Data Annotation for Healthcare AI
Unlock complex information in unstructured data with entity extraction and recognition
Empowering teams to build world-leading AI products.
80% of data in the healthcare domain is unstructured, making it inaccessible. Accessing the data requires significant manual intervention, which limits the quantity of usable data. Understanding text in the medical domain requires a deep understanding of its terminology to unlock its potential. Shaip provides you the expertise to annotate healthcare data to improve AI engines at scale.
IDC, Analyst Firm:
The worldwide installed base of storage capacity will reach 11.7 zettabytes in 2023
IBM, Gartner & IDC:
80% of the data around the world is unstructured, making it obsolete and unusable.
Analyze data to discover meaningful insights to train NLP models with Medical Text Data Annotation
We offer Medical Data annotation services that help organizations extract critical information in unstructured medical data, i.e., Physician notes, EHR admission/discharge summaries, pathology reports, etc., that help machines to identify the clinical entities present in a given text or image. Our credentialed domain experts can help you deliver domain-specific insights – i.e., symptoms, disease, allergies, & medication, to help drive insights for care.
We also offer proprietary Medical NER APIs (pre-trained NLP models), which can auto-identify & classify the named entities presented in a text document. Medical NER APIs leverage proprietary knowledge graph, with 20M+ relationships & 1.7M+ clinical concepts
From data licensing, and collection, to data annotation, Shaip has got you covered.
- Annotation and preparation of medical images, videos, and texts, including radiography, ultrasound, mammography, CT scans, MRIs, and photon emission tomography
- Pharmaceutical and other healthcare use cases for natural language processing (NLP), including medical text categorization, named entity identification, text analysis, etc.
Medical Annotation Process
Annotation process generally differs to a client’s requirement but it majorly involves:
Phase 1: Technical domain expertise (Understand scope & annotation guidelines)
Phase 2: Training appropriate resources for the project
Phase 3: Feedback cycle and QA of the annotated documents
1. Clinical Entity Recognition/Annotation
A large amount of medical data and knowledge is available in the medical records mainly in an unstructured format. Medical entity Annotation enables us to convert unstructured data into a structured format.
2. Attribution Annotation
2.1 Medicine Attributes
Medications and their attributes are documented in almost every medical record, which is an important part of the clinical domain. We can identify and annotate the various attributes of medications according to guidelines.
2.2 Lab Data Attributes
Lab data is mostly accompanied by their attributes in a medical record. We can identify and annotate the various attributes of lab data according to guidelines.
2.3 Body Measurement attributes
Body measurement is mostly accompanied by their attributes in a medical record. It mostly comprises of the vital signs. We can identify and annotate the various attributes of body measurement.
3. Oncology Specific NER Annotation
Along with generic medical NER annotation, we can also work on domain specific annotations like oncology, radiology, etc. Here are the oncology specific NER entities that can be anotated – Cancer problem, Histology, Cancer stage, TNM stage, Cancer grade, Dimension, Clinical status, Tumor marker test, Cancer medicine, Cancer surgery, Radiation, Gene studied, Variation code, Body site
4. Adverse Effect NER & Relationship Annotation
Along with identifying and annotating major clinical entities and relationships, we can also annotate the adverse effects of certain drugs or procedures. The scope is as follows: Labeling adverse effects and their causative agents. Assigning the relationship between the adverse effect and the cause of the effect.
3. Relationship Annotation
After identifying and annotating clinical entities, we also assign relevant relationship among the entities. Relationships may exist between two or more concepts.
6. Assertion Annotation
Along with identifying clinical entities and relationships, we can also assign the Status, Negation and Subject of the clinical entities.
7. Temporal Annotation
Annotating temporal entities from a medical record, helps in building a timeline of the patient’s journey. It provides reference and context to the date associated with a specific event. Here are the date entities – Diagnosis date, Procedure date, Medication start date, Medication end date, Radiation start date, Radiation end date, Date of admission, Date of discharge, Date of consultation, Note date, Onset.
8. Section Annotation
It refers to the process of systematically organizing, labeling, and categorizing different sections or parts of healthcare-related documents, images, or data i.e., annotation of relevant sections from the document and classification of the sections into their respective types. This helps in creating structured and easily accessible information, which can be used for various purposes such as clinical decision support, medical research, and healthcare data analysis.
9. ICD-10-CM & CPT Coding
Annotation of ICD-10-CM and CPT codes according to the guidelines. For each labeled medical code, the evidence (text snippets) that substantiate the labeling decision will be also annotated along with the code.
10. RXNORM Coding
Annotation of RXNORM codes according to the guidelines. For each labeled medical code, the evidence (text snippets) that substantiate the labeling decision will be also annotated along with the code.0
11. SNOMED Coding
Annotation of SNOMED codes according to the guidelines. For each labeled medical code, the evidence (text snippets) that substantiate the labeling decision will be also annotated along with the code.
12. UMLS Coding
Annotation of UMLS codes according to the guidelines. For each labeled medical code, the evidence (text snippets) that substantiate the labeling decision will be also annotated along with the code.
13. PHI De-identification
Our PHI/PII deidentification capabilities include removal of sensitive information such as names and social security numbers that may directly or indirectly connect an individual to their personal data. Its what patients deserve and HIPAA demands.
14. Data Extraction from Electronic Medical Records (EMRs)
Medical practitioners gain significant insight from Electronic Medical Records (EMRs) and physician clinical reports. Our experts can extract complex medical text that can be used in disease registries, clinical trials, and healthcare audits.
Reasons to choose Shaip as your trustworthy Medical Annotation Partner
Dedicated and trained teams:
- 30,000+ collaborators for Data Creation, Labeling & QA
- Credentialed Project Management Team
- Experienced Product Development Team
- Talent Pool Sourcing & Onboarding Team
Highest process efficiency is assured with:
- Robust 6 Sigma Stage-Gate Process
- A dedicated team of 6 Sigma black belts – Key process owners & Quality compliance
- Continuous Improvement & Feedback Loop
The patented platform offers benefits:
- Web-based end-to-end platform
- Impeccable Quality
- Faster TAT
- Seamless Delivery
Looking for Healthcare Annotation Experts for complex projects?
Contact us now to learn how we can collect and annotate dataset for your unique AI/ML solution
Frequently Asked Questions (FAQ)
Named Entity Recognition is a part of Natural Language Processing. The primary objective of NER is to process structured and unstructured data and classify these named entities into predefined categories. Some common categories include name, location, company, time, monetary values, events, and more.
In a nutshell, NER deals with:
Named entity recognition/detection – Identifying a word or series of words in a document.
Named entity classification – Classifying every detected entity into predefined categories.
Natural Language processing helps develop intelligent machines capable of extracting meaning from speech and text. Machine Learning helps these intelligent systems continue learning by training on large amounts of natural language data sets. Generally, NLP consists of three major categories:
Understanding the structure and rules of the language – Syntax
Deriving the meaning of words, text, and speech and identifying their relationships – Semantics
Identifying and recognizing spoken words and transforming them into text – Speech
Some of the common examples of a predetermined entity categorization are:
Person: Michael Jackson, Oprah Winfrey, Barack Obama, Susan Sarandon
Location: Canada, Honolulu, Bangkok, Brazil, Cambridge
Organization: Samsung, Disney, Yale University, Google
Time: 15.35, 12 PM,
The different approaches to creating NER systems are:
Machine learning-based systems
Streamlined Customer Support
Efficient Human Resources
Simplified Content Classification
Optimizing Search Engines
Accurate Content recommendation