PHYSICIAN SUBSPECIALTY TAXONOMY USING DATA-DRIVEN MODELS
A method includes receiving data associated with a plurality of physicians, extracting features from the data to determine a plurality of training examples, each training example being associated with a different physician, determining ground truth labels for one or more of the training examples to generate a plurality of labeled training examples, each ground truth label comprising a taxonomy associated with a physician, segregating the plurality of labeled training examples into training data, validation data, and test data, training a machine learning model to predict a taxonomy associated with a physician based on the training data, tuning hyperparameters of the model based on the validation data, and interpreting the model on both a taxonomy and physician level.
1 . A method comprising:
receiving data associated with a plurality of physicians;
extracting features from the data to determine a plurality of training examples, each training example being associated with a different physician;
determining ground truth labels for one or more of the training examples to generate a plurality of labeled training examples, each ground truth label comprising a taxonomy associated with a physician;
segregating the plurality of labeled training examples into training data, validation data, and test data;
training a machine learning model to predict a taxonomy associated with a physician based on the training data; and
tuning hyperparameters of the model based on the validation data.
2 . The method of claim 1 , wherein the model comprises a random forest architecture.
3 . The method of claim 1 , wherein the model comprises a deep neural network.
4 . The method of claim 1 , wherein:
the data associated with the plurality of physicians comprises data about different medical procedures performed by the physicians; and
extracting features from the data comprises determining a number of times that each of the physicians has performed each of the different medical procedures with a predetermined time period.
5 . The method of claim 1 , further comprising:
training a plurality of models to predict a taxonomy associated with a physician based on the training data;
testing a performance of each of the models based on the test data; and
selecting the model having the best performance among the plurality of models.
6 . The method of claim 5 , further comprising:
testing the performance of each of the models by determining F-scores associated with outputs of each of the models.
7 . The method of claim 1 , further comprising:
training the model to determine a confidence level of the predicted taxonomy.
8 . The method of claim 1 , further comprising:
training a first stage of the model to predict a specialty associated with a physician based on the training data; and
training a second stage of the model to predict a taxonomy within the specialty associated with the physician based on the training data.
9 . The method of claim 1 , wherein training the model comprises:
training a stacked autoencoder using unlabeled data;
adding a plurality of fully connected layers to the stacked autoencoder;
after training the stacked autoencoder, training the fully connected layers based on the training data; and
after training the fully connected layers, training the model comprising the stacked autoencoder and the fully connected layers based on the training data.
10 . The method of claim 1 , further comprising:
using unsupervised learning techniques to determine a similarity between unlabeled training examples and the labeled training examples based on the features of the labeled training examples and the features of the unlabeled training examples;
determining ground truth labels for one or more of the unlabeled training examples based on the similarity to generate supplemental labeled training examples;
combining the labeled training examples and the supplemental labeled training examples to generate expanded labeled training examples; and
segregating the expanded labeled training examples into training data, validation data, and test data.
11 . The method of claim 1 , further comprising:
determining a relative amount that one or more of the features contribute to one or more taxonomies output by the model using Shapley Additive Explanations.
12 . The method of claim 1 , further comprising:
receiving unlabeled data associated with a target physician;
extracting target features from the unlabeled data;
inputting the target features into the trained model; and
assigning a taxonomy to the target physician based on the trained model.
13 . The method of claim 12 , wherein:
the trained model outputs a probability value that the target physician is associated with each of a plurality of taxonomies; and
assigning the taxonomy to the target physician comprises selecting the taxonomy having the highest probability value output by the trained model.
14 . The method of claim 12 , further comprising:
determining a relative amount that one or more of the target features contribute to the taxonomy assigned to the target physician using Shapley Additive Explanations.
15 . An apparatus comprising a controller programmed to:
receive data associated with a plurality of physicians;
extract features from the data to determine a plurality of training examples, each training example being associated with a different physician;
determine ground truth labels for one or more of the training examples to generate a plurality of labeled training examples, each ground truth label comprising a taxonomy associated with a physician;
segregate the plurality of labeled training examples into training data, validation data, and test data;
train a machine learning model to predict a taxonomy associated with a physician based on the training data; and
tune hyperparameters of the model based on the validation data.
16 . The apparatus of claim 15 , wherein:
the data associated with the plurality of physicians comprises data about different medical procedures performed by the physicians; and
the controller is configured to extract the features from the data comprises determining a number of times that each of the physicians has performed each of the different medical procedures with a predetermined time period.
17 . The apparatus of claim 15 , wherein the controller is further programmed to:
train a plurality of models to predict a taxonomy associated with a physician based on the training data;
test a performance of each of the models based on the test data; and
select the model having the best performance among the plurality of models.
18 . The apparatus of claim 15 , wherein the controller is further programmed to:
train a first stage of the model to predict a specialty associated with a physician based on the training data; and
train a second stage of the model to predict a taxonomy within the specialty associated with the physician based on the training data.
19 . The apparatus of claim 15 , wherein the apparatus is further programmed to:
use unsupervised learning techniques to determine a similarity between unlabeled training examples and the labeled training examples based on the features of the labeled training examples and the features of the unlabeled training examples;
determine ground truth labels for one or more of the unlabeled training examples based on the similarity to generate supplemental labeled training examples;
combine the labeled training examples and the supplemental labeled training examples to generate expanded labeled training examples; and
segregate the expanded labeled training examples into training data, validation data, and test data.
20 . The apparatus of claim 15 , wherein the controller is further programmed to:
receive unlabeled data associated with a target physician;
extract target features from the unlabeled data;
input the target features into the trained model; and
assign a taxonomy to the target physician based on the trained model.