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Clinical Decision Support Track Overview   DA: 13 PA: 50 MOZ Rank: 63

Text REtrieval Conference (TREC) CDS Track Task • 30 topics • case narratives plus label designating which basic clinical task the topic pertains to • developed by physicians at NIH • 10 topics for each clinical task type • each topic statement includes both a “description” of the problem and a shorter, more focused

PubMed Central (TREC CDS)   DA: 15 PA: 9 MOZ Rank: 25

Bio-medical articles from PubMed Central.Right now, only includes subsets used for the TREC Clinical Decision Support (CDS) 2014-16 tasks.

Medical Question Answering For Clinical Decision Support   DA: 23 PA: 10 MOZ Rank: 35

  • Recently, the TREC-CDS track has addressed this challenge by evaluating results of retrieving relevant scientific articles where the answers of medical questions in support of CDS can be found
  • Although retrieving relevant medical articles instead of identifying the answers was believed to be an easier task, state-of-the-art results are not yet

TREC 2017 Precision Medicine Track Relevance Judgment   DA: 20 PA: 25 MOZ Rank: 48

TREC 2017 Precision Medicine Track Relevance Judgment Guidelines Version 2017-06-30 In the previous years of the TREC Clinical Decision Support Track, relevance assessors have

Text REtrieval Conference (TREC) 2017 Precision Medicine Track   DA: 13 PA: 22 MOZ Rank: 39

TREC 2017 Track Web Page: (See track web site to download the document collections.) Test topics Relevance judgment file suitable for trec

Diagnostic Inferencing Via Improving Clinical Concept   DA: 21 PA: 24 MOZ Rank: 50

  • 3.1 TREC CDS We use the 2015 TREC CDS track dataset (Roberts et al., 2015) to conduct our experi-ments
  • This dataset contains 30 topics, where each topic is a medical case narrative that describes a patient scenario
  • Each topic contains \description", \summary", and \diagno-

Cluster-based Query Expansion Using External Collections   DA: 21 PA: 38 MOZ Rank: 65

  • TREC CDS consists of biomedical literature, specifically a subset of PubMed Central, with 30 test queries
  • A document is a full-text XML of a journal article
  • Test queries are classified into one of three classes: diagnosis, treatment, and test
  • The characteristics of the TREC CDS collection are that the average length of both a document and

Swarm Optimized Cluster Based Framework For Information   DA: 21 PA: 38 MOZ Rank: 66

  • Since TREC-CDS and OHSUMED data collections contain medical terms hence to preserve their importance, stemming is not performed on words to get its root form
  • Once the global vocabulary is created, a document vector i.e., a vector space model (VSM), is designed, which contains the count of occurrences of each term in the specific document.

Medical Question Answering For Clinical Decision Support   DA: 25 PA: 50 MOZ Rank: 83

  • of the TREC-CDS topics, which were processed to discern the medical concepts and their assertions in the same format as the nodes from the knowledge graph
  • We experimented with three different probabilistic inference methods to identify the most likely answers for each of the TREC-CDS topics evaluated in 2015


  • Statistical signi cance (paired t-test) over the baseline is denoted by for p<0:05 and for p<0:01
  • Bold-faced values represent the approaches with maximum performance
  • : : : : 40 3.5 Estimation of query di culty for TREC CDS topics

SNUMedinfo At TREC CDS Track 2014: Medical Case-based   DA: 21 PA: 17 MOZ Rank: 48

  • TREC CDS 2014 was a medical case-based retrieval task, and each query had differ-ent target task among diagnosis, test or treatment
  • As a first step, we used external tagged knowledge based query expansion method to retrieve relevant documents
  • As a second step, we trained machine learning document classifier to compute task-

Diagnostic Inferencing Via Improving Clinical Concept   DA: 21 PA: 17 MOZ Rank: 49

  • Our preliminary experiments on the TREC CDS dataset demonstrate the effectiveness of our system over non-reinforcement learning-based strong baselines
  • BibTeX @InProceedings{pmlr-v68-ling17a, title = {Diagnostic Inferencing via Improving Clinical Concept Extraction with Deep Reinforcement Learning: A Preliminary Study}, author

LAMDA At TREC CDS Track 2015   DA: 13 PA: 32 MOZ Rank: 57

  • that the relevance score is the answer for 2014 TREC CDS track and the expected score is a value resulted from our system
  • We found the optimized weight of field through greedy search
  • 3) Borda Fuse Scoring Method In Task B, we used Borda Fuse Ranking Score model [11, 12] that is based on election strategies such as voting model

State-of-the-art In Biomedical Literature Retrieval For   DA: 18 PA: 33 MOZ Rank: 64

  • and the 2014 TREC CDS track had a similar level of participation as the previous tracks
  • 3 Task overview 3.1 Document collection The document collection for the CDS track was the open access subset1 of PubMed Central2 (PMC)
  • PMC is an online digital database of freely available full-text biomedical

NLM NIH At TREC 2016 Clinical Decision Support Track   DA: 18 PA: 33 MOZ Rank: 65

Table 2: TREC CDS 2016 results for our submitted runs Measure NLMrun1 (Summary) NLMrun2 (Summary) NLMrun3 (Summary) NLMrun4 (Note) NLMrun5 (Note) …

