Heart Disease Prediction #538

nickclyde · 2023-03-31T21:38:48Z

Thank you for contributing an eval! ♥️

🚨 Please make sure your PR follows these guidelines, failure to follow the guidelines below will result in the PR being closed automatically. Note that even if the criteria are met, that does not guarantee the PR will be merged nor GPT-4 access granted. 🚨

PLEASE READ THIS:

In order for a PR to be merged, it must fail on GPT-4. We are aware that right now, users do not have access, so you will not be able to tell if the eval fails or not. Please run your eval with GPT-3.5-Turbo, but keep in mind as we run the eval, if GPT-4 gets higher than 90% on the eval, we will likely reject since GPT-4 is already capable of completing the task.

We plan to roll out a way for users submitting evals to see the eval performance on GPT-4 soon. Stay tuned! Until then, you will not be able to see the eval performance on GPT-4. We encourage partial PR's with ~5-10 example that we can then run the evals on and share the results with you so you know how your eval does with GPT-4 before writing all 100 examples.

Eval details 📑

Eval name

Heart Disease Prediction

Eval description

This eval tests the models ability to correctly predict the probability of a patient to have heart disease. The dataset is constructed from the Heart Failure Prediction Dataset on Kaggle. The data includes the patient's age, sex, and a number of medical signals relevant to the diagnosis of heart disease.

The data is provided under the Open Database License (ODbL).

fedesoriano. (September 2021). Heart Failure Prediction Dataset. Retrieved [Mar 31, 2023] from https://www.kaggle.com/fedesoriano/heart-failure-prediction.

What makes this a useful eval?

This assesses the model's ability to correctly predict adverse medical events. Correctly predicting heart disease shows the model's capability for a strong understanding of medicine. The GPT-3.5-turbo models currently receives an accuracy of 0.778.

Criteria for a good eval ✅

Below are some of the criteria we look for in a good eval. In general, we are seeking cases where the model does not do a good job despite being capable of generating a good response (note that there are some things large language models cannot do, so those would not make good evals).

Your eval should be:

Thematically consistent: The eval should be thematically consistent. We'd like to see a number of prompts all demonstrating some particular failure mode. For example, we can create an eval on cases where the model fails to reason about the physical world.
Contains failures where a human can do the task, but either GPT-4 or GPT-3.5-Turbo could not.
Includes good signal around what is the right behavior. This means either a correct answer for Basic evals or the Fact Model-graded eval, or an exhaustive rubric for evaluating answers for the Criteria Model-graded eval.
Include at least 100 high quality examples (it is okay to only contribute 5-10 meaningful examples and have us test them with GPT-4 before adding all 100)

If there is anything else that makes your eval worth including, please document it below.

Unique eval value

Insert what makes your eval high quality that was not mentioned above. (Not required)

As far as I can tell, this is the only eval so far related to making medical diagnoses. To make sure it was a high quality eval, I tried to find a dataset with a lot of observations and created by doctors with the relevant expertise.

Eval structure 🏗️

Your eval should

Check that your data is in evals/registry/data/{name}
Check that your yaml is registered at evals/registry/evals/{name}.yaml
Ensure you have the right to use the data you submit via this eval

(For now, we will only be approving evals that use one of the existing eval classes. You may still write custom eval classes for your own cases, and we may consider merging them in the future.)

Final checklist 👀

Submission agreement

By contributing to Evals, you are agreeing to make your evaluation logic and data under the same MIT license as this repository. You must have adequate rights to upload any data used in an Eval. OpenAI reserves the right to use this data in future service improvements to our product. Contributions to OpenAI Evals will be subject to our usual Usage Policies (https://platform.openai.com/docs/usage-policies).

I agree that my submission will be made available under an MIT license and complies with OpenAI's usage policies.

Email address validation

If your submission is accepted, we will be granting GPT-4 access to a limited number of contributors. Access will be given to the email address associated with the merged pull request.

I acknowledge that GPT-4 access will only be granted, if applicable, to the email address used for my merged pull request.

Limited availability acknowledgement

We know that you might be excited to contribute to OpenAI's mission, help improve our models, and gain access to GPT-4. However, due to the requirements mentioned above and high volume of submissions, we will not be able to accept all submissions and thus not grant everyone who opens a PR GPT-4 access. We know this is disappointing, but we hope to set the right expectation before you open this PR.

I understand that opening a PR, even if it meets the requirements above, does not guarantee the PR will be merged nor GPT-4 access granted.

