Extract oncological entities and relations

**Extract oncological entities and relations:**

Pipeline meticulously developed to detect and classify a wide spectrum of oncological entities (over 40 entities), ranging from adenopathy, biomarkers, cancer diagnoses, chemotherapy details, dates, death indicators, dosages, hormonal therapy, imaging tests, metastasis, pathology results, performance statuses, racial categories, radiotherapy, smoking status, tumor size, and numerous other critical elements crucial in oncological contexts.

It also assigns an assertion status to entites and thus detects for instance if a diagnosis relates to the patient or to his/her family, if a test was conducted in the past or if a diagnosis is hypothetical or confirmed.

The pipeline also identifies relations between dates and other clinical entities, between tumor mentions and their size, between anatomical entities and other clinical entities, and between tests and their results.

Expertly tailored for oncologists, radiologists, pathologists, and clinical researchers, this model offers high precision in extracting critical data points from clinical documentation, pathology reports, diagnostic reports, and oncological research literature. Employ this entity recognizer to advance personalized cancer treatment plans, optimize clinical workflows in oncology, and drive impactful research in cancer biology and therapeutics.

**Additional Model Information**
- [Full model info on John Snow Labs Models Hub](https://nlp.johnsnowlabs.com/2023/06/26/ner_oncology_pipeline_en.html)
- **Domain:** Clinical Text Analysis
- **Subdomain:** Oncology
- **Predictable entities:** Histological_Type, Direction, Staging, Cancer_Score, Imaging_Test, Cycle_Number, Tumor_Finding, Site_Lymph_Node, Invasion, Response_To_Treatment, Smoking_Status, Tumor_Size, Cycle_Count, Adenopathy, Age, Biomarker_Result, Unspecific_Therapy, Site_Breast, Chemotherapy, Targeted_Therapy, Radiotherapy, Performance_Status, Pathology_Test, Site_Other_Body_Part, Cancer_Surgery, Line_Of_Therapy, Pathology_Result, Hormonal_Therapy, Site_Bone, Biomarker, Immunotherapy, Cycle_Day, Frequency, Route, Duration, Death_Entity, Metastasis, Site_Liver, Cancer_Dx, Grade, Date, Site_Lung, Site_Brain, Relative_Date, Race_Ethnicity, Gender, Oncogene, Dosage, Radiation_Dose,
Absent, Family, Hypothetical, Past, Possible, Present
- **Deployment Identifier:** 27. Extract oncological entities and relations

**How to run this model:**
1. Acquire a John Snow Labs Pay As You Go (PAYG) license from [John Snow Labs](https://my.johnsnowlabs.com/).
2. Import this listing.
3. Use the attached notebook to deploy the model with **27. Extract oncological entities and relations** as the model parameter. **Do not use the Open button on this page which appears after importing this listing. It will fail to deploy a model and does not work yet, you must use the attached notebook.**.

This model comes with optimized CPU and GPU builds. You can select which one to deploy via the notebook.

**How to obtain a PAYG license:**
1. Access [my.JohnSnowLabs.com](https://my.johnsnowlabs.com) and log in to your account. If you don't have an account, create one.
2. Go to the Get License page.
3. Switch to the PAYG Subscription tab and provide your credit card details.
4. Carefully review the End User License Agreement and the Terms and Conditions documents. If you agree, click on the Create Subscription button.
5. Once the process is complete, you will find your PAY-As-You-GO license listed on the My Subscriptions page.
6. Visit the My Subscriptions page and copy the PAYG license key by clicking on the copy icon in the License Key column.
7. Go to your Databricks notebook and paste your JSL-license into the JSL-License field in the top of the notebook. You are now ready to go!

搜集汇总

背景与挑战

背景概述

该数据集是一个肿瘤学专用临床文本分析模型，可精准识别40余种肿瘤相关实体（如癌症诊断、治疗方案等）及其相互关系，适用于临床文档和研究文献分析。模型支持断言状态判定和实体关系识别，专为肿瘤诊疗流程优化和个性化治疗设计而开发。