janrodriguez commited on
Commit
f710ba9
·
verified ·
1 Parent(s): 906489d

Upload README.md with huggingface_hub

Browse files
Files changed (1) hide show
  1. README.md +35 -60
README.md CHANGED
@@ -2,12 +2,12 @@
2
  language:
3
  - es
4
  tags:
5
- - biomedical
6
- - clinical
7
- - EHR
8
- - spanish
9
- - drugs
10
- - medications
11
  license: apache-2.0
12
  metrics:
13
  - precision
@@ -19,30 +19,31 @@ base_model:
19
  model-index:
20
  - name: BSC-NLP4BIA/bsc-bio-ehr-es-drugtemist
21
  results:
22
- - task:
23
- type: token-classification
24
- dataset:
25
- name: DrugTEMIST-es
26
- type: DrugTEMIST-es
27
- metrics:
28
- - name: precision
29
- type: precision
30
- value: 0.917
31
- - name: recall
32
- type: recall
33
- value: 0.909
34
- - name: f1
35
- type: f1
36
- value: 0.913
 
37
  widget:
38
- - text: "El diagnóstico definitivo de nuestro paciente fue de un Adenocarcinoma de pulmón cT2a cN3 cM1a Estadio IV (por una única lesión pulmonar contralateral) PD-L1 90%, EGFR negativo, ALK negativo y ROS-1 negativo."
39
- - text: "Durante el ingreso se realiza una TC, observándose un nódulo pulmonar en el LII y una masa renal derecha indeterminada. Se realiza punción biopsia del nódulo pulmonar, con hallazgos altamente sospechosos de carcinoma."
40
- - text: "Trombosis paraneoplásica con sospecha de hepatocarcinoma por imagen, sobre hígado cirrótico, en paciente con índice Child-Pugh B."
41
 
42
  ---
43
 
44
 
45
- # DRUG-NER-ES
46
 
47
  ## Table of contents
48
  <details>
@@ -51,7 +52,6 @@ widget:
51
  - [Model description](#model-description)
52
  - [How to use](#how-to-use)
53
  - [Limitations and bias](#limitations-and-bias)
54
- - [Training](#training)
55
  - [Evaluation](#evaluation)
56
  - [Additional information](#additional-information)
57
  - [Authors](#authors)
@@ -66,8 +66,6 @@ widget:
66
  ## Model description
67
  A fine-tuned version of the [bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) model on the [DrugTEMIST](https://zenodo.org/records/11368861) corpus (original Spanish Gold Standard).
68
 
69
- For further information, check the [official website](https://temu.bsc.es/multicardioner/).
70
-
71
  ## How to use
72
 
73
  ⚠ We recommend pre-tokenizing the input text into words instead of providing it directly to the model, as this is how the model was trained. Otherwise, the results and performance might get affected.
@@ -77,8 +75,6 @@ A usage example can be found [here](https://github.com/nlp4bia-bsc/hugging-face-
77
  ## Limitations and bias
78
  At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
79
 
80
- ## Training
81
- The model was trained using the Barcelona Supercomputing Center infrastructure.
82
 
83
  ## Evaluation
84
  F1 Score on DrugTEMIST-es: 0.913.
@@ -95,40 +91,19 @@ jan.rodriguez [at] bsc.es
95
  [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
96
 
97
  ### Funding
98
- This research was funded by the Ministerio de Ciencia e Innovación (MICINN) under project AI4ProfHealth (PID2020-119266RA-I00 MICIU/AEI/10.13039/501100011033) and BARITONE (TED2021-129974B-C22). This work is also supported by the European Union’s Horizon Europe Co-ordination \& Support Action under Grant Agreement No 101080430 (AI4HF) as well as Grant Agreement No 101057849 (DataTool4Heartproject).
99
 
