Publication
Clinical trials (CTs) often fail due to inadequate patient recruitment. Finding eligible patients involves comparing the patient’s information with the CT eligibility criteria. Automated patient matching offers the promise of improving the process, yet the main difficulties of CT retrieval lie in the semantic complexity of matching unstructured patient descriptions with semi-structured, multi-field CT documents and in capturing the meaning of negation coming from the eligibility criteria.
This paper tackles the challenges of CT retrieval by presenting an approach that addresses the patient-to-trials paradigm. Our approach involves two key components in a pipeline-based model: (i) a data enrichment technique for enhancing both queries and documents during the first retrieval stage, and (ii) a novel re-ranking schema that uses a Transformer network in a setup adapted to this task by leveraging the structure of the CT documents.
We use named entity recognition and negation detection in both patient description and the eligibility section of CTs. We further classify patient descriptions and CT eligibility criteria into current, past, and family medical conditions. This extracted information is used to boost the importance of disease and drug mentions in both query and index for lexical retrieval. Furthermore, we propose a two-step training schema for the Transformer network used to re-rank the results from the lexical retrieval. The first step focuses on matching patient information with the descriptive sections of trials, while the second step aims to determine eligibility by matching patient information with the criteria section.
Our findings indicate that the inclusion criteria section of the CT has a great influence on the relevance score in lexical models, and that the enrichment techniques for queries and documents improve the retrieval of relevant trials. The re-ranking strategy, based on our training schema, consistently enhances CT retrieval and shows improved performance by 15% in terms of precision at retrieving eligible trials.
The results of our experiments suggest the benefit of making use of extracted entities. Moreover, our proposed re-ranking schema shows promising effectiveness compared to larger neural models, even with limited training data. These findings offer valuable insights for improving methods for retrieval of clinical documents.
W. Kusa, O. E. Mendoza, P. Knoth, G. Pasi, A. Hanbury, Effective matching of patients to clinical trials using entity extraction and neural re-ranking, Journal of Biomedical Informatics 144 (2023) 104444.
Related
Signup
Cookie | Duration | Description |
---|---|---|
cookielawinfo-checkbox-analytics | 1 year | Set by the GDPR Cookie Consent plugin, this cookie records the user consent for the cookies in the "Analytics" category. |
cookielawinfo-checkbox-functional | 1 year | The GDPR Cookie Consent plugin sets the cookie to record the user consent for the cookies in the category "Functional". |
cookielawinfo-checkbox-necessary | 1 year | Set by the GDPR Cookie Consent plugin, this cookie records the user consent for the cookies in the "Necessary" category. |
CookieLawInfoConsent | 1 year | CookieYes sets this cookie to record the default button state of the corresponding category and the status of CCPA. It works only in coordination with the primary cookie. |
PHPSESSID | session | This cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed. |
viewed_cookie_policy | 1 year | The GDPR Cookie Consent plugin sets the cookie to store whether or not the user has consented to use cookies. It does not store any personal data. |
Cookie | Duration | Description |
---|---|---|
mec_cart | 1 month | Provides functionality for our ticket shop |
VISITOR_INFO1_LIVE | 6 months | YouTube sets this cookie to measure bandwidth, determining whether the user gets the new or old player interface. |
VISITOR_PRIVACY_METADATA | 6 months | YouTube sets this cookie to store the user's cookie consent state for the current domain. |
YSC | session | Youtube sets this cookie to track the views of embedded videos on Youtube pages. |
yt-remote-connected-devices | never | YouTube sets this cookie to store the user's video preferences using embedded YouTube videos. |
yt-remote-device-id | never | YouTube sets this cookie to store the user's video preferences using embedded YouTube videos. |
yt.innertube::nextId | never | YouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen. |
yt.innertube::requests | never | YouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen. |
Cookie | Duration | Description |
---|---|---|
_ga | 1 year | Google Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors. |
_ga_* | 1 year | Google Analytics sets this cookie to store and count page views. |
_gat_gtag_UA_* | 1 min | Google Analytics sets this cookie to store a unique user ID. |
_gid | 1 day | Google Analytics sets this cookie to store information on how visitors use a website while also creating an analytics report of the website's performance. Some of the collected data includes the number of visitors, their source, and the pages they visit anonymously. |