Submit eval

I have filled out all required fields in the evals PR form
(Ignore if not submitting code) I have run pip install pre-commit; pre-commit install and have verified that black, isort, and autoflake are running when I commit and push

Failure to fill out all required fields will result in the PR being closed.

Eval JSON data

Since we are using Git LFS, we are asking eval submitters to add in as many Eval Samples (at least 5) from their contribution here:

View evals in JSON

Eval

{"input": [{"role": "system", "content": "You are an AI tasked with predicting whether patients are likely to have heart disease. You will be given a description of the patient with relevant medical signals. Respond with only a 1 to signify if the patient is likely to have heart disease, or a 0 if the patient is not likely to have heart disease. Do not respond with any text or disclaimers, only respond with either 1 or 0."}, {"role": "user", "content": "Age: 40 years, Sex: Male, Chest pain type: Atypical Angina, Resting blood pressure: 140 mm Hg, Serum cholesterol: 289 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG results: Normal, Max heart rate achieved: 172, Exercise induced angina: No, Oldpeak: 0, ST Slope: Upsloping"}], "ideal": "0"}
{"input": [{"role": "system", "content": "You are an AI tasked with predicting whether patients are likely to have heart disease. You will be given a description of the patient with relevant medical signals. Respond with only a 1 to signify if the patient is likely to have heart disease, or a 0 if the patient is not likely to have heart disease. Do not respond with any text or disclaimers, only respond with either 1 or 0."}, {"role": "user", "content": "Age: 49 years, Sex: Female, Chest pain type: Non-Anginal Pain, Resting blood pressure: 160 mm Hg, Serum cholesterol: 180 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG results: Normal, Max heart rate achieved: 156, Exercise induced angina: No, Oldpeak: 1, ST Slope: Flat"}], "ideal": "1"}
{"input": [{"role": "system", "content": "You are an AI tasked with predicting whether patients are likely to have heart disease. You will be given a description of the patient with relevant medical signals. Respond with only a 1 to signify if the patient is likely to have heart disease, or a 0 if the patient is not likely to have heart disease. Do not respond with any text or disclaimers, only respond with either 1 or 0."}, {"role": "user", "content": "Age: 37 years, Sex: Male, Chest pain type: Atypical Angina, Resting blood pressure: 130 mm Hg, Serum cholesterol: 283 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG results: ST-T wave abnormality, Max heart rate achieved: 98, Exercise induced angina: No, Oldpeak: 0, ST Slope: Upsloping"}], "ideal": "0"}
{"input": [{"role": "system", "content": "You are an AI tasked with predicting whether patients are likely to have heart disease. You will be given a description of the patient with relevant medical signals. Respond with only a 1 to signify if the patient is likely to have heart disease, or a 0 if the patient is not likely to have heart disease. Do not respond with any text or disclaimers, only respond with either 1 or 0."}, {"role": "user", "content": "Age: 48 years, Sex: Female, Chest pain type: Asymptomatic, Resting blood pressure: 138 mm Hg, Serum cholesterol: 214 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG results: Normal, Max heart rate achieved: 108, Exercise induced angina: Yes, Oldpeak: 1.5, ST Slope: Flat"}], "ideal": "1"}
{"input": [{"role": "system", "content": "You are an AI tasked with predicting whether patients are likely to have heart disease. You will be given a description of the patient with relevant medical signals. Respond with only a 1 to signify if the patient is likely to have heart disease, or a 0 if the patient is not likely to have heart disease. Do not respond with any text or disclaimers, only respond with either 1 or 0."}, {"role": "user", "content": "Age: 54 years, Sex: Male, Chest pain type: Non-Anginal Pain, Resting blood pressure: 150 mm Hg, Serum cholesterol: 195 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG results: Normal, Max heart rate achieved: 122, Exercise induced angina: No, Oldpeak: 0, ST Slope: Upsloping"}], "ideal": "0"}

andrew-openai

Thanks for submitting this eval! We've approved the PR and will merge. You'll expect to see GPT-4 API access on your account soon.