100
  ### Citing information
101
 
102
  Please cite the following works:
103
 
104
- ```
105
- @inproceedings{multicardioner2024overview,
106
- title = {{Overview of MultiCardioNER task at BioASQ 2024 on Medical Speciality and Language Adaptation of Clinical NER Systems for Spanish, English and Italian}},
107
- author = {Salvador Lima-López and Eulàlia Farré-Maduell and Jan Rodríguez-Miret and Miguel Rodríguez-Ortega and Livia Lilli and Jacopo Lenkowicz and Giovanna Ceroni and Jonathan Kossoff and Anoop Shah and Anastasios Nentidis and Anastasia Krithara and Georgios Katsimpras and Georgios Paliouras and Martin Krallinger},
108
- booktitle = {CLEF Working Notes},
109
- year = {2024},
110
- editor = {Faggioli, Guglielmo and Ferro, Nicola and Galuščáková, Petra and García Seco de Herrera, Alba}
111
- }
112
-
113
- @misc{carmen_physionet,
114
- author = {Farre Maduell, Eulalia and Lima-Lopez, Salvador and Frid, Santiago Andres and Conesa, Artur and Asensio, Elisa and Lopez-Rueda, Antonio and Arino, Helena and Calvo, Elena and Bertran, Maria Jesús and Marcos, Maria Angeles and Nofre Maiz, Montserrat and Tañá Velasco, Laura and Marti, Antonia and Farreres, Ricardo and Pastor, Xavier and Borrat Frigola, Xavier and Krallinger, Martin},
115
- title = {{CARMEN-I: A resource of anonymized electronic health records in Spanish and Catalan for training and testing NLP tools (version 1.0.1)}},
116
- year = {2024},
117
- publisher = {PhysioNet},
118
- url = {https://doi.org/10.13026/x7ed-9r91}
119
- }
120
-
121
- @article{physionet,
122
- author = {Ary L. Goldberger and Luis A. N. Amaral and Leon Glass and Jeffrey M. Hausdorff and Plamen Ch. Ivanov and Roger G. Mark and Joseph E. Mietus and George B. Moody and Chung-Kang Peng and H. Eugene Stanley },
123
- title = {PhysioBank, PhysioToolkit, and PhysioNet },
124
- journal = {Circulation},
125
- volume = {101},
126
- number = {23},
127
- pages = {e215-e220},
128
- year = {2000},
129
- doi = {10.1161/01.CIR.101.23.e215},
130
- URL = {https://www.ahajournals.org/doi/abs/10.1161/01.CIR.101.23.e215}
131
- }
132
  ```
133
 
134
  ### Disclaimer
@@ -140,4 +115,4 @@ When third parties deploy or provide systems and/or services to other parties us
140
  ---
141
  Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables.
142
 
143
- Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial.
 
2
  language:
3
  - es
4
  tags:
5
+ - biomedical
6
+ - clinical
7
+ - EHR
8
+ - spanish
9
+ - drugs
10
+ - medications
11
  license: apache-2.0
12
  metrics:
13
  - precision
 
19
  model-index:
20
  - name: BSC-NLP4BIA/bsc-bio-ehr-es-drugtemist
21
  results:
22
+
23
+ - task:
24
+ type: token-classification
25
+ dataset:
26
+ name: DrugTEMIST-es
27
+ type: DrugTEMIST-es
28
+ metrics:
29
+ - name: precision
30
+ type: precision
31
+ value: 0.917
32
+ - name: recall
33
+ type: recall
34
+ value: 0.909
35
+ - name: f1
36
+ type: f1
37
+ value: 0.913
38
  widget:
39
+ - text: El diagnóstico definitivo de nuestro paciente fue de un Adenocarcinoma de pulmón cT2a cN3 cM1a Estadio IV (por una única lesión pulmonar contralateral) PD-L1 90%, EGFR negativo, ALK negativo y ROS-1 negativo.
40
+ - text: Durante el ingreso se realiza una TC, observándose un nódulo pulmonar en el LII y una masa renal derecha indeterminada. Se realiza punción biopsia del nódulo pulmonar, con hallazgos altamente sospechosos de carcinoma.
41
+ - text: Trombosis paraneoplásica con sospecha de hepatocarcinoma por imagen, sobre hígado cirrótico, en paciente con índice Child-Pugh B.
42
 
