From 5dbc3e746aa4759a26617ebda29e998fb59d4c48 Mon Sep 17 00:00:00 2001 From: C-K-Loan Date: Sat, 16 Oct 2021 04:23:33 +0200 Subject: [PATCH 1/4] Updated NLU for healthcare section --- docs/en/nlu_for_healthcare.md | 139 +++++++++++++++++++++++++++------- 1 file changed, 111 insertions(+), 28 deletions(-) diff --git a/docs/en/nlu_for_healthcare.md b/docs/en/nlu_for_healthcare.md index 9d58df0a..b2e5e71b 100644 --- a/docs/en/nlu_for_healthcare.md +++ b/docs/en/nlu_for_healthcare.md @@ -1,7 +1,7 @@ --- layout: docs header: true -title: NLU for Healthcare +title: Healthcare Models and Domains overview key: docs-nlu-for-healthcare permalink: /docs/en/nlu_for_healthcare modify_date: "2019-05-16" @@ -10,14 +10,64 @@ modify_date: "2019-05-16"
-## NLU for Healthcare asd asda sd -### Lol +This page gives you an overview of every healthcare problem and domain that can be solved with NLU for healthcare models, together with concrete +examples. See [this notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/webinars_conferences_etc/healthcare_webinar/NLU_healthcare_webinar.ipynb) +and the accompanying video below for an introduction to every healthcare domain. + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/webinars_conferences_etc/healthcare_webinar/NLU_healthcare_webinar.ipynb)
-## Helath Care Models +## Medical Named Entity Recognition (NER) +**Named entities** are sub-strings in a text that can be classified into catogires of a domain. For example, in the String +`"Tesla is a great stock to invest in "` , the sub-string `"Tesla"` is a named entity, it can be classified with the label `company` by an ML algorithm. +**Named entities** can easily be extracted by the various pre-trained Deep Learning based NER algorithms provided by NLU. +NER models can be trained for many different domains and aquire expert domain knowledge in each of them. JSL provides a wide array of experts for various Medical, Helathcare and Clinical domains + +This algorithm is provided by **Spark NLP for Healthcare's** [MedicalNerModel](https://nlp.johnsnowlabs.com/docs/en/licensed_annotators) + + +{:.table2} +| Domain | Description | Sample NLU Spells | Sample Entities | Sample Predicted Labels | Reference Links +|-------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------| +| [ADE
(Adverse Drug Events)](https://nlp.johnsnowlabs.com/2021/04/01/ner_ade_biobert_en.html) | Find adverse drug event (ADE)
related entities |`med_ner.ade_biobert` | `Aspirin` , `vomiting` | `DRUG`, `ADE` | [CADEC](https://www.sciencedirect.com/science/article/pii/S2352914819300991), [Twimed](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5438461/) | +| [Anatomy](https://nlp.johnsnowlabs.com/2021/04/01/ner_anatomy_biobert_en.html) | Find body parts, anatomical sites a
nd reference
related entities |`med_ner.anatomy` | `tubules`, `nasopharyngeal aspirates`,
`embryoid bodies`, `NK cells`,
`Mitochondrial`, `tracheoesophageal fistulas`,
`heart`, `colon cancer`, `cervical`,
`central nervous system` | `Tissue_structure`, `Organism_substance`,
`Developing_anatomical_structure`, `Cell`,
`Cellular_component`, `Immaterial_anatomical_entity`, `organ`,
`Pathological_formation`,
`Organism_subdivision`, `Anatomical_system` | [AnEM](http://www.nactem.ac.uk/anatomy/)| +| [Cellular/
Molecular Biology](https://nlp.johnsnowlabs.com/2021/04/01/ner_cellular_biobert_en.html) | Find Genes, Molecules, Cell or
general Biology
related entities |`med_ner.cellular.biobert` | `human T-cell leukemia virus type 1 Tax-responsive` ,
`primary T lymphocytes`,
`E1A-immortalized`,
`Spi-B mRNA`, `zeta-globin` | `DNA`, `Cell_type`,
`Cell_line`, `RNA`, `Protein` | [JNLPBA](http://www.geniaproject.org/) +| [Chemical/Genes/
Proteins](https://nlp.johnsnowlabs.com/2021/03/31/ner_chemprot_clinical_en.html) | Find Chemical, Gene and Protein
related entities |`med_ner.chemprot.clinical` | `nitrogen` , `β-amyloid` , `NF-kappaB` | `CHEMICAL`,
`GENE-Y`, `GENE-N` | [ChemProt](https://biocreative.bioinformatics.udel.edu/) +| [Chemical Compounds](https://nlp.johnsnowlabs.com/2021/04/01/ner_chemicals_en.html) | Find general chemical compound
related entities |`med_ner.chemicals` | `resveratrol` , `β-polyphenol` | `CHEM` | [Dataset by John Snow Labs](https://www.johnsnowlabs.com/data/) +| [Drug Chemicals](https://nlp.johnsnowlabs.com/2021/03/31/ner_drugs_en.html) | Find chemical and drug
related entities |`med_ner.drugs` | `potassium` , `anthracyclines`, `taxanes` | `DrugChem`.