Lemur Project Components: Lemur Toolkit   DA: 16 PA: 10 MOZ Rank: 41

  • The Lemur Toolkit APIs have been deprecated
  • The final released version of the Lemur Toolkit is version 4.12, released 06/21/2010.
  • The Lemur Toolkit is designed to facilitate research in language modeling and information retrieval (IR), where IR is broadly interpreted to include such technologies as ad hoc and distributed retrieval with structured queries, cross-language …

A Test Collection For Matching Patients To Clinical Trials   DA: 22 PA: 47 MOZ Rank: 85

  • the TREC CDS [11], comprising 60 patient case reports (30 from 2014 and 30 from 2015)
  • Each topic describes a patient with certain conditions and observations
  • Each patient case topic had two forms: a description (on average 78 words) and a shorter summary (on average 22 words)
  • As noted above, the topics were verbose patient case re-ports.

Saeid Balaneshin (Balaneshinkordan)   DA: 14 PA: 14 MOZ Rank: 45

  • • Knowledge-based Query Expansion in Clinical Decision Support Systems (won three competitions in TREC-CDS'15)
  • • Sequential Detection, Quickest Search and Change Point Detection Algorithms in Cognitive Radio Networks and Social Media (1 paper accepted in ISCCSP'14 and 1 workshop in ITA'14).

Cognitive Biases In Crowdsourcing   DA: 13 PA: 34 MOZ Rank: 65

  • TREC CDS Medical 1.3M 90 114k TIPSTER 1–3 Newswire 1M 200 336k Figure 1: The baseline annotation interface presents work-ers with a brief topic description as well as document title and content and asks for binary relevance labels
  • 3.2 Baseline Setup Let us consider a standard document relevance assessment task

A Study To Investigate Safety, Pharmacokinetics (PK) And   DA: 18 PA: 21 MOZ Rank: 58

A Study to Investigate Safety, Pharmacokinetics (PK) and Pharmacodynamics (PD) of BKM120 Plus GSK1120212 in Selected Advanced Solid Tumor Patients - Full Text View.

Optimal Citation Context Window Sizes For Biomedical Retrieval   DA: 11 PA: 20 MOZ Rank: 51

  • We investigate the TREC-CDS 2016 test collections as a new resource for citation context and citation-based IR experiments
  • The collection contains more than 1.25 million biomedical full-text articles in XML
  • We nd that a citation index can easily be extracted, and citation contexts easily be identi ed.

Semantically Enhanced Medical Information Retrieval System   DA: 19 PA: 17 MOZ Rank: 57

Experiments with the TREC CDS 2014 data set: 1) showed that the performance of the proposed system is significantly better than the baseline system and the systems reported in TREC CDS 2014 conference, and is comparable with the state-of-the-art systems and 2) demonstrated the capability of tensor-based semantic enrichment methods for medical

Query Expansion Study For Clinical Decision Support   DA: 21 PA: 49 MOZ Rank: 92

  • for e ective treatment, would use TREC-CDS information retrieval systems
  • But it’s not easy to access to the biomedical literature articles which should be advantageous to obtain answers to the generic clinical questions due to the high volume
  • It’s necessary to nd e cient information retrieval ways with notes (2016), descriptions and summaries

Denoising Clinical Notes For Medical Literature Retrieval   DA: 20 PA: 31 MOZ Rank: 74

  • documents.￿e approach was evaluated on the 2016 TREC CDS dataset, where it achieved a 37% improvement in infNDCG over state-of-the-art query reduction methods and a 27% improvement over the best known method for the task
  • CCS CONCEPTS •Information systems ! ￿ery reformulation; •Computing methodologies ! Neural networks; KEYWORDS

A Feedback-Based Approach To Utilizing Embeddings For   DA: 17 PA: 36 MOZ Rank: 77

  • Secondly, a text classification based method is used for the topic-specific ranking
  • Note that the topics used in the TREC CDS task are classified into three categories, i.e., diagnosis, test and treatment
  • Finally, the method combines the relevance ranking score and the topic-specific ranking score with Borda-fuse method .

The Impact Of Database Selection On Distributed Searching   DA: 17 PA: 20 MOZ Rank: 62

  • the TREC CDs, and with the restriction that all of the doc-uments in a database were from the same primary source
  • This testbed contains 100 databases
  • SYM-236 (Source-Year-Month) – This testbed was designed to contain a temporal component

An Improved BM25 Algorithm For Clinical Decision Support   DA: 38 PA: 36 MOZ Rank: 100

  • The IR aims to retrieve related documents based on a given query
  • The relevancy of documents to queries is often gauged by the score assigned by an IR model, e.g., the widely-implemented BM25 model [].On the one hand, the past few decades witnessed the implementation of machine learning technology when information retrieval was a concern.

Data Challenges Innovation Center For Biomedical   DA: 19 PA: 17 MOZ Rank: 63

  • The challenge brought together data scientists, economists, global health experts, and others to crowdsource data analysis and create visualizations or analysis tools that clearly communicate findings
  • The Data Challenge crowdsourced academic and professional talent in data science, public health policy, economics, and related fields.

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