43
  ---
44
 
45
 
46
+ # BSC-NLP4BIA/bsc-bio-ehr-es-drugtemist
47
 
48
  ## Table of contents
49
  <details>
 
52
  - [Model description](#model-description)
53
  - [How to use](#how-to-use)
54
  - [Limitations and bias](#limitations-and-bias)
 
55
  - [Evaluation](#evaluation)
56
  - [Additional information](#additional-information)
57
  - [Authors](#authors)
 
66
  ## Model description
67
  A fine-tuned version of the [bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) model on the [DrugTEMIST](https://zenodo.org/records/11368861) corpus (original Spanish Gold Standard).
68
 
 
 
69
  ## How to use
70
 
71
  ⚠ We recommend pre-tokenizing the input text into words instead of providing it directly to the model, as this is how the model was trained. Otherwise, the results and performance might get affected.
 
75
  ## Limitations and bias
76
  At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
77
 
 
 
78
 
79
  ## Evaluation
80
  F1 Score on DrugTEMIST-es: 0.913.
 
91
  [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
92
 
93
  ### Funding
94
+ This research was funded by the Ministerio de Ciencia e Innovación (MICINN) under project AI4ProfHealth (PID2020-119266RA-I00 MICIU/AEI/10.13039/501100011033) and BARITONE (TED2021-129974B-C22). This work is also supported by the European Union’s Horizon Europe Co-ordination & Support Action under Grant Agreement No 101080430 (AI4HF) as well as Grant Agreement No 101057849 (DataTool4Heartproject).
95
 
96
  ### Citing information
97
 
98
  Please cite the following works:
99
 
100
+ ```bibtex
101
+
102
+ @inproceedings{multicardioner2024overview, title = {{Overview of MultiCardioNER task at BioASQ 2024 on Medical Speciality and Language Adaptation of Clinical NER Systems for Spanish, English and Italian}}, author = {Salvador Lima-López and Eulàlia Farré-Maduell and Jan Rodríguez-Miret and Miguel Rodríguez-Ortega and Livia Lilli and Jacopo Lenkowicz and Giovanna Ceroni and Jonathan Kossoff and Anoop Shah and Anastasios Nentidis and Anastasia Krithara and Georgios Katsimpras and Georgios Paliouras and Martin Krallinger}, booktitle = {CLEF Working Notes}, year = {2024}, editor = {Faggioli, Guglielmo and Ferro, Nicola and Galuščáková, Petra and García Seco de Herrera, Alba} }
103
+
104
+ @misc{carmen_physionet, author = {Farre Maduell, Eulalia and Lima-Lopez, Salvador and Frid, Santiago Andres and Conesa, Artur and Asensio, Elisa and Lopez-Rueda, Antonio and Arino, Helena and Calvo, Elena and Bertran, Maria Jesús and Marcos, Maria Angeles and Nofre Maiz, Montserrat and Tañá Velasco, Laura and Marti, Antonia and Farreres, Ricardo and Pastor, Xavier and Borrat Frigola, Xavier and Krallinger, Martin}, title = {{CARMEN-I: A resource of anonymized electronic health records in Spanish and Catalan for training and testing NLP tools (version 1.0.1)}}, year = {2024}, publisher = {PhysioNet}, url = {https://doi.org/10.13026/x7ed-9r91} }",
105
+
106
+ @article{physionet, author = {Ary L. Goldberger and Luis A. N. Amaral and Leon Glass and Jeffrey M. Hausdorff and Plamen Ch. Ivanov and Roger G. Mark and Joseph E. Mietus and George B. Moody and Chung-Kang Peng and H. Eugene Stanley }, title = {PhysioBank, PhysioToolkit, and PhysioNet }, journal = {Circulation}, volume = {101}, number = {23}, pages = {e215-e220}, year = {2000}, doi = {10.1161/01.CIR.101.23.e215}, URL = {https://www.ahajournals.org/doi/abs/10.1161/01.CIR.101.23.e215} }"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
107
  ```
108
 
109
  ### Disclaimer
 
115
  ---
116
  Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables.
117
 
118
+ Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial.