System and method for smart pooling

Abstract

A system for smart pooling includes a computing device configured to obtain a feature datum, identify a predictive prevalence value as a function of the feature datum, wherein identifying the predictive prevalence value further comprises receiving a predictive training set correlating the feature datum with a probabilistic outcome, training a predictive machine-learning model as a function of the predictive training set, and identifying the predictive prevalence value as a function of the trained predictive machine-learning model and the feature datum, and determine an enhanced well count.

What is claimed is: 
     
         1 . A system for smart pooling, the system comprising a computing device, wherein the computing device is configured to:
 obtain a feature datum;   identify a predictive prevalence value as a function of the feature datum, wherein identifying the predictive prevalence value further comprises:
 receiving a predictive training set correlating the feature datum with a probabilistic outcome; 
 training a predictive machine-learning model as a function of the predictive training set; and 
 identifying the predictive prevalence value as a function of the trained predictive machine-learning model and the feature datum; and 
   determine an enhanced well count.   
     
     
         2 . The system of  claim 1 , wherein obtaining the feature datum further comprises identifying a clinical element and obtaining the feature datum as a function of the clinical element. 
     
     
         3 . The system of  claim 1 , wherein obtaining the feature datum further comprises receiving a medical input and obtaining the feature datum as a function of the medical input. 
     
     
         4 . The system of  claim 1 , wherein identifying the predictive prevalence value further comprises determining a probabilistic distribution and identifying the predictive prevalence value as a function of the probabilistic distribution. 
     
     
         5 . The system of  claim 1 , wherein determining the enhanced well count further comprises:
 generating a pooling threshold; and   determining the enhanced well count as a function of the pooling threshold and the predictive prevalence value.   
     
     
         6 . The system of  claim 5 , wherein generating the pooling threshold further comprises:
 receiving a probability limiter; and   generating the pooling threshold as a function of the probability limiter.   
     
     
         7 . The system of  claim 1 , wherein the computing device is further configured to:
 receive a lab specimen associated with the feature datum;   generate an assignment of the lab specimen to a well as a function of the enhanced well count; and   produce a pool database as a function of assigning the lab specimen to the well.   
     
     
         8 . The system of  claim 7 , wherein generating the assignment further comprises:
 receiving a grouping element; and   generating the assignment of the lab specimen as a function of the grouping element and a grouping machine-learning model.   
     
     
         9 . The system of  claim 7 , wherein generating the assignment further comprises:
 identifying a similar predictive prevalence; and   generating the assignment as a function of the similar predictive prevalence.   
     
     
         10 . The system of  claim 7 , wherein producing the pool database further comprises identifying a delegated pooling strategy and producing the pool database as a function of the delegated pooling strategy. 
     
     
         11 . A method for smart pooling, the method comprising:
 obtaining, by a computing device, a feature datum;   identifying, by the computing device, a predictive prevalence value as a function of the feature datum, wherein identifying the predictive prevalence value further comprises:
 receiving a predictive training set correlating the feature datum with a probabilistic outcome; 
 training a predictive machine-learning model as a function of the predictive training set; and 
 identifying the predictive prevalence value as a function of the trained predictive machine-learning model and the feature datum; and 
   determining, by the computing device, an enhanced well count.   
     
     
         12 . The method of  claim 11 , wherein obtaining the feature datum further comprises identifying a clinical element and obtaining the feature datum as a function of the clinical element. 
     
     
         13 . The method of  claim 11 , wherein obtaining the feature datum further comprises receiving a medical input and obtaining the feature datum as a function of the medical input. 
     
     
         14 . The method of  claim 11 , wherein identifying the predictive prevalence value further comprises determining a probabilistic distribution and identifying the predictive prevalence value as a function of the probabilistic distribution. 
     
     
         15 . The method of  claim 11 , wherein determining the enhanced well count further comprises:
 generating a pooling threshold; and   determining the enhanced well count as a function of the pooling threshold and the predictive prevalence value.   
     
     
         16 . The method of  claim 15 , wherein generating the pooling threshold further comprises:
 receiving a probability limiter; and   generating the pooling threshold as a function of the probability limiter.   
     
     
         17 . The method of  claim 11 , further comprising:
 receiving a lab specimen associated with the feature datum;   generating an assignment of the lab specimen to a well as a function of the enhanced well count; and   producing a pool database as a function of assigning the lab specimen to the well.   
     
     
         18 . The method of  claim 17 , wherein generating the assignment further comprises:
 receiving a grouping element; and   generating the assignment of the lab specimen as a function of the grouping element and a grouping machine-learning model.   
     
     
         19 . The method of  claim 17 , wherein generating the assignment further comprises:
 identifying a similar predictive prevalence;   generating the assignment as a function of the similar predictive prevalence.   
     
     
         20 . The method of  claim 17 , wherein producing the pool database further comprises identifying a delegated pooling strategy and producing the pool database as a function of the delegated pooling strategy.