Data Annotation for Healthcare AI

Human-Powered Medical Data Annotation

Unlock complex information in unstructured data with entity extraction and recognition

Medicus Ner

Featured Clientes

Permissum teams aedificare, mundum ducens intellegentiae artificialis products.

Amazon
Google
Microsoft
Cogknit
There’s an increasing demand to analyze unstructured, complex medical data to uncover undiscovered insights. Medical data annotation comes to the rescue

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:

Mundus installed basis repono capacitatis attinget 11.7 zettabytes in 2023

IBM, Gartner & IDC;

80% notitia circa mundum est informis, obsoleta et inutile. 

Verus Solutio in mundo,

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

Verus Solutio in mundo,

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:

Domain Expertise

Tempus 1: Technical domain peritia (Intellectus project scope & annotation guidelines)

Lorem Resources

Tempus 2: Opportunae facultates exercendae ad documentum

Qa Documenta

Tempus 3: Feedback cyclus et QA documentorum annotatorum

nostrum de victoria

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.

Clinical Entity Annotation
Medicine Attributes

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.

Lab Data Attributes
Body Measurement Attributes

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. Relationship Annotation

After identifying and annotating clinical entities, we also assign relevant relationship among the entities. Relationships may exist between two or more concepts.

Relationship Annotation
Adverse Effect Annotation

4. Adverse effect 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.

5. PHI De-identification

PHI noster / Pii deidentification remotionem elit includit numero securitati sociali et ex sensitivo notitia ut nomen illud sit directe vel indirecte personalis notitia ad coniungere per singula. Quod merentur aegris HIPAA sua, et requirat.

De-documenta COGNOSCO Free Text
EMR

6. 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.

7. Status/Negation/Subject

Along with identifying clinical entities and relationships, we can also assign the Status, Negation and Subject of the clinical entities.

Status-Negation-Subject

Reasons to choose Shaip as your trustworthy Medical Annotation Partner

Populus

Populus

Et exercitatus iam dicata teams:

  • Collaborators 30,000+ data est quod creatio, Labeling QA &
  • Credentialed Procuratio Team
  • Peritus Product ipsum Team
  • Fontes Juris talentum stagnum & Onboarding Team
process

process

Eu summo constitit in via:

  • VI-Gate Processus robust Sigma Tempus
  • VI quadrigis a A dedicated nigrum cingulum Sigma - processus Key owners obsequio qualitas &
  • & Continua emendationem videre loop
suggestus

suggestus

De patented suggestus offert beneficia,

  • Web-fundatur, est finis, finis platform
  • Qualitas impeccabilem
  • citius TAT
  • seamless Delivery

Quid Shaip?

Dedicate Team

Aestimatur notitias phisicorum super 80% temporis in praeparatione data consumere. Cum emissa, turma tua progressionem algorithmorum robustorum intendere potest, taedium relinquens partem entis cognitionis nobis datas colligendi.

scalability

Exemplar mediocris ML collectionem requireret et tagging magnas rerum nominatarum datastarum, quae societates ad opes ex aliis iugis traherent. Cum sociis similes nobiscum, peritos domain offerimus qui facile scalis negotium tuum augeri possunt.

melius qualitas

Experts dedicated domain qui annotate sunt in-dies-hodie sunt et voluntas - ullam die - non est superior ad officium comparari cum quadrigis, et necessitates accommodare, ut adnotatio in occupatus opus cedulas. Vanum es dicere hoc magis results output.

operational excellentia

Nostra probata notitia qualitas certitudinis processus, sanationes technologiae et multiplices gradus QA, adiuvat nos libera qualitatem optimae in-classis quae saepe exspectationem excedit.

Securitas cum Privacy

Nos certificati sumus ad servandum summa signa securitatis notitiarum cum secreto dum operando cum clientibus nostris ut secreto

competitive Morbi cursus sapien

Ut periti in curanda, disciplina, ac iunctione administrandi peritorum opificum, incepta curare possumus ut in praevisionibus liberentur.

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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

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Entity Recognitio Nominata pars est Processus Linguae Naturalis. Objectum primarium NER est ad notitias structas et informes processus, et has entias nominatas in categorias praedefinitas classificet. Quaedam genera communia comprehendunt nomen, situm, societatem, tempus, valores nummarios, eventus, et plura.

In nuce NER agit;

Recognitio / deprehensio nominata - Verbum vel seriem verborum in documento cognoscens.

Divisio entitatis nominata – omne entitatem detectam in categoriis praedefinitis indicans.

Naturalis Linguae processus adiuvat ut machinas intelligentes explicant, quae sensum ex oratione et textu extrahunt. Apparatus Discendi adiuvat has disciplinas intellegentias doctrinas perdurare per disciplinas in magna copia notitiarum linguarum naturalium. Fere NLP tribus maioribus generibus constat;

Intelligere structuram et regulas linguae - Syntaxis

Sensum verborum, textuum et locutionum trahunt et earum relationes distinguunt - Semantics

Distinguendi et cognoscendi verba vocum et eas in textum convertendi - Oratio

Exempla quaedam communia categoricae praefiniti entis sunt:

hominem; Michael Jackson, Oprah Winfrey, Barack Obama, Susan Sarandon

Location: Canada, Honolulu, Bangkok, Brazil, Cambridge

Unitarum: Samsung, Disney, Yale University, Google

Tempus: 15.35, 12 PM;

Diversi aditus ad systemata NER creandi sunt:

Dictionarium secundum systemata

Regulae-fundatur systemata

Apparatus doctrina-fundatur systemata

Turpis dui

Humanae Resources efficient

Simplicior Content Classification

Optimizing Engines Quaerere

Accurate Content commendaticiis