Data Annotation for Healthcare AI
Unlock complex information in unstructured data with entity extraction and recognition
Featured Clientes
Permissum teams aedificare, mundum ducens intellegentiae artificialis 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:
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
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:
Tempus 1: Technical domain peritia (Intellectus project scope & annotation guidelines)
Tempus 2: Opportunae facultates exercendae ad documentum
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.
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. Relationship Annotation
After identifying and annotating clinical entities, we also assign relevant relationship among the entities. Relationships may exist between two or more concepts.
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.
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.
Reasons to choose Shaip as your trustworthy Medical Annotation Partner
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
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
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.
commendatur Resources
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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
Frequenter Interrogata De Quaestiones (FAQ)
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