`DrugChem`.`DrugChem` | [i2b2 + FDA](https://www.i2b2.org/NLP/Medication) +| [Posology/Drugs](https://nlp.johnsnowlabs.com/2021/04/01/ner_posology_biobert_en.html) | Find posology and drug
related entities |`med_ner.posology.biobert` | `5000 units`, `Aspirin`, `14 days`, `tablets`,
`daily`, `topically`, `30 mg` | `DOSAGE`, `DRUG`, `DURATION`, `FORM`,
`FREQUENCY`, `ROUTE`, `STRENGTH`. | [i2b2 + FDA](https://www.i2b2.org/NLP/Medication) +| [Risk Factors](https://nlp.johnsnowlabs.com/2021/04/01/ner_risk_factors_biobert_en.html) | Find risk factor of patient
related entities |`med_ner.risk_factors.biobert` | `coronary artery disease`, `hypertension`,
`Smokes 2 packs of cigarettes per day`, `morbid obesity`,
`Actos`, `Works in School`, `diabetic`,
`diabetic` | `CAD`, `HYPERTENSION`,
`SMOKER`, `OBESE`,
`FAMILY_HIST`,
`MEDICATION`, `PHI`,
`HYPERLIPIDEMIA`, `DIABETES` | [De-identification and Heart Disease Risk Factors Challenge datasets](https://portal.dbmi.hms.harvard.edu/projects/n2c2-2014/) +| [cancer Genetics](https://nlp.johnsnowlabs.com/2021/03/31/ner_cancer_genetics_en.html) | Find cancer and genetics
related entities |`med_ner.cancer` | `human`, `Kir 3.3`, `GIRK3`, `potassium`,
`GIRK`, `chromosome 1q21-23`, `pancreas`, `tissues`,
`fat andskeletal muscle`, `KCNJ9`, `Type II`,
`breast cancer`,
`patients`, `anthracyclines`,
`taxanes`,
`vinorelbine`, `patients`,
`breast`,
`vinorelbine inpatients`, `anthracyclines` | `Amino_acid`, `Anatomical_system`,
`cancer`,
`Cell`, `Cellular_component`, `Developing_anatomical_Structure`
, `Gene_or_gene_product`, `Immaterial_anatomical_entity`,
`Multi-tissue_structure`, `Organ`, `Organism` ,
`Organism_subdivision`, `Simple_chemical`, `Tissue` | [CG TASK of BioNLP 2013](http://2013.bionlp-st.org/tasks/cancer-genetics) +| [Diseases](https://nlp.johnsnowlabs.com/2021/04/01/ner_diseases_biobert_en.html) | Find disease related entities |`med_ner.diseases.biobert` | `the cyst`, `a large Prolene suture`,
`a very small incisional hernia`, `the hernia cavity`,
`omentum`, `the hernia`, `the wound lesion`,
`The lesion`,
`the existing scar`, `the cyst`,
`the wound`,
`this cyst down to its base`,
`a small incisional hernia`, `The cyst` | `Disease` | [CG TASK of BioNLP 2013](http://2013.bionlp-st.org/tasks/cancer-genetics) +| [Bacterial Species](https://nlp.johnsnowlabs.com/2021/04/01/ner_bacterial_species_en.html) | Find bacterial species
related entities |`med_ner.bacterial_species` | `Neisseria wadsworthii`,
`N. bacilliformis`,
`Spirochaeta litoralis` | `SPECIES` | [Dataset by John Snow Labs](https://www.johnsnowlabs.com/data/) +| [Medical
Problem/Test/Treatment](https://nlp.johnsnowlabs.com/2021/04/21/ner_healthcare_en.html) | Find medical problem,test
and treatment
related entities |`med_ner.healthcare` | `respiratory tract infection` ,
`Ourexpression studies`,
`atorvastatin` | `PROBLEM`, `TEST`, `TREATMENT` | [i2b2](https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/) +| [Clinical
Admission Events](https://nlp.johnsnowlabs.com/2021/03/31/ner_events_admission_clinical_en.html) | Find clinical admission event
related entities |`med_ner.admission_events` | `2007`, `12 AM`,
`Headache`,
`blood sample`,
`presented`,
`emergency room`,
`daily` | `DATE`, `TIME`, `PROBLEM`,
`TEST`, `TREATMENT`, `OCCURENCE`,
`CLINICAL_DEPT`, `EVIDENTIAL`, `DURATION`,
`FREQUENCY`, `ADMISSION`, `DISCHARGE` | [Custom i2b2, enriched with Events](https://www.i2b2.org/NLP/Medication) +| [Genetic Variants](https://nlp.johnsnowlabs.com/2021/06/25/ner_genetic_variants_en.html) | Find genetic variant
related entities |`en.med_ner.genetic_variants` | `rs1061170`, `p.S45P`, `T13046C` | `DNAMutation`, `ProteinMutation`, `SNP` | [TMVAR](https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/tmvar/) +| [PHI (Protected Healthcare
Information)](https://nlp.johnsnowlabs.com/2021/03/31/ner_deidentify_dl_en.html) | Find PHI(Protected Healthcare)
related entities |`en.med_ner.deid` | `2093-01-13`, `David Hale`, `Hendrickson,
Ora`, `7194334`,
`01/13/93`, `Oliveira`,
`25-year-old`, `1-11-2000`, `Cocke County Baptist Hospital`,
`0295 Keats Street.`, `(302) 786-5227`, `Brothers Coal-Mine` | `MEDICALRECORD`,
`ORGANIZATION`, `DOCTOR`,
`USERNAME`, `PROFESSION`,
`HEALTHPLAN`, `URL`, `CITY`,
`DATE`, `LOCATION-OTHER`, `STATE`,
`PATIENT`,
`DEVICE`, `COUNTRY`,
`ZIP`, `PHONE`,
`HOSPITAL`, `EMAIL`, `IDNUM`,
`SREET`, `BIOID`, `FAX`, `AGE` | [n2c2](https://portal.dbmi.hms.harvard.edu/projects/n2c2-2014/) [i2b2-PHI]((https://www.i2b2.org/NLP/)) +| [Social Determinants /
Demographic Data](https://nlp.johnsnowlabs.com/2021/03/31/ner_jsl_enriched_en.html) | Find Social Determinants and
Demographic Data Related Entities |`med_ner.jsl.enriched` |`21-day-old`, `male`, `congestion`,
`mom`, `suctioning yellow discharge`,
`she`, `problems with his breathing`,
`perioral cyanosis`, `retractions`, `mom`,
`Tylenol`, `His`, `his`, `respiratory congestion`,
`He`, `tired`, `fussy`, `albuterol` | `Age`, `Diagnosis`, `Dosage`,
`Drug_Name`, `Frequency`, `Gender`,
`Lab_Name`, `Lab_Result`, `Symptom_Name` | [Dataset by John Snow Labs](https://www.johnsnowlabs.com/data/) + + +## Entity Status Assertion +Named Entities extracted by an NER model can be further classified into sub-classes or statuses, depending on the context of the sentence. See the following two examples : +1. Billy hates having a `headache` +2. Billy has a `headache` +3. Billy said his father has regular `headaches` + +All sentences have the entity `headache` which is of class `disease`. +But there is a semantic difference on what the actual status of the disease mentioned in text is. In the first and third sentence, `Billy has no headache`, but in the second sentence `Billy actually has a sentence`. +The `Entity Assertion` Algorithms provided by JSL solve this problem. The `disease` entity can be classified into `ABSENT` for the first case and into `PRESENT` for the second case. The third case can be classified into `PRESENT IN FAMILY`. +This has immense implications for various data analytical approaches in the helathcare domain. + +I.e. imagine