Machine learning feature data from EHR, labels, and estimates for next generation sequencing-based assay

Next-generation sequencing-based tests have advanced the field of medical diagnostics, but their novelty and cost can lead to uncertainty in clinical deployment. The Heme-STAMP is one such assay that tracks mutations in genes implicated in hematolymphoid neoplasms. Rather than limiting its clinical usage or imposing rule-based criteria, we propose leveraging machine learning to guide clinical decision-making on whether this test should be ordered. We trained a machine learning model to predict the outcome of Heme-STAMP testing using 3,472 orders placed between May 2018 and September 2021 from an academic medical center and demonstrated how to integrate a custom machine learning model into a live clinical environment to obtain real-time model and physician estimates. The model predicted the results of a complex next-generation sequencing test with discriminatory power comparable to expert hematologists (AUC score: 0.77 [0.66, 0.87], 0.78 [0.68, 0.86] respectively) and with capacity to im..., The feature data was pulled from the STAnford medicine Research data Repository (STARR) and further processed to meet the needs of this study and privacy guidelines. Labels were obtained through the Stanford Pathology Department. Ordering physician estimates were generated by participating physicians and model estimates were generated by the machine learning model used in the study., , # Machine learning feature data from EHR, labels, and estimates for next generation sequencing-based assay

[https://doi.org/10.5061/dryad.nzs7h450b](https://doi.org/10.5061/dryad.nzs7h450b)

## Description of the data and file structure

These datasets were utilized to train and evaluate a machine learning model that predicts the outcome of the Heme-STAMP test, a next generation sequencing assay that tracks mutations in genes implicated in hematolymphoid neoplasms. The feature_data_anon.csv was used to train/test a Random Forest model and uses features such as demographics, lab results, medications, diagnoses, etc. Numerical values were binned by their distribution. For example, \"Age0\" would correspond to the 1st bucket of values while \"Age_3\" would correspond to the 4th bucket. The estimates.csv contains the estimations generated by the ordering physician and the machine learning model on the orders that were prospectively collected. 

### Files and variables

#### File: Feature\_data...