Med7: a transferable clinical natural language processing model for electronic health records
The field of clinical natural language processing has been advanced significantly since the introduction of deep learning models. The self-supervised representation learning and the transfer learning paradigm became the methods of choice in many natural language processing application, in particular...
| Main Authors: | , , , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
Schloss Dagstuhl
2020
|
| _version_ | 1797053758985732096 |
|---|---|
| author | Kormilitzin, A Vaci, N Liu, Q Nevado-Holgado, A |
| author_facet | Kormilitzin, A Vaci, N Liu, Q Nevado-Holgado, A |
| author_sort | Kormilitzin, A |
| collection | OXFORD |
| description | The field of clinical natural language processing has been advanced
significantly since the introduction of deep learning models. The
self-supervised representation learning and the transfer learning paradigm
became the methods of choice in many natural language processing application,
in particular in the settings with the dearth of high quality manually
annotated data. Electronic health record systems are ubiquitous and the
majority of patients' data are now being collected electronically and in
particular in the form of free text. Identification of medical concepts and
information extraction is a challenging task, yet important ingredient for
parsing unstructured data into structured and tabulated format for downstream
analytical tasks. In this work we introduced a named-entity recognition model
for clinical natural language processing. The model is trained to recognise
seven categories: drug names, route, frequency, dosage, strength, form,
duration. The model was first self-supervisedly pre-trained by predicting the
next word, using a collection of 2 million free-text patients' records from
MIMIC-III corpora and then fine-tuned on the named-entity recognition task. The
model achieved a lenient (strict) micro-averaged F1 score of 0.957 (0.893)
across all seven categories. Additionally, we evaluated the transferability of
the developed model using the data from the Intensive Care Unit in the US to
secondary care mental health records (CRIS) in the UK. A direct application of
the trained NER model to CRIS data resulted in reduced performance of F1=0.762,
however after fine-tuning on a small sample from CRIS, the model achieved a
reasonable performance of F1=0.944. This demonstrated that despite a close
similarity between the data sets and the NER tasks, it is essential to
fine-tune on the target domain data in order to achieve more accurate results. |
| first_indexed | 2024-03-06T18:48:12Z |
| format | Journal article |
| id | oxford-uuid:0f44c54c-af53-47be-9b84-bfe47fa4f8c8 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T18:48:12Z |
| publishDate | 2020 |
| publisher | Schloss Dagstuhl |
| record_format | dspace |
| spelling | oxford-uuid:0f44c54c-af53-47be-9b84-bfe47fa4f8c82022-03-26T09:50:24ZMed7: a transferable clinical natural language processing model for electronic health recordsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:0f44c54c-af53-47be-9b84-bfe47fa4f8c8EnglishSymplectic ElementsSchloss Dagstuhl2020Kormilitzin, AVaci, NLiu, QNevado-Holgado, AThe field of clinical natural language processing has been advanced significantly since the introduction of deep learning models. The self-supervised representation learning and the transfer learning paradigm became the methods of choice in many natural language processing application, in particular in the settings with the dearth of high quality manually annotated data. Electronic health record systems are ubiquitous and the majority of patients' data are now being collected electronically and in particular in the form of free text. Identification of medical concepts and information extraction is a challenging task, yet important ingredient for parsing unstructured data into structured and tabulated format for downstream analytical tasks. In this work we introduced a named-entity recognition model for clinical natural language processing. The model is trained to recognise seven categories: drug names, route, frequency, dosage, strength, form, duration. The model was first self-supervisedly pre-trained by predicting the next word, using a collection of 2 million free-text patients' records from MIMIC-III corpora and then fine-tuned on the named-entity recognition task. The model achieved a lenient (strict) micro-averaged F1 score of 0.957 (0.893) across all seven categories. Additionally, we evaluated the transferability of the developed model using the data from the Intensive Care Unit in the US to secondary care mental health records (CRIS) in the UK. A direct application of the trained NER model to CRIS data resulted in reduced performance of F1=0.762, however after fine-tuning on a small sample from CRIS, the model achieved a reasonable performance of F1=0.944. This demonstrated that despite a close similarity between the data sets and the NER tasks, it is essential to fine-tune on the target domain data in order to achieve more accurate results. |
| spellingShingle | Kormilitzin, A Vaci, N Liu, Q Nevado-Holgado, A Med7: a transferable clinical natural language processing model for electronic health records |
| title | Med7: a transferable clinical natural language processing model for electronic health records |
| title_full | Med7: a transferable clinical natural language processing model for electronic health records |
| title_fullStr | Med7: a transferable clinical natural language processing model for electronic health records |
| title_full_unstemmed | Med7: a transferable clinical natural language processing model for electronic health records |
| title_short | Med7: a transferable clinical natural language processing model for electronic health records |
| title_sort | med7 a transferable clinical natural language processing model for electronic health records |
| work_keys_str_mv | AT kormilitzina med7atransferableclinicalnaturallanguageprocessingmodelforelectronichealthrecords AT vacin med7atransferableclinicalnaturallanguageprocessingmodelforelectronichealthrecords AT liuq med7atransferableclinicalnaturallanguageprocessingmodelforelectronichealthrecords AT nevadoholgadoa med7atransferableclinicalnaturallanguageprocessingmodelforelectronichealthrecords |