you want you want to make a study about hearth attacks and survival rate of potential procedures. You can process all your digital patient notes with an Medical NER model and filter for documents that have the `Hearth Attack` entity. +But your collected data will have wrong data entries because of the above mentioned Entity status problem. You cannot deduct that a document is talking about a patient having a hearth attack, unless you **assert** that the problem is actually there which is what the Resolutions algorithms do for you. + + +Keep in mind: This is a simplified example, entities should actually be mapped to their according Terminology (ICD-10-CM/ICD-10-PCS, etc..) to solve disambiguity problems and based on their codes all analysis should be performed + +This algorithm is provided by **Spark NLP for Healthcare's** [AssertionDLModel](https://nlp.johnsnowlabs.com/docs/en/licensed_annotators#assertiondl) {:.table-model-big} | nlu.load() Refrence | Spark NLP Refrence | | ------------------------------------------------------------ | ------------------------------------------------------------ | @@ -43,27 +93,6 @@ modify_date: "2019-05-16"
-## Ner Domains Overview WEbi - -{:.table2} -| Domain | Description | Sample NLU Spells | Sample Entities | Sample Predicted Labels | Reference Links -|-------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------| -| [ADE
(Adverse Drug Events)](https://nlp.johnsnowlabs.com/2021/04/01/ner_ade_biobert_en.html) | Find adverse drug event (ADE)
related entities |`med_ner.ade_biobert` | `Aspirin` , `vomiting` | `DRUG`, `ADE` | [CADEC](https://www.sciencedirect.com/science/article/pii/S2352914819300991), [Twimed](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5438461/) | -| [Anatomy](https://nlp.johnsnowlabs.com/2021/04/01/ner_anatomy_biobert_en.html) | Find body parts, anatomical sites a
nd reference
related entities |`med_ner.anatomy` | `tubules`, `nasopharyngeal aspirates`,
`embryoid bodies`, `NK cells`,
`Mitochondrial`, `tracheoesophageal fistulas`,
`heart`, `colon cancer`, `cervical`,
`central nervous system` | `Tissue_structure`, `Organism_substance`,
`Developing_anatomical_structure`, `Cell`,
`Cellular_component`, `Immaterial_anatomical_entity`, `organ`,
`Pathological_formation`,
`Organism_subdivision`, `Anatomical_system` | [AnEM](http://www.nactem.ac.uk/anatomy/)| -| [Cellular/
Molecular Biology](https://nlp.johnsnowlabs.com/2021/04/01/ner_cellular_biobert_en.html) | Find Genes, Molecules, Cell or
general Biology
related entities |`med_ner.cellular.biobert` | `human T-cell leukemia virus type 1 Tax-responsive` ,
`primary T lymphocytes`,
`E1A-immortalized`,
`Spi-B mRNA`, `zeta-globin` | `DNA`, `Cell_type`,
`Cell_line`, `RNA`, `Protein` | [JNLPBA](http://www.geniaproject.org/) -| [Chemical/Genes/
Proteins](https://nlp.johnsnowlabs.com/2021/03/31/ner_chemprot_clinical_en.html) | Find Chemical, Gene and Protein
related entities |`med_ner.chemprot.clinical` | `nitrogen` , `β-amyloid` , `NF-kappaB` | `CHEMICAL`,
`GENE-Y`, `GENE-N` | [ChemProt](https://biocreative.bioinformatics.udel.edu/) -| [Chemical Compounds](https://nlp.johnsnowlabs.com/2021/04/01/ner_chemicals_en.html) | Find general chemical compound
related entities |`med_ner.chemicals` | `resveratrol` , `β-polyphenol` | `CHEM` | [Dataset by John Snow Labs](https://www.johnsnowlabs.com/data/) -| [Drug Chemicals](https://nlp.johnsnowlabs.com/2021/03/31/ner_drugs_en.html) | Find chemical and drug
related entities |`med_ner.drugs` | `potassium` , `anthracyclines`, `taxanes` | `DrugChem`.
`DrugChem`.`DrugChem` | [i2b2 + FDA](https://www.i2b2.org/NLP/Medication) -| [Posology/Drugs](https://nlp.johnsnowlabs.com/2021/04/01/ner_posology_biobert_en.html) | Find posology and drug
related entities |`med_ner.posology.biobert` | `5000 units`, `Aspirin`, `14 days`, `tablets`,
`daily`, `topically`, `30 mg` | `DOSAGE`, `DRUG`, `DURATION`, `FORM`,
`FREQUENCY`, `ROUTE`, `STRENGTH`. | [i2b2 + FDA](https://www.i2b2.org/NLP/Medication) -| [Risk Factors](https://nlp.johnsnowlabs.com/2021/04/01/ner_risk_factors_biobert_en.html) | Find risk factor of patient
related entities |`med_ner.risk_factors.biobert` | `coronary artery disease`, `hypertension`,
`Smokes 2 packs of cigarettes per day`, `morbid obesity`,
`Actos`, `Works in School`, `diabetic`,
`diabetic` | `CAD`, `HYPERTENSION`,
`SMOKER`, `OBESE`,
`FAMILY_HIST`,
`MEDICATION`, `PHI`,
`HYPERLIPIDEMIA`, `DIABETES` | [De-identification and Heart Disease Risk Factors Challenge datasets](https://portal.dbmi.hms.harvard.edu/projects/n2c2-2014/) -| [cancer Genetics](https://nlp.johnsnowlabs.com/2021/03/31/ner_cancer_genetics_en.html) | Find cancer and genetics
related entities |`med_ner.cancer` | `human`, `Kir 3.3`, `GIRK3`, `potassium`,
`GIRK`, `chromosome 1q21-23`, `pancreas`, `tissues`,
`fat andskeletal muscle`, `KCNJ9`, `Type II`,
`breast cancer`,
`patients`, `anthracyclines`,
`taxanes`,
`vinorelbine`, `patients`,
`breast`,
`vinorelbine inpatients`, `anthracyclines` | `Amino_acid`, `Anatomical_system`,
`cancer`,
`Cell`, `Cellular_component`, `Developing_anatomical_Structure`
, `Gene_or_gene_product`, `Immaterial_anatomical_entity`,
`Multi-tissue_structure`, `Organ`, `Organism` ,
`Organism_subdivision`, `Simple_chemical`, `Tissue` | [CG TASK of BioNLP 2013](http://2013.bionlp-st.org/tasks/cancer-genetics) -| [Diseases](https://nlp.johnsnowlabs.com/2021/04/01/ner_diseases_biobert_en.html) | Find disease related entities |`med_ner.diseases.biobert` | `the cyst`, `a large Prolene suture`,
`a very small incisional hernia`, `the hernia cavity`,
`omentum`, `the hernia`, `the wound lesion`,
`The lesion`,
`the existing scar`, `the cyst`,
`the wound`,
`this cyst down to its base`,
`a small incisional hernia`, `The cyst` | `Disease` | [CG TASK of BioNLP 2013](http://2013.bionlp-st.org/tasks/cancer-genetics) -| [Bacterial Species](https://nlp.johnsnowlabs.com/2021/04/01/ner_bacterial_species_en.html) | Find bacterial species
related entities |`med_ner.bacterial_species` | `Neisseria wadsworthii`,
`N. bacilliformis`,
