Learning to Rank for Consumer Health Search: A Semantic Approach

@inproceedings{Soldaini2017LearningTR,
  title={Learning to Rank for Consumer Health Search: A Semantic Approach},
  author={Luca Soldaini and Nazli Goharian},
  booktitle={ECIR},
  year={2017}
}
For many internet users, searching for health advice online is the first step in seeking treatment. We present a Learning to Rank system that uses a novel set of syntactic and semantic features to improve consumer health search. Our approach was evaluated on the 2016 CLEF eHealth dataset, outperforming the best method by 26.6% in NDCG@10. 
Aggregation on Learning to Rank for Consumer Health Information Retrieval
TLDR
This paper proposes to train a set of L2R models each using features extracted from only one field and then apply aggregation methods to combine the results obtained from each model. Expand
Improving Personalized Consumer Health Search: Notebook for eHealth at CLEF 2018
TLDR
A number of field based features are used for training a learning to rank model and a medical concept model proposed in previous work is re-employed for this year’s new task. Expand
Exploring Understandability Features to Personalize Consumer Health Search. TUW at CLEF 2017 eHealth
TLDR
This paper describes the participation of Technical University of Vienna (TUW) at CLEF eHealth 2017 Task 3, which aims to foster research on search for health consumers, emphasizing crucial aspects of this domain such as document understandability and trustworthiness. Expand
SIGIR 2017 Tutorial on Health Search (HS2017): A Full-day from Consumers to Clinicians
TLDR
This tutorial will provide attendees with a full stack of knowledge on health search, from understanding users and their problems to practical, hands-on sessions on current tools and techniques, current campaigns and evaluation resources, as well as important open questions and future directions. Expand
SIGIR 2018 Tutorial on Health Search (HS2018): A Full-day from Consumers to Clinicians
TLDR
This tutorial will provide attendees with a full stack of knowledge on health search, from understanding users and their problems to practical, hands-on sessions on current tools and techniques, current campaigns and evaluation resources, as well as important open questions and future directions. Expand
The Knowledge and Language Gap in Medical Information Seeking
TLDR
This dissertation proposes several methods to overcome challenges to overcome the language and knowledge gap that affects health experts' ability to properly formulate information needs and improve search outcomes. Expand
WSDM 2019 Tutorial on Health Search (HS2019): A Full-Day from Consumers to Clinicians
TLDR
This tutorial will provide attendees with a full stack of knowledge on health search, from understanding users and their problems to practical, hands-on sessions on current tools and techniques, current campaigns and evaluation resources, as well as important open questions and future directions. Expand
Choices in Knowledge-Base Retrieval for Consumer Health Search
TLDR
This paper investigates how retrieval using knowledge bases can be effectively translated to the consumer health search (CHS) domain, and delves into the finer details of doing this effectively, highlighting both pitfalls and payoffs. Expand
Improving medical search tasks using learning to rank
  • Mohammad Alsulmi, Ben Carterette
  • Computer Science
  • 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
  • 2018
TLDR
This paper presents a general approach for applying learning to rank (LtR), broadly applicable across many different algorithms, to clinical search tasks and shows that the proposed LtR ranking can effectively promote search results for the clinical domain resulting in a performance increase up to 28% compared to traditional ranking models. Expand
SIGIR 2017 Tutorial on Health Search (HS2017)
TLDR
This tutorial will provide attendees with a full stack of knowledge on health search, from understanding users and their problems to practical, hands-on sessions on current tools and techniques, current campaigns and evaluation resources, as well as important open questions and future directions. Expand
...
1
2
3
...

References

SHOWING 1-10 OF 14 REFERENCES
Ranking Health Web Pages with Relevance and Understandability
TLDR
The findings suggest that this approach promotes documents that are at the same time topically relevant and understandable. Expand
Team GU-IRLAB at CLEF eHealth 2016: Task 3
TLDR
This work uses synonyms and hypernyms from a large medical ontology to generate alternative formulations for a query and results obtained by the reformulated queries are fused using the Borda rank aggregation algorithm. Expand
The IR Task at the CLEF eHealth Evaluation Lab 2016: User-centred Health Information Retrieval
This paper details the collection, systems and evaluation methods used in the IR Task of the CLEF 2016 eHealth Evaluation Lab. This task investigates the e↵ectiveness of web search engines inExpand
How users search and what they search for in the medical domain
TLDR
The study reveals that, conversely to what is stated in much of the literature, the main focus of users, both laypeople and professionals, is on disease rather than symptoms, and a classifier to infer user expertise is built. Expand
Enhancing web search in the medical domain via query clarification
TLDR
The utility of bridging the gap between layperson and expert vocabularies is investigated and the approach adds the most appropriate expert expression to queries submitted by users, a task the authors call query clarification. Expand
QuickUMLS: a Fast, Unsupervised Approach for Medical Concept Extraction
Entity extraction is a fundamental step in many health informatics systems. In recent years, tools such as MetaMap and cTAKES have been widely used for medical concept extraction on medicalExpand
LETOR: Benchmark Dataset for Research on Learning to Rank for Information Retrieval
TLDR
This paper has constructed a benchmark dataset referred to as LETOR, derived the LETOR data from the existing data sets widely used in IR, namely, OHSUMED and TREC data and provided the results of several state-ofthe-arts learning to rank algorithms on the data. Expand
Positive attitudes and failed queries: an exploration of the conundrums of consumer health information retrieval
TLDR
It is found that many consumers were unable to find satisfactory information when performing a specific query, while in general the group viewed health information retrieval (HIR) on the Internet in a positive light. Expand
A Neural Attention Model for Categorizing Patient Safety Events
TLDR
A neural network architecture for identifying the type of safety events which is the first step in understanding these narratives is presented, based on a soft neural attention model to improve the effectiveness of encoding long sequences. Expand
Learning to rank: from pairwise approach to listwise approach
TLDR
It is proposed that learning to rank should adopt the listwise approach in which lists of objects are used as 'instances' in learning, and introduces two probability models, respectively referred to as permutation probability and top k probability, to define a listwise loss function for learning. Expand
...
1
2
...