# Dataset Card for "DiagTrast"

## Table of Content
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Team members](#team-members)

## Dataset Description

### Dataset Summary

For the creation of this dataset, ChatGPT-4 was used to generate statements based on the characteristics of some of the mental disorders described in the "Manual Diagnóstico y Estadístico de Trastornos Mentales (DSM-5)". The mental disorders included are:
- Narcissistic personality disorder.
- Histrionic personality disorder.
- Borderline personality disorder.
- Antisocial personality disorder.
- Schizotypal personality disorder.

### Supported Tasks and Leaderboards

- text-classification: The dataset can be used to train a model for text classification, which consists in assigning a label or class to a given text. Some use cases are sentiment analysis, natural language inference, and assessing grammatical correctness. Success on this task is typically measured by achieving a high/low accuracy.

### Languages

This dataset of statements is in Spanish only.

## Dataset Structure

### Data Instances

A typical instance in the dataset comprises a statement describing one or more symptoms of a disorder, the name of the disorder, a sequential numerical id representing the disorder, and the clean text of the initial statement (i.e. free of punctuation marks and connectors).

The following is a JSON-formatted example of a typical case in this dataset:
```
{
  'Sintoma': "Su comportamiento es a menudo extraño y excéntrico, como llevar ropa que no coincide o actuar de una manera inapropiada en situaciones sociales.",
  'Padecimiento': "Trastornos de la personalidad esquizotípica",
  'Padecimiento_cat': 2,
  'Sintoma_limpia ': "comportamiento menudo extraño excentrico llevar ropa coincide actuar manera inapropiada situaciones sociales"
}
```

### Data Fields

- `Sintoma`: a string, representing a paragraph that a professional would enter describing the symptoms identified in a patient.
- `Padecimiento`: a string that indicates the disorder according to DSM-5.
- `Padecimiento_cat`: an integer representing the `Padecimiento` field, this field can be used as a label in a text-classification model.
- `Sintoma_Limpia`: a string, this field is the clean text of the `Sintoma` field. For the text-classification task, is advisable to use this field instead of the "Padecimiento" field to reduce the noise that punctuation marks, articles and connectors generate in the models.

### Data Splits

The data were not split into training and test subsets, instead having a single set with the following distribution:

|             Disorder             | Records | 
| -                                | -       |
| Narcissistic personality disorder| 250     |
| Histrionic personality disorder  | 250     | 
| Borderline personality disorder  | 358     |
| Antisocial personality disorder  | 250     |
| Schizotypal personality disorder | 225     |

## Dataset Creation

### Curation Rationale

It was decided to create this dataset because there is an extensive manual called DSM-5 which details the characteristics that must be present in a patient to diagnose a mental disorder. Some disorders have characteristics in common as well as their differences, for this reason we sought to classify, according to the DSM-5, statements that contain symptoms and characteristics identified by health professionals.

### Source Data

Data was generated using chatGPT, we first introduce the symptoms specified in the DSM-5 and request it to create statements containing one or more characteristics but without mentioning the name of the disorder. When the artificial intelligence generates the statements, a quick check is made to ensure that they are of the minimum expected quality, i.e., that they do not include the name of the disorder, that they are not too long or too short, and above all that they specifically contain the characteristics that were entered.

### Annotations

#### Annotation process

The generation of the data was carried out for each mental disorder, so that when we obtained the statements we also knew which label corresponded to it, so it was not necessary to make manual or automated annotations.

## Considerations for Using the Data

### Social Impact of Dataset

We hope that through the creation of models using this or a similar dataset, we can help to reduce the diagnosis times of mental disorders and increase the number of patients that can be seen and treated. On the other hand, we must consider the importance of using these technologies properly because if these models are used indiscriminately by people who do not have sufficient knowledge or experience to detect unusual behaviors in people, these models could negatively influence people by making them believe that they have a disorder.

### Discussion of Biases

It should not be forgotten that these data have been artificially generated so models that are trained might expect different inputs than a real mental health professional would generate. To mitigate this bias the team has closely verified the data generation process and this has evolved while identifying better prompts as well as filtering the statements and feeding back to the artificial intelligence to finally obtain the desired quality.

### Other Known Limitations

We have only generated data for 5 of the disorders described in the DSM-5.

