Methods and systems for recommending filters to apply to clinical trial search results using machine learning techniques

A system, comprising:

• a processor;
• and
• a memory having instructions stored thereon which, when executed by the processor, performs an operation for cognitive clinical trial filtration, the operation comprising:
• receiving a first set of search filters that were applied to a first plurality of clinical trials with respect to a first patient;
• determining one or more attributes of the first patient;
• training a machine learning (ML) model based on the first set of filters and the one or more attributes of the first patient;
• receiving a selection of a second patient;
• determining one or more attributes of the second patient;
• generating a base search result set for the second patient based on the determined one or more attributes of the second patient, the base search result set including a set of potentially relevant clinical trials for the second patient;
• generating a second set of search filters by processing the one or more attributes of the second patient using the trained ML model, wherein the second set of search filters comprise search filters recommended for searching for relevant clinical trials for the second patient based on filters used to search for relevant clinical trials for historical patients having similar attributes to the determined one or more attributes of the second patient, and wherein the second set of search filters comprise search filters having a highest probability of relevance in a probability distribution over a universe of candidate search filters;
• and
• generating a refined search result set by applying the generated second set of search filters to the base search result set.

A non-transitory computer-readable storage medium having instructions stored thereon which, when executed by a processor, performs an operation for cognitive clinical trial filtration, the operation comprising:

• receiving a first set of search filters that were applied to a first plurality of clinical trials with respect to a first patient;
• determining one or more attributes of the first patient;
• training a machine learning (ML) model based on the first set of filters and the one or more attributes of the first patient;
• receiving a selection of a second patient;
• determining one or more attributes of the second patient;
• generating a base search result set for the second patient based on the determined one or more attributes of the second patient, the base search result set including a set of potentially relevant clinical trials for the second patient;
• generating a second set of search filters by processing the one or more attributes of the second patient using the trained ML model, wherein the second set of search filters comprise search filters recommended for searching for relevant clinical trials for the second patient based on filters used to search for relevant clinical trials for historical patients having similar attributes to the determined one or more attributes of the second patient, and wherein the second set of search filters comprise search filters having a highest probability of relevance in a probability distribution over a universe of candidate search filters;
• and
• generating a refined search result set by applying the generated second set of search filters to the base search result set.