SYSTEM AND METHOD FOR SMART POOLING
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.
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.