Detect Radiology Related Entities

Description

Pretrained named entity recognition deep learning model for radiology related texts and reports.

Predicted Entities

ImagingTest, Imaging_Technique, ImagingFindings, OtherFindings, BodyPart, Direction, Test, Symptom, Disease_Syndrome_Disorder, Medical_Device, Procedure, Measurements, Units

Open in Colab Download

How to use

...
radiology_ner = MedicalNerModel.pretrained("ner_radiology", "en", "clinical/models") \
  .setInputCols(["sentence", "token", "embeddings"]) \
  .setOutputCol("ner")
...
nlpPipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, word_embeddings, radiology_ner, ner_converter])

model = nlpPipeline.fit(spark.createDataFrame([['''Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.''']]).toDF("text"))

results = model.transform(data)

Results

|    | chunks                | entities                  |
|----|-----------------------|---------------------------|
| 0  | Bilateral             | Direction                 |
| 1  | breast                | BodyPart                  |
| 2  | ultrasound            | ImagingTest               |
| 3  | ovoid mass            | ImagingFindings           |
| 4  | 0.5 x 0.5 x 0.4       | Measurements              |
| 5  | cm                    | Units                     |
| 6  | anteromedial aspect   | Direction                 |
| 7  | left                  | Direction                 |
| 8  | shoulder              | BodyPart                  |
| 9  | mass                  | ImagingFindings           |
| 10 | isoechoic echotexture | ImagingFindings           |
| 11 | muscle                | BodyPart                  |
| 12 | internal color flow   | ImagingFindings           |
| 13 | benign fibrous tissue | ImagingFindings           |
| 14 | lipoma                | Disease_Syndrome_Disorder |

Model Information

Model Name: ner_radiology
Compatibility: Spark NLP for Healthcare 3.0.0+
License: Licensed
Edition: Official
Input Labels: [sentence, token, embeddings]
Output Labels: [ner]
Language: en

Data Source

Trained on a custom dataset comprising of MIMIC-CXR and MT Radiology texts

Benchmarking

+--------------------+------+-----+-----+------+---------+------+------+
|              entity|    tp|   fp|   fn| total|precision|recall|    f1|
+--------------------+------+-----+-----+------+---------+------+------+
|       OtherFindings|   8.0| 15.0| 63.0|  71.0|   0.3478|0.1127|0.1702|
|        Measurements| 481.0| 30.0| 15.0| 496.0|   0.9413|0.9698|0.9553|
|           Direction| 650.0|137.0| 94.0| 744.0|   0.8259|0.8737|0.8491|
|     ImagingFindings|1345.0|355.0|324.0|1669.0|   0.7912|0.8059|0.7985|
|            BodyPart|1942.0|335.0|290.0|2232.0|   0.8529|0.8701|0.8614|
|      Medical_Device| 236.0| 75.0| 64.0| 300.0|   0.7588|0.7867|0.7725|
|                Test| 222.0| 41.0| 48.0| 270.0|   0.8441|0.8222| 0.833|
|           Procedure| 269.0|117.0|116.0| 385.0|   0.6969|0.6987|0.6978|
|         ImagingTest| 263.0| 50.0| 43.0| 306.0|   0.8403|0.8595|0.8498|
|             Symptom| 498.0|101.0|132.0| 630.0|   0.8314|0.7905|0.8104|
|Disease_Syndrome_...|1180.0|258.0|200.0|1380.0|   0.8206|0.8551|0.8375|
|               Units| 269.0| 10.0|  2.0| 271.0|   0.9642|0.9926|0.9782|
|   Imaging_Technique| 140.0| 38.0| 25.0| 165.0|   0.7865|0.8485|0.8163|
+--------------------+------+-----+-----+------+---------+------+------+

                                                    +------------------+
                                                    |             macro|
                                                    +------------------+
                                                    |0.7524248724038437|
                                                    +------------------+

                                                    +------------------+
                                                    |             micro|
                                                    +------------------+
                                                    |0.8315240382681794|
                                                    +------------------+