DOI: 10.57761/ak51-d519
title: INSPECT EHR

description: Jason Fries (jfries@stanford.edu) 
description: The INSPECT dataset (Integrating Numerous Sources for Prognostic Evaluation of Clinical Timelines) contains de-identified longitudinal electronic health records (EHRs) from a large cohort of pulmonary embolism (PE) patients, along with ground truth labels for multiple outcomes. It includes 19,390 patients EHRs linked to 23,248 CTPA studies with paired radiology impressions.
description: 

![inspect-logo.png](<https://redivis.com/fileUploads/282601b3-2c4b-4de2-a84c-742037a916cd>)

**1\. Overview**

INSPECT is a large-scale 3D multimodal medical imaging dataset:

- **19,390** patients
- **23,248** CT scans
- **225+ million** clinical events
- **3** linked modalities

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**2\. CT Scans + Radiology Impression Notes**

Imaging data are [available for download](<https://aimi.stanford.edu/datasets/inspect-Multimodal-Dataset-for-Pulmonary-Embolism-Diagnosis-and-Prognosis>) from the Stanford AIMI Center.



**3\. EHR Data**

EHR data is sourced from [Stanford’s STARR-OMOP database. ](<https://academic.oup.com/jamiaopen/article/6/3/ooad054/7236015>)Data are standardized in the [OMOP CDM](<https://www.ohdsi.org/data-standardization/>) schema and are fully de-identified. Complete technical details are included in the paper, but key highlights:



- Dates are jittered within patient to conceal real dates (but preserve deltas between dates)
- Data for patients >= 90 years old are removed
- Data for minors <18 are removed
- Unstructured text fields not mappable to OMOP standard concepts are redacted
- All clinical note text is redacted
- HIV test result are redacted.
- Provider names and NPIs are redacted

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Please see our Github repo to obtain code for loading the dataset, including a full data preprocessing pipeline for reproducibility, and running a set of pretrained baseline models




description: Access to the INSPECT dataset requires the following:

- Verified Affiliation (Academic, Government, Industry Research Lab). Please use your **verified email address** when applying, **do not use gmail or personal emails.**
- Encryption Verification / Attestation for Data Storage
- Signing the terms of the INSPECT Data Set License 1.0
- Providing a short description of your intended research use of INSPECT
- CITI Training

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**These data must remain on your encrypted machine. Redistribution of data is FORBIDDEN and will result in immediate termination of access privileges. **



**IMPORTANT NOTES**:

- Our policy on derived works aligns with PhysioNet's guidelines, requiring that these artifacts be hosted on Redivis. If you create derived research artifacts based on INSPECT EHR (such as additional annotations or synthetic data), please contact us to discuss hosting arrangements.
- Sending INSPECT data over a non-HIPAA-compliant API is a violation of the DUA.

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Please allow 7-10 business days to process applications.


description: #Citation  
@inproceedings{NEURIPS2023\_39736af1,

author = {Huang, Shih-Cheng and Huo, Zepeng and Steinberg, Ethan and Chiang, Chia-Chun and Langlotz, Curtis and Lungren, Matthew and Yeung, Serena and Shah, Nigam and Fries, Jason},

booktitle = {Advances in Neural Information Processing Systems},

editor = {A. Oh and T. Naumann and A. Globerson and K. Saenko and M. Hardt and S. Levine},

pages = {17742--17772},

publisher = {Curran Associates, Inc.},

title = {INSPECT: A Multimodal Dataset for Patient Outcome Prediction of Pulmonary Embolisms},

url = {https://proceedings.neurips.cc/paper\_files/paper/2023/file/39736af1b9d87a1fddad9f84a6bcf64c-Paper-Datasets\_and\_Benchmarks.pdf},

volume = {36},

year = {2023}

}