## Team members

- [Alberto Martín Garrido](https://huggingface.co/Stremie)
- [Edgar Mencia](https://huggingface.co/edmenciab)
- [Miguel Ángel Solís Orozco](https://huggingface.co/homosapienssapiens)
- [Jose Carlos Vílchez Villegas](https://huggingface.co/JCarlos)
task_categories:text-classification size_categories:1K<n<10K language:es license:mit mental medical disorder region:us
{"language": ["es"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "DiagTrast", "dataset_info": {"features": [{"name": "Sintoma", "dtype": "string"}, {"name": "Padecimiento", "dtype": "string"}, {"name": "Padecimiento_cat", "dtype": "int64"}, {"name": "Sintoma_limpia", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 524464, "num_examples": 1333}], "download_size": 232511, "dataset_size": 524464}, "tags": ["mental", "medical", "disorder"]}
TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-Spanish #license-mit #mental #medical #disorder #region-us 

Dataset Card for "DiagTrast"
============================


Table of Content
----------------


* Table of Contents
* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Team members


Dataset Description
-------------------


### Dataset Summary


For the creation of this dataset, ChatGPT-4 was used to generate statements based on the characteristics of some of the mental disorders described in the "Manual Diagnóstico y Estadístico de Trastornos Mentales (DSM-5)". The mental disorders included are:


* Narcissistic personality disorder.
* Histrionic personality disorder.
* Borderline personality disorder.
* Antisocial personality disorder.
* Schizotypal personality disorder.


### Supported Tasks and Leaderboards


* text-classification: The dataset can be used to train a model for text classification, which consists in assigning a label or class to a given text. Some use cases are sentiment analysis, natural language inference, and assessing grammatical correctness. Success on this task is typically measured by achieving a high/low accuracy.


### Languages


This dataset of statements is in Spanish only.


Dataset Structure
-----------------


### Data Instances


A typical instance in the dataset comprises a statement describing one or more symptoms of a disorder, the name of the disorder, a sequential numerical id representing the disorder, and the clean text of the initial statement (i.e. free of punctuation marks and connectors).


The following is a JSON-formatted example of a typical case in this dataset:


### Data Fields


* 'Sintoma': a string, representing a paragraph that a professional would enter describing the symptoms identified in a patient.
* 'Padecimiento': a string that indicates the disorder according to DSM-5.
* 'Padecimiento\_cat': an integer representing the 'Padecimiento' field, this field can be used as a label in a text-classification model.
* 'Sintoma\_Limpia': a string, this field is the clean text of the 'Sintoma' field. For the text-classification task, is advisable to use this field instead of the "Padecimiento" field to reduce the noise that punctuation marks, articles and connectors generate in the models.


### Data Splits


The data were not split into training and test subsets, instead having a single set with the following distribution:



Dataset Creation
----------------


### Curation Rationale


It was decided to create this dataset because there is an extensive manual called DSM-5 which details the characteristics that must be present in a patient to diagnose a mental disorder. Some disorders have characteristics in common as well as their differences, for this reason we sought to classify, according to the DSM-5, statements that contain symptoms and characteristics identified by health professionals.


### Source Data


Data was generated using chatGPT, we first introduce the symptoms specified in the DSM-5 and request it to create statements containing one or more characteristics but without mentioning the name of the disorder. When the artificial intelligence generates the statements, a quick check is made to ensure that they are of the minimum expected quality, i.e., that they do not include the name of the disorder, that they are not too long or too short, and above all that they specifically contain the characteristics that were entered.


### Annotations


#### Annotation process


The generation of the data was carried out for each mental disorder, so that when we obtained the statements we also knew which label corresponded to it, so it was not necessary to make manual or automated annotations.


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


We hope that through the creation of models using this or a similar dataset, we can help to reduce the diagnosis times of mental disorders and increase the number of patients that can be seen and treated. On the other hand, we must consider the importance of using these technologies properly because if these models are used indiscriminately by people who do not have sufficient knowledge or experience to detect unusual behaviors in people, these models could negatively influence people by making them believe that they have a disorder.


### Discussion of Biases


It should not be forgotten that these data have been artificially generated so models that are trained might expect different inputs than a real mental health professional would generate. To mitigate this bias the team has closely verified the data generation process and this has evolved while identifying better prompts as well as filtering the statements and feeding back to the artificial intelligence to finally obtain the desired quality.


### Other Known Limitations


We have only generated data for 5 of the disorders described in the DSM-5.


Team members
------------


* Alberto Martín Garrido
* Edgar Mencia
* Miguel Ángel Solís Orozco
* Jose Carlos Vílchez Villegas



### Dataset Summary


For the creation of this dataset, ChatGPT-4 was used to generate statements based on the characteristics of some of the mental disorders described in the "Manual Diagnóstico y Estadístico de Trastornos Mentales (DSM-5)". The mental disorders included are:


* Narcissistic personality disorder.
* Histrionic personality disorder.
* Borderline personality disorder.
* Antisocial personality disorder.
* Schizotypal personality disorder. ### Supported Tasks and Leaderboards


* text-classification: The dataset can be used to train a model for text classification, which consists in assigning a label or class to a given text. Some use cases are sentiment analysis, natural language inference, and assessing grammatical correctness. Success on this task is typically measured by achieving a high/low accuracy. ### Languages


This dataset of statements is in Spanish only.


