Clinical Patient-Trial Matching Task
A Clinical Patient-Trial Matching Task is a clinical trial task that is a matching task of clinical trial patients and clinical trials.
References
2022
- HTTP://chat.openai.com
- Q: what is a patient-trial matching task?
- A: A patient-trial matching task involves matching patients with clinical trials that may be appropriate for them based on their medical history, current health status, and other relevant factors. This process is often performed by clinical trial coordinators or other healthcare professionals, and it is an important part of the clinical trial process.
The goal of patient-trial matching is to identify clinical trials that are suitable for a particular patient, based on factors such as the patient's diagnosis, the stage of the disease, and the patient's overall health. Matching patients with appropriate clinical trials can help ensure that the trial results are representative of the patient population and that the trial is conducted in a way that is safe and ethical.
There are several factors that can be taken into consideration when matching patients with clinical trials, including the patient's age, gender, medical history, and current health status. Other factors that may be considered include the type of treatment being tested in the trial, the stage of the disease, and the patient's ability to comply with the trial protocol.
It is important to note that participating in a clinical trial is entirely voluntary, and patients have the right to decline participation or to withdraw from a trial at any time. However, clinical trials offer patients the opportunity to receive cutting-edge treatments and to contribute to the advancement of medical knowledge.
2020
- (Gao et al., 2020) ⇒ Junyi Gao, Cao Xiao, Lucas M. Glass, and Jimeng Sun. (2020). “COMPOSE: Cross-modal Pseudo-siamese Network for Patient Trial Matching.” In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 803-812.
- ABSTRACT: Clinical trials play important roles in drug development but often suffer from expensive, inaccurate and insufficient patient recruitment. The availability of massive electronic health records (EHR) data and trial eligibility criteria (EC) bring a new opportunity to data driven patient recruitment. One key task named patient-trial matching is to find qualified patients for clinical trials given structured EHR and unstructured EC text (both inclusion and exclusion criteria). How to match complex EC text with longitudinal patient EHRs? How to embed many-to-many relationships between patients and trials? How to explicitly handle the difference between inclusion and exclusion criteria? In this paper, we proposed CrOss-Modal PseudO-SiamEse network (COMPOSE) to address these challenges for patient-trial matching. One path of the network encodes EC using convolutional highway network. The other path processes EHR with multi-granularity memory network that encodes structured patient records into multiple levels based on medical ontology. Using the EC embedding as query, COMPOSE performs attentional record alignment and thus enables dynamic patient-trial matching. COMPOSE also introduces a composite loss term to maximize the similarity between patient records and inclusion criteria while minimize the similarity to the exclusion criteria. Experiment results show COMPOSE can reach 98.0% AUC on patient-criteria matching and 83.7% accuracy on patient-trial matching, which leads 24.3% improvement over the best baseline on real-world patient-trial matching tasks.