`Spirochaeta litoralis` | `SPECIES` | [Dataset by John Snow Labs](https://www.johnsnowlabs.com/data/) -| [Medical
Problem/Test/Treatment](https://nlp.johnsnowlabs.com/2021/04/21/ner_healthcare_en.html) | Find medical problem,test
and treatment
related entities |`med_ner.healthcare` | `respiratory tract infection` ,
`Ourexpression studies`,
`atorvastatin` | `PROBLEM`, `TEST`, `TREATMENT` | [i2b2](https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/) -| [Clinical
Admission Events](https://nlp.johnsnowlabs.com/2021/03/31/ner_events_admission_clinical_en.html) | Find clinical admission event
related entities |`med_ner.admission_events` | `2007`, `12 AM`,
`Headache`,
`blood sample`,
`presented`,
`emergency room`,
`daily` | `DATE`, `TIME`, `PROBLEM`,
`TEST`, `TREATMENT`, `OCCURENCE`,
`CLINICAL_DEPT`, `EVIDENTIAL`, `DURATION`,
`FREQUENCY`, `ADMISSION`, `DISCHARGE` | [Custom i2b2, enriched with Events](https://www.i2b2.org/NLP/Medication) -| [Genetic Variants](https://nlp.johnsnowlabs.com/2021/06/25/ner_genetic_variants_en.html) | Find genetic variant
related entities |`en.med_ner.genetic_variants` | `rs1061170`, `p.S45P`, `T13046C` | `DNAMutation`, `ProteinMutation`, `SNP` | [TMVAR](https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/tmvar/) -| [PHI (Protected Healthcare
Information)](https://nlp.johnsnowlabs.com/2021/03/31/ner_deidentify_dl_en.html) | Find PHI(Protected Healthcare)
related entities |`en.med_ner.deid` | `2093-01-13`, `David Hale`, `Hendrickson,
Ora`, `7194334`,
`01/13/93`, `Oliveira`,
`25-year-old`, `1-11-2000`, `Cocke County Baptist Hospital`,
`0295 Keats Street.`, `(302) 786-5227`, `Brothers Coal-Mine` | `MEDICALRECORD`,
`ORGANIZATION`, `DOCTOR`,
`USERNAME`, `PROFESSION`,
`HEALTHPLAN`, `URL`, `CITY`,
`DATE`, `LOCATION-OTHER`, `STATE`,
`PATIENT`,
`DEVICE`, `COUNTRY`,
`ZIP`, `PHONE`,
`HOSPITAL`, `EMAIL`, `IDNUM`,
`SREET`, `BIOID`, `FAX`, `AGE` | [n2c2](https://portal.dbmi.hms.harvard.edu/projects/n2c2-2014/) [i2b2-PHI]((https://www.i2b2.org/NLP/)) -| [Social Determinants /
Demographic Data](https://nlp.johnsnowlabs.com/2021/03/31/ner_jsl_enriched_en.html) | Find Social Determinants and
Demographic Data Related Entities |`med_ner.jsl.enriched` |`21-day-old`, `male`, `congestion`,
`mom`, `suctioning yellow discharge`,
`she`, `problems with his breathing`,
`perioral cyanosis`, `retractions`, `mom`,
`Tylenol`, `His`, `his`, `respiratory congestion`,
`He`, `tired`, `fussy`, `albuterol` | `Age`, `Diagnosis`, `Dosage`,
`Drug_Name`, `Frequency`, `Gender`,
`Lab_Name`, `Lab_Result`, `Symptom_Name` | [Dataset by John Snow Labs](https://www.johnsnowlabs.com/data/)
@@ -83,7 +112,28 @@ modify_date: "2019-05-16"
-## Resolution domains WEbi + +## Entity Resolution +**Named entities** are sub-strings in a text that can be classified into catogires of a domain. For example, in the String +`"Tesla is a great stock to invest in "` , the sub-string `"Tesla"` is a named entity, it can be classified with the label `company` by an ML algorithm. +**Named entities** can easily be extracted by the various pre-trained Deep Learning based NER algorithms provided by NLU. + + + +After extracting **named entities** an **entity resolution algorithm** can be applied to the extracted named entities. The resolution algorithm classifies each extracted entitiy into a class, which reduces dimensionality of the data and has many useful applications. +For example : +- "**Tesla** is a great stock to invest in " +- "**TSLA** is a great stock to invest in " +- "**Tesla, Inc** is a great company to invest in" +The sub-strings `Tesla` , `TSLA` and `Tesla, Inc` are all named entities, that are classified with the labeld `company` by the NER algorithm. It tells us, all these 3 sub-strings are of type `company`, but we cannot yet infer that these 3 strings are actually referring to literally the same company. + +This exact problem is solved by the resolver algorithms, it would resolve all these 3 entities to a common name, like a company ID. This maps every reference of Tesla, regardless of how the string is represented, to the same ID. + +This example can analogusly be expanded to healthcare any any other text problems. In medical documents, the same disease can be referenced in many different ways. + +With NLU Healthcare you can leverage state of the art pre-trained NER models to extract **Medical Named Entities** (Diseases, Treatments, Posology, etc..) and **resolve these** to common **healthcare disease codes**. + +This algorithm is provided by **Spark NLP for Healthcare's** [SentenceEntitiyResolver](https://nlp.johnsnowlabs.com/docs/en/licensed_annotators#sentenceentityresolver) {:.table2} | Domain/Terminology | Description | Sample NLU Spells | Sample Entities | Sample Predicted Codes | Reference Links @@ -105,7 +155,40 @@ modify_date: "2019-05-16"
-## Relation Domains +## Entity Relationship Extraction +Most sentences and documents have a lof of `entities` which can be extracted with NER. These entities alone already provide a lot of insight and information about your data, but there is even more information extractable... + +Each `entity` in a sentence always has some kind of `relationship` to every other `entity` in the sentence. In other words, each entity pair has a relationship ! If a sentence has N entities, there are `NxN` potential binary relationships and `NxNxK` for `k-ary relationships`. +The `RelationExtraction` algortihms provided by JSL classify for each pair of entities what the type of relationship between is, based on some domain. + +A concrete use-case example: +Lets say you want to analyze the survival rate of `amputation procedures` performed on the `left hand`. +Using just `NER`, we could find all documents that mention the entity `amputation` , `left` and `hand`. +The collected data will have wrong entries, imagine the following clinical note : + +- The patients `left` `foot` and his `right` `hand` were `amputated` + +This record would be part of our analysis, if we just use `NER` with the above mentioned filtering. +The `RelationExtraction` Algorithms provided by JSL solves this