Clinical decision support systems and methods

A clinical decision support (CDS) method implemented by a CDS system, the CDS method comprising:

• providing, by a processor of the CDS system, a graphical user interface to a user, the graphical user interface graphically representing patient cases on a display device by patient case icons comprising patient images or thumbnail images generated from patient images;
• receiving, from the user via the graphical user interface, a selection of patient cases that form each of a plurality of user-defined clinically related groups;
• determining, by the processor, probative features of the patient cases having values that distinguish among the user-defined clinically related groups using a machine learning component that implements an algorithm or combination of algorithms selected from a group consisting of:
• a genetic algorithm, a k-nearest neighbor algorithm;
• a k-mean clustering algorithm, a simulated annealing algorithm, an exhaustive search algorithm, a feature reduction algorithm, and a floating search algorithm;
• receiving current patient data related to a current patient;
• identifying, by the processor, a select clinically related group of the plurality of clinically related groups based on values of the probative features determined from the received current patient data;
• determining clinical decision support information for the current patient based on one or more treatments associated with the select clinically related group;
• and
• providing the clinical decision support information to the user for prescribing to the current patient;
• wherein the clinical decision support information includes a medical regime to be applied corresponding to a prior medical regime of one or more prior patient cases having values of probative features most closely related to the values of the probative features determined from the current patient data.

A non-transitory computer readable medium that includes instructions executable by a digital processor to:

• provide a graphical user interface by which a user groups patient cases into a plurality of user-defined clinically related groups, the graphical user interface graphically representing patient cases by patient case icons comprising patient images or thumbnail images generated from patient images;
• determine probative features of the patient cases having values that distinguish among the user-defined clinically related groups using a machine learning component that implements an algorithm or combination of algorithms selected from a group consisting of:
• a genetic algorithm, a k-nearest neighbor algorithm, a k-mean clustering algorithm, a simulated annealing algorithm, an exhaustive search algorithm, a feature reduction algorithm, and a floating search algorithm;
• receive current patient data related to a current patient;
• identify a select clinically related group of the plurality of clinically related groups based on values of the probative features determined from the received current patient data,
• determine clinical decision support information for the current patient based on one or more treatments associated with the select clinically related group;
• and
• provide the clinical decision support information to the user for prescribing to the current patient;
• wherein the clinical decision support information includes a medical regime to be applied corresponding to a prior medical regime of one or more prior patient cases having values of probative features most closely related to the values of the probative features determined from the current patient data.