System and method for identifying outlier data and generating corrective action
A system identifying outlier data and generating corrective action, wherein the system includes at least a processor and a memory communicatively connected to the at least a processor and containing instructions. The instructions may configure the at least a processor to obtain a dataset, apply a clustering model to the dataset to generate a set of clusters based on the inherent relationships between the data points, determine the distance metric for each data point relative to its corresponding cluster centroid, define an outlier threshold based on the distance metrics of the data points, identify the data points that exceed the outlier threshold as one or more outliers, classify the one or more outliers across one or more axes, and output a report of the one or more identified outliers, wherein the report suggests corrective actions and insights for each of the one or more identified outliers.
1. A system for identifying outlier data and generating corrective action, wherein the system comprises:
at least a processor; and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
obtain a dataset comprising a plurality of data points;
apply a clustering model to the dataset to generate a set of clusters based on inherent relationships between the data points;
determine a distance metric for each data point relative to its corresponding cluster centroid;
define an outlier threshold based on the distance metrics of the data points;
identify the data points that exceed the outlier threshold as one or more outliers;
classify the one or more outliers across one or more axes, wherein classifying the one or more outliers further comprises categorizing each of the one or more outliers into specific outlier types;
correlate each categorized specific outlier type to a recommended corrective action using a look-up table; and
output a report of the one or more identified outliers and the specific outlier types, wherein the report suggests the recommended corrective actions and insights for each of the one or more identified outliers.
2. The system of claim 1 , wherein the at least a processor is further configured to investigate the one or more outliers, wherein investigating the one or more outliers comprises:
verifying the outliers by reviewing each individual outlier, wherein reviewing each individual outlier comprises:
comparing each individual outlier to a standard set of criteria; and
preforming a contextual analysis on each individual outlier, wherein the contextual analysis comprises identifying one or more relevant features, wherein relevant features comprise one or more of temporal context, environmental factors, patient characteristics, or comparative data.
3. The system of claim 2 , wherein verification is accomplished utilizing a verification model employing data validation techniques.
4. The system of claim 3 , wherein the verification model employs a logistic regression model to provide insights into which features are most significant in predicting whether a dataset is an outlier.
5. The system of claim 3 , wherein the verification model employs a support vector model to provide insights into which features are most significant in predicting whether a dataset is an outlier.
6. The system of claim 2 , wherein the contextual analysis is accomplished utilizing a context model, wherein the context model comprises a machine-learning model configured to analyze the one or more outliers across one or more axes in relation to the one or more relevant features.
7. The system of claim 6 , wherein the context model is trained using exemplary historical admission data correlated with exemplary contextual analysis reports.
8. The system of claim 1 , wherein the report further comprises instructions, wherein the instructions are configured to convey corrective steps.
9. The system of claim 1 , wherein the at least a processor is further configured to integrate the report into one or more external systems.
10. The system of claim 1 , wherein the at least a processor is further configured to store the report in a database.
11. A method for identifying outlier data and generating corrective action, wherein the method comprises:
obtaining a dataset comprising a plurality of data points;
applying a clustering model to the dataset to generate a set of clusters based on inherent relationships between the data points;
determining a distance metric for each data point relative to its corresponding cluster centroid;
defining an outlier threshold based on the distance metrics of the data points;
identifying the data points that exceed the outlier threshold as one or more outliers;
classifying the one or more outliers across one or more axes, wherein classifying the one or more outliers further comprises categorizing each of the one or more outliers into specific outlier types;
correlate each categorized specific outlier type to a recommended corrective action using a look-up table; and
outputting a report of the one or more identified outliers, and the specific outlier types, wherein the report suggests the recommended corrective actions and insights for each of the one or more identified outliers.
12. The method of claim 11 , wherein the method further comprises investigating the one or more outliers, wherein investigating the one or more outliers comprises:
verifying the outliers by reviewing each individual outlier, wherein reviewing each individual outlier comprises:
comparing each individual outlier to a standard set of criteria; and
preforming a contextual analysis on each individual outlier, wherein the contextual analysis comprises identifying one or more relevant features, wherein relevant features comprise one or more of temporal context, environmental factors, patient characteristics, or comparative data.
13. The method of claim 12 , wherein verification is accomplished utilizing a verification model employing data validation techniques.
14. The method of claim 13 , wherein the verification model employs a logistic regression model to provide insights into which features are most significant in predicting whether a dataset is an outlier.
15. The method of claim 13 , wherein the verification model employs a support vector model to provide insights into which features are most significant in predicting whether a dataset is an outlier.
16. The method of claim 12 , wherein the contextual analysis is accomplished utilizing a context model, wherein the context model comprises a machine-learning model configured to analyze the one or more outliers across one or more axes in relation to the one or more relevant features.
17. The method of claim 16 , wherein the context model is trained using exemplary historical admission data correlated with exemplary contextual analysis reports.
18. The method of claim 11 , wherein the report further comprises instructions, wherein the instructions are configured to convey corrective steps.
19. The method of claim 11 , wherein the at least a processor is further configured to integrate the report into one or more external systems.
20. The method of claim 11 , wherein the at least a processor is further configured to store the report in a database.