Dataset Structure
----------------- ### Data Instances


A typical instance in the dataset comprises a statement describing one or more symptoms of a disorder, the name of the disorder, a sequential numerical id representing the disorder, and the clean text of the initial statement (i.e. free of punctuation marks and connectors).


The following is a JSON-formatted example of a typical case in this dataset: ### Data Fields


* 'Sintoma': a string, representing a paragraph that a professional would enter describing the symptoms identified in a patient.
* 'Padecimiento': a string that indicates the disorder according to DSM-5.
* 'Padecimiento\_cat': an integer representing the 'Padecimiento' field, this field can be used as a label in a text-classification model.
* 'Sintoma\_Limpia': a string, this field is the clean text of the 'Sintoma' field. For the text-classification task, is advisable to use this field instead of the "Padecimiento" field to reduce the noise that punctuation marks, articles and connectors generate in the models. ### Data Splits


The data were not split into training and test subsets, instead having a single set with the following distribution:



Dataset Creation
---------------- ### Curation Rationale


It was decided to create this dataset because there is an extensive manual called DSM-5 which details the characteristics that must be present in a patient to diagnose a mental disorder. Some disorders have characteristics in common as well as their differences, for this reason we sought to classify, according to the DSM-5, statements that contain symptoms and characteristics identified by health professionals. ### Source Data


Data was generated using chatGPT, we first introduce the symptoms specified in the DSM-5 and request it to create statements containing one or more characteristics but without mentioning the name of the disorder. When the artificial intelligence generates the statements, a quick check is made to ensure that they are of the minimum expected quality, i.e., that they do not include the name of the disorder, that they are not too long or too short, and above all that they specifically contain the characteristics that were entered. ### Annotations #### Annotation process


The generation of the data was carried out for each mental disorder, so that when we obtained the statements we also knew which label corresponded to it, so it was not necessary to make manual or automated annotations.


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset


We hope that through the creation of models using this or a similar dataset, we can help to reduce the diagnosis times of mental disorders and increase the number of patients that can be seen and treated. On the other hand, we must consider the importance of using these technologies properly because if these models are used indiscriminately by people who do not have sufficient knowledge or experience to detect unusual behaviors in people, these models could negatively influence people by making them believe that they have a disorder. ### Discussion of Biases


It should not be forgotten that these data have been artificially generated so models that are trained might expect different inputs than a real mental health professional would generate. To mitigate this bias the team has closely verified the data generation process and this has evolved while identifying better prompts as well as filtering the statements and feeding back to the artificial intelligence to finally obtain the desired quality. ### Other Known Limitations


We have only generated data for 5 of the disorders described in the DSM-5.


Team members
------------


* Alberto Martín Garrido
* Edgar Mencia
* Miguel Ángel Solís Orozco
* Jose Carlos Vílchez Villegas
TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-Spanish #license-mit #mental #medical #disorder #region-us 
 ### Dataset Summary


For the creation of this dataset, ChatGPT-4 was used to generate statements based on the characteristics of some of the mental disorders described in the "Manual Diagnóstico y Estadístico de Trastornos Mentales (DSM-5)". The mental disorders included are:


* Narcissistic personality disorder.
* Histrionic personality disorder.
* Borderline personality disorder.
* Antisocial personality disorder.
* Schizotypal personality disorder. ### Supported Tasks and Leaderboards


* text-classification: The dataset can be used to train a model for text classification, which consists in assigning a label or class to a given text. Some use cases are sentiment analysis, natural language inference, and assessing grammatical correctness. Success on this task is typically measured by achieving a high/low accuracy. ### Languages


This dataset of statements is in Spanish only.


Dataset Structure
----------------- ### Data Instances


A typical instance in the dataset comprises a statement describing one or more symptoms of a disorder, the name of the disorder, a sequential numerical id representing the disorder, and the clean text of the initial statement (i.e. free of punctuation marks and connectors).


The following is a JSON-formatted example of a typical case in this dataset: ### Data Fields


* 'Sintoma': a string, representing a paragraph that a professional would enter describing the symptoms identified in a patient.
* 'Padecimiento': a string that indicates the disorder according to DSM-5.
* 'Padecimiento\_cat': an integer representing the 'Padecimiento' field, this field can be used as a label in a text-classification model.
* 'Sintoma\_Limpia': a string, this field is the clean text of the 'Sintoma' field. For the text-classification task, is advisable to use this field instead of the "Padecimiento" field to reduce the noise that punctuation marks, articles and connectors generate in the models. ### Data Splits


The data were not split into training and test subsets, instead having a single set with the following distribution:



