MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
i2b2 query data 1.0
This is a dataset of i2b2 query builder examples that are taken from a test environment of i2b2 and then pre-processed with AI descriptions.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Raw runtimes for metadata associated with the Advancing clinical cohort selection with genomics analysis on a distributed platform manuscript. Markdown used to generate plots at: https://github.com/OmicsDataAutomation/i2b2-oda-framework/blob/master/genomicsdb/results.Rmd.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
A dataset with information on cancer history, mutation status and surveillance history for more than 100 000 study patients is provided in i2b2 (Informatics for Integrating Biology and the Bedside, http://www.i2b2.org/software). Members of the German Consortium for Hereditary Breast and Ovarian Cancer can request access to i2b2 and will be able to perform database queries independently, e.g. with regard to identify suitable patient populations for scientific evaluation projects.
The data for the smoking challenge consisted exclusively of discharge summaries from Partners HealthCare which were preprocessed and converted into XML format, and separated into training and test sets. I2B2 is a data warehouse containing clinical data on over 150k patients, including outpatient DX, lab results, medications, and inpatient procedures. ETL processes authored to pull data from EMR and finance systems Institutional review boards of Partners HealthCare approved the challenge and the data preparation process. The data were annotated by pulmonologists and classified patients into Past Smokers, Current Smokers, Smokers, Non-smokers, and unknown. Second-hand smokers were considered non-smokers. Other institutions involved include Massachusetts Institute of Technology, and the State University of New York at Albany. i2b2 is a passionate advocate for the potential of existing clinical information to yield insights that can directly impact healthcare improvement. In our many use cases (Driving Biology Projects) it has become increasingly obvious that the value locked in unstructured text is essential to the success of our mission. In order to enhance the ability of natural language processing (NLP) tools to prise increasingly fine grained information from clinical records, i2b2 has previously provided sets of fully deidentified notes from the Research Patient Data Repository at Partners HealthCare for a series of NLP Challenges organized by Dr. Ozlem Uzuner. We are pleased to now make those notes available to the community for general research purposes. At this time we are releasing the notes (~1,000) from the first i2b2 Challenge as i2b2 NLP Research Data Set #1. A similar set of notes from the Second i2b2 Challenge will be released on the one year anniversary of that Challenge (November, 2010).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Combining clinical and genomics queries using i2b2 – Three methods - Table 1
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The affordability of next-generation genomic sequencing and the improvement of medical data management have contributed largely to the evolution of biological analysis from both a clinical and research perspective. Precision medicine is a response to these advancements that places individuals into better-defined subsets based on shared clinical and genetic features. The identification of personalized diagnosis and treatment options is dependent on the ability to draw insights from large-scale, multi-modal analysis of biomedical datasets. Driven by a real use case, we premise that platforms that support precision medicine analysis should maintain data in their optimal data stores, should support distributed storage and query mechanisms, and should scale as more samples are added to the system. We extended a genomics-based columnar data store, GenomicsDB, for ease of use within a distributed analytics platform for clinical and genomic data integration, known as the ODA framework. The framework supports interaction from an i2b2 plugin as well as a notebook environment. We show that the ODA framework exhibits worst-case linear scaling for array size (storage), import time (data construction), and query time for an increasing number of samples. We go on to show worst-case linear time for both import of clinical data and aggregate query execution time within a distributed environment. This work highlights the integration of a distributed genomic database with a distributed compute environment to support scalable and efficient precision medicine queries from a HIPAA-compliant, cohort system in a real-world setting. The ODA framework is currently deployed in production to support precision medicine exploration and analysis from clinicians and researchers at UCLA David Geffen School of Medicine.
https://www.cancerimagingarchive.net/data-usage-policies-and-restrictions/https://www.cancerimagingarchive.net/data-usage-policies-and-restrictions/
The Data Integration & Imaging Informatics (DI-Cubed) project explored the issue of lack of standardized data capture at the point of data creation, as reflected in the non-image data accompanying 4 TCIA breast cancer collections (Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN 6657 trials (ISPY1), BREAST-DIAGNOSIS, Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy (Breast-MRI-NACT-Pilot), The Cancer Genome Atlas Breast Invasive Carcinoma Collection (TCGA-BRCA)) and the Ivy Glioblastoma Atlas Project (IvyGAP) brain cancer collection. The work addressed the desire for semantic interoperability between various NCI initiatives by aligning on common clinical metadata elements and supporting use cases that connect clinical, imaging, and genomics data. Accordingly, clinical and measurement data imported into I2B2 were cross-mapped to industry standard concepts for names and values including those derived from BRIDG, CDISC SDTM, DICOM Structured Reporting models and using NCI Thesaurus, SNOMED CT and LOINC controlled terminology. A subset of the standardized data was then exported from I2B2 in SDTM compliant SAS transport files. The SDTM data was derived from data taken from both the curated TCIA spreadsheets as well as tumor measurements and dates from the TCIA Restful API. Due to the nature of the available data not all SDTM conformance rules were applicable or adhered to. These Study Data Tabulation Model format (SDTM) datasets were validated using Pinnacle 21 CDISC validation software. The validation software reviews datasets according to their degree of conformance to rules developed for the purposes of FDA submissions of electronic data. Iterative refinements were made to the datasets based upon group discussions and feedback from the validation tool. Export datasets for the following SDTM domains were generated:
Data and knowledge management infrastructure for the new Center for Clinical and Translational Science (CCTS) at the University of Utah. This clinical cohort search tool is used to search across the University of Utah clinical data warehouse and the Utah Population Database for people who satisfy various criteria of the researchers. It uses the i2b2 front end but has a set of terminology servers, metadata servers and federated query tool as the back end systems. FURTHeR does on-the-fly translation of search terms and data models across the source systems and returns a count of results by unique individuals. They are extending the set of databases that can be queried.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Data Sets were generated using the Weakly Supervised NER pipeline (https://github.com/HUMADEX/Weekly-Supervised-NER-pipline) to train the symptom extraction NER models.