problem. The `relation.bodypart.directions` model can classify for each entity pair, wether they are related or not. +In our example, it can classify that `left` and `foot` are related and that `right` and `hand` are related. Based on these classified relationships, we can easily enhance our filters and make sure no wrong records are used for our surival rate analysis. + +But what about the following sentence? + +- The patients `left` `hand` was saved but his `foot` was `amputated` + +This would pass all the `NER` and `Relationship` filters defined sofar. But we can easily cover this case by using the `relation.bodypart.procedures` model, which can predict wether a procedure entity was peformed on some bodypart or not. In the last example, it can predict `foot` and `amputated` are related, but`hand` and `amputated` are not in relationship, aswell as `left` and `amputated` (since every entity pair gets a prediction). + + +In conclusion, we can adjust our filters to additionaly verify that the `amputation` procedure is peformed on a `hand` and that this `hand` is in relationship with a direction entity with the value `left`. + +Keep in mind: This is a simplified example, entities should actually be mapped to their according Terminology (ICD-10-CM/ICD-10-PCS, etc..) to solve disambiguity problems and based on their codes all analysis should be performed + +These algorithms are provided by **Spark NLP for Healthcare's** [RelationExtraction](https://nlp.johnsnowlabs.com/docs/en/licensed_annotators#relationextraction) and [RelationExtractionDL](https://nlp.johnsnowlabs.com/docs/en/licensed_annotators#relationextractiondl) + + + + +### Entity Relationship Extraction - Overview | Domain | Description | Sample NLU Spells | Predictable Relationships and Explanation |----------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- @@ -125,7 +208,7 @@ modify_date: "2019-05-16"
-## Relation Domain Examples +### Entity Relationship Extraction - Examples | Domain | Sentence With Relationships | Predicted Relationships for Sample Sentence | Reference Links |------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| From 202610c479124a2717b4a1c2e19de8690df57b3b Mon Sep 17 00:00:00 2001 From: C-K-Loan Date: Sat, 16 Oct 2021 04:23:50 +0200 Subject: [PATCH 2/4] Updated Nav Bar --- docs/_data/navigation.yml | 22 ++++++++++++++-------- 1 file changed, 14 insertions(+), 8 deletions(-) diff --git a/docs/_data/navigation.yml b/docs/_data/navigation.yml index 1671774a..9a4fdcc4 100644 --- a/docs/_data/navigation.yml +++ b/docs/_data/navigation.yml @@ -2,20 +2,24 @@ header: - title: Home url: / - title: Docs - url: /docs/en/install + url: https://nlp.johnsnowlabs.com/docs key: docs + - title: NLU for Healthcare + url: /docs/en/nlu_for_healthcare + key: docs-nlu-for-healthcare - title: Visualizations url: /docs/en/viz_examples key: viz - title: Streamlit url: /docs/en/streamlit_viz_examples key: streamlit_viz - - title: Tutorial Notebooks + - title: Tutorials url: /docs/en/notebooks key: tutorial_notebooks - title: Spellbook url: /docs/en/spellbook key: tutorial_notebooks + # - title: Articles # url: /articles # key: articles @@ -29,23 +33,25 @@ header: - title: '' url: https://join.slack.com/t/spark-nlp/shared_invite/zt-lutct9gm-kuUazcyFKhuGY3_0AMkxqA docs-en: - - title: Getting Started + - title: NLU children: - - title: Quick Start + - title: Installation url: /docs/en/install - - title: General Concepts + - title: Usage url: /docs/en/concepts - - title: Examples + - title: General Examples url: /docs/en/examples + - title: NLU for Healthcare + url: /docs/en/nlu_for_healthcare - title: NLU for Healthcare Examples url: /docs/en/examples_hc - title: Training Models url: /docs/en/training - - title: Notebook tutorials + - title: Tutorial Notebooks url: /docs/en/notebooks - title: Visualization Examples url: /docs/en/viz_examples - - title: Streamlit Visualization + - title: Streamlit Visualizations url: /docs/en/streamlit_viz_examples key: streamlit_viz - title: Spellbook From e007dd1a0f8c723306317ebe868fee9d495bb217 Mon Sep 17 00:00:00 2001 From: C-K-Loan Date: Sat, 16 Oct 2021 04:24:46 +0200 Subject: [PATCH 3/4] Updated Nav Bar --- .github/workflows/nlu_test_flow.yaml | 2 +- docs/en/examples_healthcare.md | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/nlu_test_flow.yaml b/.github/workflows/nlu_test_flow.yaml index 59ce8fe1..f70f7caf 100644 --- a/.github/workflows/nlu_test_flow.yaml +++ b/.github/workflows/nlu_test_flow.yaml @@ -28,7 +28,7 @@ jobs: run: | python -m pip install --upgrade pip pip install pypandoc sklearn - pip install wheel dataclasses pandas numpy pytest modin[ray] pyspark==3.0.1 spark-nlp + pip install wheel dataclasses pandas numpy pytest modin[ray] pyspark==3.1.2 spark-nlp java -version if [ -f requirements.txt ]; then pip install -r requirements.txt; fi # ! echo 2 | update-alternatives --config java diff --git a/docs/en/examples_healthcare.md b/docs/en/examples_healthcare.md index 298a81b6..0db563aa 100644 --- a/docs/en/examples_healthcare.md +++ b/docs/en/examples_healthcare.md @@ -6,7 +6,6 @@ key: docs-examples-hc permalink: /docs/en/examples_hc modify_date: "2019-05-16" --- -
From 79c95704f8ecfebc5166d8ad53ce96397de395e3 Mon Sep 17 00:00:00 2001 From: C-K-Loan Date: Sun, 17 Oct 2021 04:24:13 +0200 Subject: [PATCH 4/4] Fixed table structure --- docs/en/nlu_for_healthcare.md | 30 +++++++----------------------- 1 file changed, 7 insertions(+), 23 deletions(-) diff --git a/docs/en/nlu_for_healthcare.md b/docs/en/nlu_for_healthcare.md index b2e5e71b..fe3dd8b9 100644 --- a/docs/en/nlu_for_healthcare.md +++ b/docs/en/nlu_for_healthcare.md @@ -48,6 +48,13 @@ This algorithm is provided by **Spark NLP for Healthcare's** [MedicalNerModel]( | [Genetic Variants](https://nlp.johnsnowlabs.com/2021/06/25/ner_genetic_variants_en.html) | Find genetic variant
related entities |`en.med_ner.genetic_variants` | `rs1061170`, `p.S45P`, `T13046C` | `DNAMutation`, `ProteinMutation`, `SNP` | [TMVAR](https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/tmvar/) | [PHI (Protected Healthcare
Information)](https://nlp.johnsnowlabs.com/2021/03/31/ner_deidentify_dl_en.html) | Find PHI(Protected Healthcare)
related entities |`en.med_ner.deid` | `2093-01-13`, `David Hale`, `Hendrickson,
Ora`, `7194334`,
`01/13/93`, `Oliveira`,
`25-year-old`, `1-11-2000`, `Cocke County Baptist Hospital`,
`0295 Keats Street.`, `(302) 786-5227`, `Brothers Coal-Mine` | `MEDICALRECORD`,
`ORGANIZATION`, `DOCTOR`,
`USERNAME`, `PROFESSION`,
`HEALTHPLAN`, `URL`, `CITY`,
`DATE`, `LOCATION-OTHER`, `STATE`,
`PATIENT`,
`DEVICE`, `COUNTRY`,
`ZIP`, `PHONE`,
`HOSPITAL`, `EMAIL`, `IDNUM`,
`SREET`, `BIOID`, `FAX`, `AGE` | [n2c2](https://portal.dbmi.hms.harvard.edu/projects/n2c2-2014/) [i2b2-PHI]((https://www.i2b2.org/NLP/)) | [Social Determinants /
Demographic Data](https://nlp.johnsnowlabs.com/2021/03/31/ner_jsl_enriched_en.html) | Find Social Determinants and
Demographic Data Related Entities |`med_ner.jsl.enriched` |`21-day-old`, `male`, `congestion`,
`mom`, `suctioning yellow discharge`,
`she`, `problems with his breathing`,
`perioral cyanosis`, `retractions`, `mom`,
`Tylenol`, `His`, `his`, `respiratory congestion`,
`He`, `tired`, `fussy`, `albuterol` | `Age`, `Diagnosis`, `Dosage`,
`Drug_Name`, `Frequency`, `Gender`,
`Lab_Name`, `Lab_Result`, `Symptom_Name` | [Dataset by John Snow Labs](https://www.johnsnowlabs.com/data/) +| [General Clinical](https://nlp.johnsnowlabs.com/2021/04/01/jsl_ner_wip_modifier_clinical_en.html) | Find General Clinical Entities |`med_ner.jsl.wip.clinical.modifier` | `28-year-old`, `female`,
`gestational`, `diabetes`,
`mellitus`, `eight`, `years`,
`prior`, `type`,
`two`, `diabetes`, `mellitus`, `T2DM`,
`HTG-induced`, `pancreatitis`,
`three`, `years`,
`prior`, `acute`,
`hepatitis`, `obesity`,
`body`, `mass`, `index`,
`BMI`,
`kg/m2`, `polyuria`,
`polydipsia`,
`poor`,
`appetite`, `vomiting`,
`Two`,
`weeks`, `prior`,
`she`, `five-day`, `course` | `Injury_or_Poisoning`, `Direction`,
`Test`, `Admission_Discharge`, `Death_Entity`,
`Relationship_Status`, `Duration`, `Respiration`,
`Hyperlipidemia`, `Birth_Entity`, `Age`, `Labour_Delivery`,
`Family_History_Header`, `BMI`, `Temperature`,
`Alcohol`,
`Kidney_Disease`, `Oncological`,
`Medical_History_Header`, `Cerebrovascular_Disease`, `Oxygen_Therapy`,
`O2_Saturation`, `Psychological_Condition`,
`Heart_Disease`, `Employment`, `Obesity`,
`Disease_Syndrome_Disorder`, `Pregnancy`,
`ImagingFindings`, `Procedure`,
`Medical_Device`, `Race_Ethnicity`,
`Section_Header`, `Symptom`,
`Treatment`, `Substance`,
`Route`, `Drug_Ingredient`,
`Blood_Pressure`, `Diet`,
`External_body_part_or_region`,
`LDL`, `VS_Finding`, `Allergen`,
`EKG_Findings`, `Imaging_Technique`, `Triglycerides`,
`RelativeTime`, `Gender`, `Pulse`,
`Social_History_Header`, `Substance_Quantity`,
`Diabetes`, `Modifier`,
`Internal_organ_or_component`,
`Clinical_Dept`, `Form`, `Drug_BrandName`,
`Strength`, `Fetus_NewBorn`,
`RelativeDate`, `Height`, `Test_Result`,
`Sexually_Active_or_Sexual_Orientation`, `Frequency`, `Time`,
`Weight`, `Vaccine`,
`Vital_Signs_Header`,
`Communicable_Disease`, `Dosage`,
`Overweight`, `Hypertension`,
`HDL`, `Total_Cholesterol`, `Smoking`, ` | [Dataset by John Snow Labs](https://www.johnsnowlabs.com/data/) +| [Radiology](https://nlp.johnsnowlabs.com/2021/04/01/ner_radiology_wip_clinical_en.html) | Find Radiology
related entities |`med_ner.radiology.wip_clinical` | `Bilateral`, `breast`, `ultrasound`,
`ovoid mass`,
`0.5 x 0.5 x 0.4`, `cm`, `anteromedial aspect`,
`left`, `shoulder`, `mass`,
`isoechoic echotexture`, `muscle`,
`internal color flow`,
`benign fibrous tissue`, `lipoma` | `ImagingTest`, `Imaging_Technique`, `ImagingFindings`, `OtherFindings`, `BodyPart`, `Direction`, `Test`,
`Symptom`, `Disease_Syndrome_Disorder`,
`Medical_Device`, `Procedure`, `Measurements`, `Units` | [Dataset by John Snow Labs](https://www.johnsnowlabs.com/data/), [MIMIC-CXR and MT Radiology texts](https://physionet.org/content/mimic-cxr-jpg/2.0.0/) +| [Radiology Clinical
JSL-V1](https://nlp.johnsnowlabs.com/2021/07/26/jsl_rd_ner_wip_greedy_biobert_en.html) | Find radiology
related entities in clinical setting |`med_ner.radiology.wip_greedy_biobert` | `Bilateral`, `breast`, `ultrasound`,
`ovoid mass`,
`0.5 x 0.5 x 0.4`, `cm`, `anteromedial aspect`,
`left`, `shoulder`, `mass`,
`isoechoic echotexture`, `muscle`,
`internal color flow`,
`benign fibrous tissue`, `lipoma` | `Test_Result`, `OtherFindings`, `BodyPart`, `ImagingFindings`,
`Disease_Syndrome_Disorder`, `ImagingTest`,
`Measurements`, `Procedure`,
`Score`, `Test`, `Medical_Device`, `Direction`,
`Symptom`, `Imaging_Technique`, `ManualFix`, `Units` | [Dataset by John Snow Labs](https://www.johnsnowlabs.com/data/), +| [Genes
and Phenotypes](https://nlp.johnsnowlabs.com/2021/04/01/ner_human_phenotype_gene_biobert_en.html) | Find Genes and Phenotypes
(the observable physical
properties of an organism) related entities |`med_ner.human_phenotype.gene_biobert` | `APOC4` , `polyhydramnios` | `GENE`, `PHENOTYPE` | [PGR_1](https://aclweb.org/anthology/papers/N/N19/N19-1152/), [PGR_2](https://github.com/lasigeBioTM/PGR) | +| [Normalized Genes
and Phenotypes](https://nlp.johnsnowlabs.com/2021/04/01/ner_human_phenotype_go_biobert_en.html) | Find Normalized Genes and Phenotypes
(the observable physical
properties of an organism)
related entities |`med_ner.human_phenotype.go_biobert` | `protein complex oligomerization` , `defective platelet aggregation` | `GO`, `HP` | [PGR_1](https://aclweb.org/anthology/papers/N/N19/N19-1152/), [PGR_2](https://github.com/lasigeBioTM/PGR) | +| [Radiology Clinical
JSL-V2](https://nlp.johnsnowlabs.com/2021/04/01/jsl_rd_ner_wip_greedy_clinical_en.html) | Find radiology
related entities in clinical setting |`med_ner.jsl.wip.clinical.rd` | | `Kidney_Disease`, `HDL`, `Diet`, `Test`, `Imaging_Technique`,
`Triglycerides`, `Obesity`, `Duration`, `Weight`,
`Social_History_Header`, `ImagingTest`, `Labour_Delivery`,
`Disease_Syndrome_Disorder`,
`Communicable_Disease`, `Overweight`,
`Units`, `Smoking`,
`Score`, `Substance_Quantity`,
`Form`, `Race_Ethnicity`,
`Modifier`, `Hyperlipidemia`, `ImagingFindings`,
