Estimating Consumer Familiarity with Health Terminology: A Context-based Approach

Zeng‐Treitler, Qing; Goryachev, Sergey; Tse, Tony; Keselman, Alla; Boxwala, Aziz A.

doi:10.1197/jamia.m2592

Cited by 30 publications

(44 citation statements)

References 12 publications

(20 reference statements)

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“…A second patient commented, "I think hypertension is something that's being used so much with the ads, with the drug companies, that people probably understand it." Although the science of determining what patients understand is very much in its infancy, 31,32 work is ongoing to develop consumer health vocabularies.…”

Section: Discussionmentioning

confidence: 99%

No Change in Physician Dictation Patterns When Visit Notes Are Made Available Online for Patients

Kind

Fowles

Craft

et al. 2011

Mayo Clinic Proceedings

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Section: Discussionmentioning

confidence: 99%

No Change in Physician Dictation Patterns When Visit Notes Are Made Available Online for Patients

Kind

Fowles

Craft

et al. 2011

Mayo Clinic Proceedings

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“…The vocabulary is selected based on perceived familiarity of the general public with certain medical terms. Many approaches have been used for estimating familiarity, including surveys [21], predictive modeling [41], query log analysis [42], and text mining [40]. While this work helps with the choice of terminology medical professionals can use to communicate to the lay person, it does not directly help with the task of understanding a lay person's query.…”

Section: Related Workmentioning

confidence: 99%

Circumlocution in diagnostic medical queries

Stanton

Ieong

Mishra

2014

Proceedings of the 37th International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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Circumlocution is when many words are used to describe what could be said with fewer, e.g., "a machine that takes moisture out of the air" instead of "dehumidifier". Web search is a perfect backdrop for circumlocution where people struggle to name what they seek. In some domains, not knowing the correct term can have a significant impact on the search results that are retrieved. We study the medical domain, where professional medical terms are not commonly known and where the consequence of not knowing the correct term can impact the accuracy of surfaced information, as well as escalation of anxiety, and ultimately the medical care sought. Given a free-form colloquial health search query, our objective is to find the underlying professional medical term. The problem is complicated by the fact that people issue quite varied queries to describe what they have. Machine-learning algorithms can be brought to bear on the problem, but there are two key complexities: creating highquality training data and identifying predictive features. To our knowledge, no prior work has been able to crack this important problem due to the lack of training data. We give novel solutions and demonstrate their efficacy via extensive experiments, greatly improving over the prior art.

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“…Learning to Rank: Last, we analyse the results obtained by the learning to rank methods (indices [31][32][33][34][35]. Unlike with the previous methods, we did not impose a rank cut-off on learning to rank.…”

Section: Evaluation Of Understandability Retrievalmentioning

confidence: 99%

“…While we replicated here many of the features devised in these works, they focus on estimating readability of general English documents rather than medical ones. In the medical domain, Zeng et al explored features such as word frequency in different medical corpora to estimate concept familiarity, which prompted the construction of the Consumer Health Vocabulary (CHV) [31,32,33].…”

Section: Related Workmentioning

confidence: 99%

Consumer Health Search on the Web: Study of Web Page Understandability and Its Integration in Ranking Algorithms (Preprint)

Palotti¹,

Zuccon²,

Hanbury³

2018

Preprint

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Background: Understandability plays a key role in ensuring that people accessing health information are capable of gaining insights that can assist them with their health concerns and choices. The access to unclear or misleading information has been shown to negatively impact on the health decisions of the general public. Objective:We investigated methods to estimate the understandability of health Web pages and used these to improve the retrieval of information for people seeking health advice on the Web. Methods:Our investigation considered methods to automatically estimate the understandability of health information in Web pages, and it provided a thorough evaluation of these methods using human assessments as well as an analysis of preprocessing factors affecting understandability estimations, and associated pitfalls. Furthermore, lessons learnt for estimating Web page understandability were applied to the construction of retrieval methods with specific attention to retrieving information understandable by the general public. Results:We found that machine learning techniques were more suitable to estimate health Web page understandability than traditional readability formulae, which are often used as guidelines and benchmarking by health information providers on the Web (larger difference found for Pearson correlation of .602 using Gradient Boosting regressor compared to .438 using SMOG Index with CLEF 2015 collection). Learning to rank effectively exploited these estimates to provide the general public with more understandable search results ( H RBP ¿ reached 29.20, 22% higher than a BM25 baseline and 13% higher than the best system at CLEF 2016, both P≤ .001 ).

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Estimating Consumer Familiarity with Health Terminology: A Context-based Approach

Abstract: The context-based approach provides a good alternative to the existing term familiarity assessment methods.

Cited by 30 publications

References 12 publications

No Change in Physician Dictation Patterns When Visit Notes Are Made Available Online for Patients

No Change in Physician Dictation Patterns When Visit Notes Are Made Available Online for Patients

Circumlocution in diagnostic medical queries

Consumer Health Search on the Web: Study of Web Page Understandability and Its Integration in Ranking Algorithms (Preprint)

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