a survey of literature search tools tailored to both general and specific information needs in biomedicine, with the objective of helping readers efficiently fulfill their information needs. We first examine the widely used PubMed search engine, discussing recent improvements and continued challenges. We then describe literature search tools catering to five specific information needs: 1. Identifying high-quality clinical research for evidence-based medicine. 2. Retrieving gene-related information for precision medicine and genomics. 3. Searching by meaning, including natural language questions. 4. Locating related articles with literature recommendation. 5. Mining literature to discover associations between concepts such as diseases and genetic variants. Additionally, we cover practical considerations and best practices for choosing and using these tools. Finally, we provide a perspective on the future of literature search engines, considering recent breakthroughs in large language models such as ChatGPT. In summary, our survey provides a comprehensive view of biomedical literature search functionalities with 36 publicly available tools.Comment: 27 pages, 6 figures, 36 tool

Formative evaluation of a patient-specific clinical knowledge summarization tool

To iteratively design a prototype of a computerized clinical knowledge summarization (CKS) tool aimed at helping clinicians finding answers to their clinical questions; and to conduct a formative assessment of the usability, usefulness, efficiency, and impact of the CKS prototype on physicians’ perceived decision quality compared with standard search of UpToDate and PubMed

"Evidence-based librarianship: opportunity for law librarians?"

28 pages. Susan Nissen Lerdal is a Reference Librarian at the Law Library at Drake University. Her email address is: [email protected] librarians consult empirical research when making day-to-day operational decisions? Ms. Lerdal describes the concepts and methods of evidence-based librarianship, suggesting that this approach may provide opportunities for law librarians to increase their credibility in the eyes of those who fund and use their libraries