Dataset Creation
---------------- ### Curation Rationale


It was decided to create this dataset because there is an extensive manual called DSM-5 which details the characteristics that must be present in a patient to diagnose a mental disorder. Some disorders have characteristics in common as well as their differences, for this reason we sought to classify, according to the DSM-5, statements that contain symptoms and characteristics identified by health professionals. ### Source Data


Data was generated using chatGPT, we first introduce the symptoms specified in the DSM-5 and request it to create statements containing one or more characteristics but without mentioning the name of the disorder. When the artificial intelligence generates the statements, a quick check is made to ensure that they are of the minimum expected quality, i.e., that they do not include the name of the disorder, that they are not too long or too short, and above all that they specifically contain the characteristics that were entered. ### Annotations #### Annotation process


The generation of the data was carried out for each mental disorder, so that when we obtained the statements we also knew which label corresponded to it, so it was not necessary to make manual or automated annotations.


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset


We hope that through the creation of models using this or a similar dataset, we can help to reduce the diagnosis times of mental disorders and increase the number of patients that can be seen and treated. On the other hand, we must consider the importance of using these technologies properly because if these models are used indiscriminately by people who do not have sufficient knowledge or experience to detect unusual behaviors in people, these models could negatively influence people by making them believe that they have a disorder. ### Discussion of Biases


It should not be forgotten that these data have been artificially generated so models that are trained might expect different inputs than a real mental health professional would generate. To mitigate this bias the team has closely verified the data generation process and this has evolved while identifying better prompts as well as filtering the statements and feeding back to the artificial intelligence to finally obtain the desired quality. ### Other Known Limitations


We have only generated data for 5 of the disorders described in the DSM-5.


Team members
------------


* Alberto Martín Garrido
* Edgar Mencia
* Miguel Ángel Solís Orozco
* Jose Carlos Vílchez Villegas
passage: TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-Spanish #license-mit #mental #medical #disorder #region-us 
### Dataset Summary


For the creation of this dataset, ChatGPT-4 was used to generate statements based on the characteristics of some of the mental disorders described in the "Manual Diagnóstico y Estadístico de Trastornos Mentales (DSM-5)". The mental disorders included are:


* Narcissistic personality disorder.
* Histrionic personality disorder.
* Borderline personality disorder.
* Antisocial personality disorder.
* Schizotypal personality disorder.### Supported Tasks and Leaderboards


* text-classification: The dataset can be used to train a model for text classification, which consists in assigning a label or class to a given text. Some use cases are sentiment analysis, natural language inference, and assessing grammatical correctness. Success on this task is typically measured by achieving a high/low accuracy.### Languages


This dataset of statements is in Spanish only.


Dataset Structure
-----------------### Data Instances


A typical instance in the dataset comprises a statement describing one or more symptoms of a disorder, the name of the disorder, a sequential numerical id representing the disorder, and the clean text of the initial statement (i.e. free of punctuation marks and connectors).


The following is a JSON-formatted example of a typical case in this dataset: passage: ### Data Fields


* 'Sintoma': a string, representing a paragraph that a professional would enter describing the symptoms identified in a patient.
* 'Padecimiento': a string that indicates the disorder according to DSM-5.
* 'Padecimiento\_cat': an integer representing the 'Padecimiento' field, this field can be used as a label in a text-classification model.
* 'Sintoma\_Limpia': a string, this field is the clean text of the 'Sintoma' field. For the text-classification task, is advisable to use this field instead of the "Padecimiento" field to reduce the noise that punctuation marks, articles and connectors generate in the models.### Data Splits


The data were not split into training and test subsets, instead having a single set with the following distribution:



Dataset Creation
----------------### Curation Rationale


It was decided to create this dataset because there is an extensive manual called DSM-5 which details the characteristics that must be present in a patient to diagnose a mental disorder. Some disorders have characteristics in common as well as their differences, for this reason we sought to classify, according to the DSM-5, statements that contain symptoms and characteristics identified by health professionals.### Source Data


Data was generated using chatGPT, we first introduce the symptoms specified in the DSM-5 and request it to create statements containing one or more characteristics but without mentioning the name of the disorder. When the artificial intelligence generates the statements, a quick check is made to ensure that they are of the minimum expected quality, i.e., that they do not include the name of the disorder, that they are not too long or too short, and above all that they specifically contain the characteristics that were entered.### Annotations#### Annotation process


The generation of the data was carried out for each mental disorder, so that when we obtained the statements we also knew which label corresponded to it, so it was not necessary to make manual or automated annotations.


Considerations for Using the Data
---------------------------------### Social Impact of Dataset


We hope that through the creation of models using this or a similar dataset, we can help to reduce the diagnosis times of mental disorders and increase the number of patients that can be seen and treated. On the other hand, we must consider the importance of using these technologies properly because if these models are used indiscriminately by people who do not have sufficient knowledge or experience to detect unusual behaviors in people, these models could negatively influence people by making them believe that they have a disorder.