`Psychological_Condition`, `OtherFindings`,
`Cerebrovascular_Disease`, `Date`, `Test_Result`,
`VS_Finding`, `Employment`,
`Death_Entity`, `Gender`, `Oncological`,
`Heart_Disease`, `Medical_Device`,
`Total_Cholesterol`, `ManualFix`,
`Time`, `Route`, `Pulse`,
`Admission_Discharge`, `RelativeDate`
, `O2_Saturation`, `Frequency`,
`RelativeTime`, `Hypertension`, `Alcohol`,
`Allergen`, `Fetus_NewBorn`,
`Birth_Entity`, `Age`,
`Respiration`, `Medical_History_Header`,
`Oxygen_Therapy`, `Section_Header`, `LDL`,
`Treatment`, `Vital_Signs_Header`, `Direction`,
`BMI`, `Pregnancy`,
`Sexually_Active_or_Sexual_Orientation`, `Symptom`,
`Clinical_Dept`, `Measurements`,
`Height`, `Family_History_Header`,
`Substance`,
`Strength`,
`Injury_or_Poisoning`,
`Relationship_Status`,
`Blood_Pressure`, `Drug`, `Temperature, ` ,
`EKG_Findings`, `Diabetes`, `BodyPart`,
`Vaccine`, `Procedure`, `Dosage` | [Dataset by John Snow Labs](https://www.johnsnowlabs.com/data/), +| [General
Medical Terms ](https://nlp.johnsnowlabs.com/2021/04/01/ner_medmentions_coarse_en.html) | Find general medical terms
and medical entities. |`med_ner.medmentions` | | `Qualitative_Concept`, `Organization`, `Manufactured_Object`,
`Amino_Acid`, `Peptide_or_Protein`,
`Pharmacologic_Substance`, `Professional_or_Occupational_Group`,
`Cell_Component`, `Neoplastic_Process`, `Substance`, `Laboratory_Procedure`,
`Nucleic_Acid_Nucleoside_or_Nucleotide`,
`Research_Activity`, `Gene_or_Genome`, `Indicator_Reagent_or_Diagnostic_Aid`,
`Biologic_Function`, `Chemical`, `Mammal`,
`Molecular_Function`, `Quantitative_Concept`,
`Prokaryote`, `Mental_or_Behavioral_Dysfunction`,
`Injury_or_Poisoning`, `Body_Location_or_Region`,
`Spatial_Concept`, `Nucleotide_Sequence`,
`Tissue`, `Pathologic_Function`,
`Body_Substance`, `Fungus`, `Mental_Process`,
`Medical_Device`, `Plant`, `Health_Care_Activity`,
`Clinical_Attribute`, `Genetic_Function`,
`Food`, `Therapeutic_or_Preventive_Procedure`,
`Body_Part_Organ`,
`Organ_Component`, `Geographic_Area`, `Virus`,
`Biomedical_or_Dental_Material`, `Diagnostic_Procedure`, `Eukaryote`,
`Anatomical_Structure`, `Organism_Attribute`,
`Molecular_Biology_Research_Technique`, `Organic_Chemical`, `Cell`,
`Daily_or_Recreational_Activity`,
`Population_Group`, `Disease_or_Syndrome`,
`Group`, `Sign_or_Symptom`, `Body_System` | [MedMentions](https://arxiv.org/abs/1902.09476) ## Entity Status Assertion @@ -68,16 +75,6 @@ But your collected data will have wrong data entries because of the above mentio Keep in mind: This is a simplified example, entities should actually be mapped to their according Terminology (ICD-10-CM/ICD-10-PCS, etc..) to solve disambiguity problems and based on their codes all analysis should be performed This algorithm is provided by **Spark NLP for Healthcare's** [AssertionDLModel](https://nlp.johnsnowlabs.com/docs/en/licensed_annotators#assertiondl) -{:.table-model-big} -| nlu.load() Refrence | Spark NLP Refrence | - | ------------------------------------------------------------ | ------------------------------------------------------------ | -| [en.resolve.snomed_conditions](https://nlp.johnsnowlabs.com//2021/08/28/sbertresolve_snomed_conditions_en.html) | [sbertresolve_snomed_conditions](https://nlp.johnsnowlabs.com//2021/08/28/sbertresolve_snomed_conditions_en.html) | -| [en.resolve.cpt.procedures_measurements](https://nlp.johnsnowlabs.com//2021/07/02/sbiobertresolve_cpt_procedures_measurements_augmented_en.html) | [sbiobertresolve_cpt_procedures_measurements_augmented](https://nlp.johnsnowlabs.com//2021/07/02/sbiobertresolve_cpt_procedures_measurements_augmented_en.html) | -| [en.resolve.icdo.base](https://nlp.johnsnowlabs.com//2021/07/02/sbiobertresolve_icdo_base_en.html) | [sbiobertresolve_icdo_base](https://nlp.johnsnowlabs.com//2021/07/02/sbiobertresolve_icdo_base_en.html) | -| [en.resolve.rxnorm.disposition.sbert](https://nlp.johnsnowlabs.com//2021/08/28/sbertresolve_rxnorm_disposition_en.html) | [sbertresolve_rxnorm_disposition](https://nlp.johnsnowlabs.com//2021/08/28/sbertresolve_rxnorm_disposition_en.html) | -| [en.resolve.rxnorm_disposition.sbert](https://nlp.johnsnowlabs.com//2021/08/28/sbertresolve_rxnorm_disposition_en.html) | [sbertresolve_rxnorm_disposition](https://nlp.johnsnowlabs.com//2021/08/28/sbertresolve_rxnorm_disposition_en.html) | -| [en.med_ner.posology.experimental](https://nlp.johnsnowlabs.com//2021/09/01/ner_posology_experimental_en.html) | [ner_posology_experimental](https://nlp.johnsnowlabs.com//2021/09/01/ner_posology_experimental_en.html) | -| [en.med_ner.deid.subentity_augmented](https://nlp.johnsnowlabs.com//2021/09/03/ner_deid_subentity_augmented_en.html) | [ner_deid_subentity_augmented](https://nlp.johnsnowlabs.com//2021/09/03/ner_deid_subentity_augmented_en.html) |
@@ -96,20 +93,7 @@ This algorithm is provided by **Spark NLP for Healthcare's** [AssertionDLModel]
-
- -| Domain | Description | Sample NLU Spells | Sample Entities | Sample Predicted Labels | Reference Links -|-------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------| -| [General Clinical](https://nlp.johnsnowlabs.com/2021/04/01/jsl_ner_wip_modifier_clinical_en.html) | Find General Clinical Entities |`med_ner.jsl.wip.clinical.modifier` | `28-year-old`, `female`,
`gestational`, `diabetes`,
`mellitus`, `eight`, `years`,
`prior`, `type`,
`two`, `diabetes`, `mellitus`, `T2DM`,
`HTG-induced`, `pancreatitis`,
`three`, `years`,
`prior`, `acute`,
`hepatitis`, `obesity`,
`body`, `mass`, `index`,
`BMI`,
`kg/m2`, `polyuria`,
`polydipsia`,
`poor`,
`appetite`, `vomiting`,
`Two`,
`weeks`, `prior`,
`she`, `five-day`, `course` | `Injury_or_Poisoning`, `Direction`,
`Test`, `Admission_Discharge`, `Death_Entity`,
`Relationship_Status`, `Duration`, `Respiration`,
`Hyperlipidemia`, `Birth_Entity`, `Age`, `Labour_Delivery`,
`Family_History_Header`, `BMI`, `Temperature`,
`Alcohol`,
`Kidney_Disease`, `Oncological`,
`Medical_History_Header`, `Cerebrovascular_Disease`, `Oxygen_Therapy`,
`O2_Saturation`, `Psychological_Condition`,
`Heart_Disease`, `Employment`, `Obesity`,
`Disease_Syndrome_Disorder`, `Pregnancy`,
