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md files and release note updated (#726)
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akrztrk authored Oct 23, 2023
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34 changes: 1 addition & 33 deletions docs/_posts/C-K-Loan/2023-06-17-ner_biomarker_pipeline_en.md
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Expand Up @@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_biomarker](https://nlp.john

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}

```python
from sparknlp.pretrained import PretrainedPipeline

Expand Down Expand Up @@ -62,40 +63,10 @@ nlu.load("en.med_ner.biomarker.pipeline").predict("""Here , we report the first

</div>

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python
from sparknlp.pretrained import PretrainedPipeline

pipeline = PretrainedPipeline("ner_biomarker_pipeline", "en", "clinical/models")

text = '''Here , we report the first case of an intraductal tubulopapillary neoplasm of the pancreas with clear cell morphology . Immunohistochemistry revealed positivity for Pan-CK , CK7 , CK8/18 , MUC1 , MUC6 , carbonic anhydrase IX , CD10 , EMA , β-catenin and e-cadherin '''

result = pipeline.fullAnnotate(text)
```
```scala
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val pipeline = new PretrainedPipeline("ner_biomarker_pipeline", "en", "clinical/models")

val text = "Here , we report the first case of an intraductal tubulopapillary neoplasm of the pancreas with clear cell morphology . Immunohistochemistry revealed positivity for Pan-CK , CK7 , CK8/18 , MUC1 , MUC6 , carbonic anhydrase IX , CD10 , EMA , β-catenin and e-cadherin "

val result = pipeline.fullAnnotate(text)
```

{:.nlu-block}
```python
import nlu
nlu.load("en.med_ner.biomarker.pipeline").predict("""Here , we report the first case of an intraductal tubulopapillary neoplasm of the pancreas with clear cell morphology . Immunohistochemistry revealed positivity for Pan-CK , CK7 , CK8/18 , MUC1 , MUC6 , carbonic anhydrase IX , CD10 , EMA , β-catenin and e-cadherin """)
```
</div>

## Results

```bash
Results


| | ner_chunks | begin | end | ner_label | confidence |
|---:|:-------------------------|--------:|------:|:----------------------|-------------:|
| 0 | intraductal | 38 | 48 | CancerModifier | 0.9998 |
Expand All @@ -114,9 +85,6 @@ Results
| 13 | EMA | 234 | 236 | Biomarker | 0.9985 |
| 14 | β-catenin | 240 | 248 | Biomarker | 0.9948 |
| 15 | e-cadherin | 254 | 263 | Biomarker | 0.9952 |


{:.model-param}
```

{:.model-param}
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74 changes: 1 addition & 73 deletions docs/_posts/C-K-Loan/2023-06-17-oncology_general_pipeline_en.md
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Expand Up @@ -34,6 +34,7 @@ This pipeline includes Named-Entity Recognition, Assertion Status and Relation E

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}

```python
from sparknlp.pretrained import PretrainedPipeline

Expand Down Expand Up @@ -65,78 +66,13 @@ The tumor is positive for ER and PR.""")

</div>

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python
from sparknlp.pretrained import PretrainedPipeline

pipeline = PretrainedPipeline("oncology_general_pipeline", "en", "clinical/models")

text = '''The patient underwent a left mastectomy for a left breast cancer two months ago.
The tumor is positive for ER and PR.'''

result = pipeline.fullAnnotate(text)
```
```scala
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val pipeline = new PretrainedPipeline("oncology_general_pipeline", "en", "clinical/models")

val text = "The patient underwent a left mastectomy for a left breast cancer two months ago.
The tumor is positive for ER and PR."

val result = pipeline.fullAnnotate(text)
```

{:.nlu-block}
```python
import nlu
nlu.load("en.oncology_general.pipeline").predict("""The patient underwent a left mastectomy for a left breast cancer two months ago.
The tumor is positive for ER and PR.""")
```
</div>

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python
from sparknlp.pretrained import PretrainedPipeline

pipeline = PretrainedPipeline("oncology_general_pipeline", "en", "clinical/models")

text = '''The patient underwent a left mastectomy for a left breast cancer two months ago.
The tumor is positive for ER and PR.'''

result = pipeline.fullAnnotate(text)
```
```scala
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val pipeline = new PretrainedPipeline("oncology_general_pipeline", "en", "clinical/models")

val text = "The patient underwent a left mastectomy for a left breast cancer two months ago.
The tumor is positive for ER and PR."

val result = pipeline.fullAnnotate(text)
```

{:.nlu-block}
```python
import nlu
nlu.load("en.oncology_general.pipeline").predict("""The patient underwent a left mastectomy for a left breast cancer two months ago.
The tumor is positive for ER and PR.""")
```
</div>

## Results