Supported Languages and dataset locations for the specific language:
English (base language): https://huggingface.co/HUMADEX/english_medical_ner
German: https://huggingface.co/HUMADEX/german_medical_ner
Italian: https://huggingface.co/HUMADEX/italian_medical_ner
Spanish: https://huggingface.co/HUMADEX/spanish_medical_ner
Greek: https://huggingface.co/HUMADEX/german_medical_ner
Slovenian: https://huggingface.co/HUMADEX/slovenian_medical_ner
Polish: https://huggingface.co/HUMADEX/polish_medical_ner
Portuguese: https://huggingface.co/HUMADEX/portugese_medical_ner
Dataset Building
Acknowledgement
This dataset had been created as part of joint research of HUMADEX research group (https://www.linkedin.com/company/101563689/) and has received funding by the European Union Horizon Europe Research and Innovation Program project SMILE (grant number 101080923) and Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks, project BosomShield ((rant number 101073222). Responsibility for the information and views expressed herein lies entirely with the authors.
Authors:
dr. Izidor Mlakar, Rigona Sallauka, dr. Umut Arioz, dr. Matej Rojc
Please cite as:
Article title: Weakly-Supervised Multilingual Medical NER For Symptom Extraction For Low-Resource Languages
Doi: 10.20944/preprints202504.1356.v1
Website: https://www.preprints.org/manuscript/202504.1356/v1" href="https://www.preprints.org/manuscript/202504.1356/v1">https://www.preprints.org/manuscript/202504.1356/v1
https://www.cancerimagingarchive.net/data-usage-policies-and-restrictions/https://www.cancerimagingarchive.net/data-usage-policies-and-restrictions/
The Data Integration & Imaging Informatics (DI-Cubed) project explored the issue of lack of standardized data capture at the point of data creation, as reflected in the non-image data accompanying various TCIA breast cancer collections. The work addressed the desire for semantic interoperability between various NCI initiatives by aligning on common clinical metadata elements and supporting use cases that connect clinical, imaging, and genomics data. Accordingly, clinical and measurement data was imported into I2B2 and cross-mapped to industry standard concepts for names and values including those derived from BRIDG, CDISC SDTM, DICOM Structured Reporting models and using NCI Thesaurus, SNOMED CT and LOINC controlled terminology. A subset of the standardized data was then exported from I2B2 to CSV and thence converted to DICOM SR according to the the DICOM Breast Imaging Report template [1] , which supports description of patient characteristics, histopathology, receptor status and clinical findings including measurements. The purpose was not to advocate DICOM SR as an appropriate format for interchange or storage of such information for query purposes, but rather to demonstrate that use of standard concepts harmonized across multiple collections could be transformed into an existing standard report representation. The DICOM SR can be stored and used together with the images in repositories such as TCIA and in image viewers that support rendering of DICOM SR content. During the project, various deficiencies in the DICOM Breast Imaging Report template were identified with respect to describing breast MR studies, laterality of findings versus procedures, more recently developed receptor types, and patient characteristics and status. These were addressed via DICOM CP 1838, finalized in Jan 2019, and this subset reflects those changes. DICOM Breast Imaging Report Templates available from: http://dicom.nema.org/medical/dicom/current/output/chtml/part16/sect_BreastImagingReportTemplates.html
https://github.com/MIT-LCP/license-and-dua/tree/master/draftshttps://github.com/MIT-LCP/license-and-dua/tree/master/drafts
RadCoref is a small subset of MIMIC-CXR with manually annotated coreference mentions and clusters. The dataset is annotated by a panel of three cross-disciplinary experts with experience in clinical data processing following the i2b2 annotation scheme with minimum modification. The dataset consists of Findings and Impression sections extracted from full radiology reports. The dataset has 950, 25 and 200 section documents for training, validation, and testing, respectively. The training and validation sets are annotated by one annotator. The test set is annotated by two human annotators independently, of which the results are merged manually by the third annotator. The dataset aims to support the task of coreference resolution on radiology reports. Given that the MIMIC-CXR has been de-identified already, no protected health information (PHI) is included.
Not seeing a result you expected?
Learn how you can add new datasets to our index.
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
i2b2 query data 1.0
This is a dataset of i2b2 query builder examples that are taken from a test environment of i2b2 and then pre-processed with AI descriptions.