`ImagingFindings`, `Procedure`,
`Medical_Device`, `Race_Ethnicity`,
`Section_Header`, `Symptom`,
`Treatment`, `Substance`,
`Route`, `Drug_Ingredient`,
`Blood_Pressure`, `Diet`,
`External_body_part_or_region`,
`LDL`, `VS_Finding`, `Allergen`,
`EKG_Findings`, `Imaging_Technique`, `Triglycerides`,
`RelativeTime`, `Gender`, `Pulse`,
`Social_History_Header`, `Substance_Quantity`,
`Diabetes`, `Modifier`,
`Internal_organ_or_component`,
`Clinical_Dept`, `Form`, `Drug_BrandName`,
`Strength`, `Fetus_NewBorn`,
`RelativeDate`, `Height`, `Test_Result`,
`Sexually_Active_or_Sexual_Orientation`, `Frequency`, `Time`,
`Weight`, `Vaccine`,
`Vital_Signs_Header`,
`Communicable_Disease`, `Dosage`,
`Overweight`, `Hypertension`,
`HDL`, `Total_Cholesterol`, `Smoking`, ` | [Dataset by John Snow Labs](https://www.johnsnowlabs.com/data/) -| [Radiology](https://nlp.johnsnowlabs.com/2021/04/01/ner_radiology_wip_clinical_en.html) | Find Radiology
related entities |`med_ner.radiology.wip_clinical` | `Bilateral`, `breast`, `ultrasound`,
`ovoid mass`,
`0.5 x 0.5 x 0.4`, `cm`, `anteromedial aspect`,
`left`, `shoulder`, `mass`,
`isoechoic echotexture`, `muscle`,
`internal color flow`,
`benign fibrous tissue`, `lipoma` | `ImagingTest`, `Imaging_Technique`, `ImagingFindings`, `OtherFindings`, `BodyPart`, `Direction`, `Test`,
`Symptom`, `Disease_Syndrome_Disorder`,
`Medical_Device`, `Procedure`, `Measurements`, `Units` | [Dataset by John Snow Labs](https://www.johnsnowlabs.com/data/), [MIMIC-CXR and MT Radiology texts](https://physionet.org/content/mimic-cxr-jpg/2.0.0/) -| [Radiology Clinical
JSL-V1](https://nlp.johnsnowlabs.com/2021/07/26/jsl_rd_ner_wip_greedy_biobert_en.html) | Find radiology
related entities in clinical setting |`med_ner.radiology.wip_greedy_biobert` | `Bilateral`, `breast`, `ultrasound`,
`ovoid mass`,
`0.5 x 0.5 x 0.4`, `cm`, `anteromedial aspect`,
`left`, `shoulder`, `mass`,
`isoechoic echotexture`, `muscle`,
`internal color flow`,
`benign fibrous tissue`, `lipoma` | `Test_Result`, `OtherFindings`, `BodyPart`, `ImagingFindings`,
`Disease_Syndrome_Disorder`, `ImagingTest`,
`Measurements`, `Procedure`,
`Score`, `Test`, `Medical_Device`, `Direction`,
`Symptom`, `Imaging_Technique`, `ManualFix`, `Units` | [Dataset by John Snow Labs](https://www.johnsnowlabs.com/data/), -| [Genes
and Phenotypes](https://nlp.johnsnowlabs.com/2021/04/01/ner_human_phenotype_gene_biobert_en.html) | Find Genes and Phenotypes
(the observable physical
properties of an organism) related entities |`med_ner.human_phenotype.gene_biobert` | `APOC4` , `polyhydramnios` | `GENE`, `PHENOTYPE` | [PGR_1](https://aclweb.org/anthology/papers/N/N19/N19-1152/), [PGR_2](https://github.com/lasigeBioTM/PGR) | -| [Normalized Genes
and Phenotypes](https://nlp.johnsnowlabs.com/2021/04/01/ner_human_phenotype_go_biobert_en.html) | Find Normalized Genes and Phenotypes
(the observable physical
properties of an organism)
related entities |`med_ner.human_phenotype.go_biobert` | `protein complex oligomerization` , `defective platelet aggregation` | `GO`, `HP` | [PGR_1](https://aclweb.org/anthology/papers/N/N19/N19-1152/), [PGR_2](https://github.com/lasigeBioTM/PGR) | -| [Radiology Clinical
JSL-V2](https://nlp.johnsnowlabs.com/2021/04/01/jsl_rd_ner_wip_greedy_clinical_en.html) | Find radiology
related entities in clinical setting |`med_ner.jsl.wip.clinical.rd` | | `Kidney_Disease`, `HDL`, `Diet`, `Test`, `Imaging_Technique`,
`Triglycerides`, `Obesity`, `Duration`, `Weight`,
`Social_History_Header`, `ImagingTest`, `Labour_Delivery`,
`Disease_Syndrome_Disorder`,
`Communicable_Disease`, `Overweight`,
`Units`, `Smoking`,
`Score`, `Substance_Quantity`,
`Form`, `Race_Ethnicity`,
`Modifier`, `Hyperlipidemia`, `ImagingFindings`,
`Psychological_Condition`, `OtherFindings`,
`Cerebrovascular_Disease`, `Date`, `Test_Result`,
`VS_Finding`, `Employment`,
`Death_Entity`, `Gender`, `Oncological`,
`Heart_Disease`, `Medical_Device`,
`Total_Cholesterol`, `ManualFix`,
`Time`, `Route`, `Pulse`,
`Admission_Discharge`, `RelativeDate`
, `O2_Saturation`, `Frequency`,
`RelativeTime`, `Hypertension`, `Alcohol`,
`Allergen`, `Fetus_NewBorn`,
`Birth_Entity`, `Age`,
`Respiration`, `Medical_History_Header`,
`Oxygen_Therapy`, `Section_Header`, `LDL`,
`Treatment`, `Vital_Signs_Header`, `Direction`,
`BMI`, `Pregnancy`,
`Sexually_Active_or_Sexual_Orientation`, `Symptom`,
`Clinical_Dept`, `Measurements`,
`Height`, `Family_History_Header`,
`Substance`,
`Strength`,
`Injury_or_Poisoning`,
`Relationship_Status`,
`Blood_Pressure`, `Drug`, `Temperature, ` ,
`EKG_Findings`, `Diabetes`, `BodyPart`,
`Vaccine`, `Procedure`, `Dosage` | [Dataset by John Snow Labs](https://www.johnsnowlabs.com/data/), -| [General
Medical Terms ](https://nlp.johnsnowlabs.com/2021/04/01/ner_medmentions_coarse_en.html) | Find general medical terms
and medical entities. |`med_ner.medmentions` | | `Qualitative_Concept`, `Organization`, `Manufactured_Object`,
`Amino_Acid`, `Peptide_or_Protein`,
`Pharmacologic_Substance`, `Professional_or_Occupational_Group`,
`Cell_Component`, `Neoplastic_Process`, `Substance`, `Laboratory_Procedure`,
`Nucleic_Acid_Nucleoside_or_Nucleotide`,
`Research_Activity`, `Gene_or_Genome`, `Indicator_Reagent_or_Diagnostic_Aid`,
`Biologic_Function`, `Chemical`, `Mammal`,
`Molecular_Function`, `Quantitative_Concept`,
`Prokaryote`, `Mental_or_Behavioral_Dysfunction`,
`Injury_or_Poisoning`, `Body_Location_or_Region`,
`Spatial_Concept`, `Nucleotide_Sequence`,
`Tissue`, `Pathologic_Function`,
`Body_Substance`, `Fungus`, `Mental_Process`,
`Medical_Device`, `Plant`, `Health_Care_Activity`,
`Clinical_Attribute`, `Genetic_Function`,
`Food`, `Therapeutic_or_Preventive_Procedure`,
`Body_Part_Organ`,
`Organ_Component`, `Geographic_Area`, `Virus`,
`Biomedical_or_Dental_Material`, `Diagnostic_Procedure`, `Eukaryote`,
`Anatomical_Structure`, `Organism_Attribute`,
`Molecular_Biology_Research_Technique`, `Organic_Chemical`, `Cell`,
`Daily_or_Recreational_Activity`,
`Population_Group`, `Disease_or_Syndrome`,
`Group`, `Sign_or_Symptom`, `Body_System` | [MedMentions](https://arxiv.org/abs/1902.09476) - -