```bash
Results


Results


"
******************** ner_oncology_wip results ********************

| chunk | ner_label |
Expand Down Expand Up @@ -217,14 +153,6 @@ Results
| tumor | Tumor_Finding | PR | Biomarker | O |
| positive | Biomarker_Result | ER | Biomarker | is_finding_of |
| positive | Biomarker_Result | PR | Biomarker | is_finding_of |
{:.model-param}
{:.model-param}
```

{:.model-param}
Expand Down
36 changes: 1 addition & 35 deletions docs/_posts/C-K-Loan/2023-06-17-oncology_therapy_pipeline_en.md
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Expand Up @@ -34,6 +34,7 @@ This pipeline includes Named-Entity Recognition and Assertion Status models to e

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}

```python
from sparknlp.pretrained import PretrainedPipeline

Expand Down Expand Up @@ -62,41 +63,10 @@ nlu.load("en.oncology_therpay.pipeline").predict("""The patient underwent a mast

</div>

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python
from sparknlp.pretrained import PretrainedPipeline

pipeline = PretrainedPipeline("oncology_therapy_pipeline", "en", "clinical/models")

text = '''The patient underwent a mastectomy two years ago. She is currently receiving her second cycle of adriamycin and cyclophosphamide, and is in good overall condition.'''

result = pipeline.fullAnnotate(text)
```
```scala
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val pipeline = new PretrainedPipeline("oncology_therapy_pipeline", "en", "clinical/models")

val text = "The patient underwent a mastectomy two years ago. She is currently receiving her second cycle of adriamycin and cyclophosphamide, and is in good overall condition."

val result = pipeline.fullAnnotate(text)
```

{:.nlu-block}
```python
import nlu
nlu.load("en.oncology_therpay.pipeline").predict("""The patient underwent a mastectomy two years ago. She is currently receiving her second cycle of adriamycin and cyclophosphamide, and is in good overall condition.""")
```
</div>

## Results

```bash
Results


"
******************** ner_oncology_wip results ********************

| chunk | ner_label |
Expand Down Expand Up @@ -153,10 +123,6 @@ Results
| mastectomy | Cancer_Surgery | Present_Or_Past |
| adriamycin | Chemotherapy | Present_Or_Past |
| cyclophosphamide | Chemotherapy | Present_Or_Past |
{:.model-param}
```

{:.model-param}
Expand Down
74 changes: 1 addition & 73 deletions docs/_posts/Cabir40/2023-06-16-oncology_general_pipeline_en.md
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Original file line number Diff line number Diff line change
Expand Up @@ -34,6 +34,7 @@ This pipeline includes Named-Entity Recognition, Assertion Status and Relation E

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}

```python
from sparknlp.pretrained import PretrainedPipeline

Expand Down Expand Up @@ -65,78 +66,13 @@ The tumor is positive for ER and PR.""")

</div>

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python
from sparknlp.pretrained import PretrainedPipeline

pipeline = PretrainedPipeline("oncology_general_pipeline", "en", "clinical/models")

text = '''The patient underwent a left mastectomy for a left breast cancer two months ago.
The tumor is positive for ER and PR.'''

result = pipeline.fullAnnotate(text)
```
```scala
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val pipeline = new PretrainedPipeline("oncology_general_pipeline", "en", "clinical/models")

val text = "The patient underwent a left mastectomy for a left breast cancer two months ago.
The tumor is positive for ER and PR."

val result = pipeline.fullAnnotate(text)
```

{:.nlu-block}
```python
import nlu
nlu.load("en.oncology_general.pipeline").predict("""The patient underwent a left mastectomy for a left breast cancer two months ago.
The tumor is positive for ER and PR.""")
```
</div>

<div class="tabs-box" markdown="1">
{% include programmingLanguageSelectScalaPythonNLU.html %}
```python
from sparknlp.pretrained import PretrainedPipeline

pipeline = PretrainedPipeline("oncology_general_pipeline", "en", "clinical/models")

text = '''The patient underwent a left mastectomy for a left breast cancer two months ago.
The tumor is positive for ER and PR.'''

result = pipeline.fullAnnotate(text)
```
```scala
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val pipeline = new PretrainedPipeline("oncology_general_pipeline", "en", "clinical/models")

val text = "The patient underwent a left mastectomy for a left breast cancer two months ago.
The tumor is positive for ER and PR."

val result = pipeline.fullAnnotate(text)
```

{:.nlu-block}
```python
import nlu
nlu.load("en.oncology_general.pipeline").predict("""The patient underwent a left mastectomy for a left breast cancer two months ago.
The tumor is positive for ER and PR.""")
```
</div>

## Results

```bash
Results


Results


"
******************** ner_oncology_wip results ********************

| chunk | ner_label |
Expand Down Expand Up @@ -217,14 +153,6 @@ Results
| tumor | Tumor_Finding | PR | Biomarker | O |
| positive | Biomarker_Result | ER | Biomarker | is_finding_of |
| positive | Biomarker_Result | PR | Biomarker | is_finding_of |
{:.model-param}
{:.model-param}
```

{:.model-param}
Expand Down
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