# Thank you for contributing an eval! ♥️ 🚨 Please make sure your PR follows these guidelines, __failure to follow the guidelines below will result in the PR being closed automatically__. Note that even if the criteria are met, that does not guarantee the PR will be merged nor GPT-4 access granted. 🚨 __PLEASE READ THIS__: In order for a PR to be merged, it must fail on GPT-4. We are aware that right now, users do not have access, so you will not be able to tell if the eval fails or not. Please run your eval with GPT-3.5-Turbo, but keep in mind as we run the eval, if GPT-4 gets higher than 90% on the eval, we will likely reject since GPT-4 is already capable of completing the task. We plan to roll out a way for users submitting evals to see the eval performance on GPT-4 soon. Stay tuned! Until then, you will not be able to see the eval performance on GPT-4. We encourage partial PR's with ~5-10 example that we can then run the evals on and share the results with you so you know how your eval does with GPT-4 before writing all 100 examples. ## Eval details 📑 ### Eval name Heart Disease Prediction ### Eval description This eval tests the models ability to correctly predict the probability of a patient to have heart disease. The dataset is constructed from the [Heart Failure Prediction Dataset](https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction) on Kaggle. The data includes the patient's age, sex, and a number of medical signals relevant to the diagnosis of heart disease. The data is provided under the Open Database License (ODbL). ``` fedesoriano. (September 2021). Heart Failure Prediction Dataset. Retrieved [Mar 31, 2023] from https://www.kaggle.com/fedesoriano/heart-failure-prediction. ``` ### What makes this a useful eval? This assesses the model's ability to correctly predict adverse medical events. Correctly predicting heart disease shows the model's capability for a strong understanding of medicine. The GPT-3.5-turbo models currently receives an accuracy of 0.778. <img width="1250" alt="Screenshot 2023-03-31 at 2 24 13 PM" src="https://user-images.githubusercontent.com/9121162/229234376-9cdd1315-5df0-48bf-9328-ac31aabec3cc.png"> ## Criteria for a good eval ✅ Below are some of the criteria we look for in a good eval. In general, we are seeking cases where the model does not do a good job despite being capable of generating a good response (note that there are some things large language models cannot do, so those would not make good evals). Your eval should be: - [x] Thematically consistent: The eval should be thematically consistent. We'd like to see a number of prompts all demonstrating some particular failure mode. For example, we can create an eval on cases where the model fails to reason about the physical world. - [x] Contains failures where a human can do the task, but either GPT-4 or GPT-3.5-Turbo could not. - [x] Includes good signal around what is the right behavior. This means either a correct answer for `Basic` evals or the `Fact` Model-graded eval, or an exhaustive rubric for evaluating answers for the `Criteria` Model-graded eval. - [x] Include at least 100 high quality examples (it is okay to only contribute 5-10 meaningful examples and have us test them with GPT-4 before adding all 100) If there is anything else that makes your eval worth including, please document it below. ### Unique eval value > Insert what makes your eval high quality that was not mentioned above. (Not required) As far as I can tell, this is the only eval so far related to making medical diagnoses. To make sure it was a high quality eval, I tried to find a dataset with a lot of observations and created by doctors with the relevant expertise. ## Eval structure 🏗️ Your eval should - [x] Check that your data is in `evals/registry/data/{name}` - [x] Check that your yaml is registered at `evals/registry/evals/{name}.yaml` - [x] Ensure you have the right to use the data you submit via this eval (For now, we will only be approving evals that use one of the existing eval classes. You may still write custom eval classes for your own cases, and we may consider merging them in the future.) ## Final checklist 👀 ### Submission agreement By contributing to Evals, you are agreeing to make your evaluation logic and data under the same MIT license as this repository. You must have adequate rights to upload any data used in an Eval. OpenAI reserves the right to use this data in future service improvements to our product. Contributions to OpenAI Evals will be subject to our usual Usage Policies (https://platform.openai.com/docs/usage-policies). - [x] I agree that my submission will be made available under an MIT license and complies with OpenAI's usage policies. ### Email address validation If your submission is accepted, we will be granting GPT-4 access to a limited number of contributors. Access will be given to the email address associated with the merged pull request. - [x] I acknowledge that GPT-4 access will only be granted, if applicable, to the email address used for my merged pull request. ### Limited availability acknowledgement We know that you might be excited to contribute to OpenAI's mission, help improve our models, and gain access to GPT-4. However, due to the requirements mentioned above and high volume of submissions, we will not be able to accept all submissions and thus not grant everyone who opens a PR GPT-4 access. We know this is disappointing, but we hope to set the right expectation before you open this PR. - [x] I understand that opening a PR, even if it meets the requirements above, does not guarantee the PR will be merged nor GPT-4 access granted. ### Submit eval - [x] I have filled out all required fields in the evals PR form - [x] (Ignore if not submitting code) I have run `pip install pre-commit; pre-commit install` and have verified that `black`, `isort`, and `autoflake` are running when I commit and push Failure to fill out all required fields will result in the PR being closed. ### Eval JSON data Since we are using Git LFS, we are asking eval submitters to add in as many Eval Samples (at least 5) from their contribution here: <details> <summary>View evals in JSON</summary> ### Eval ```jsonl {"input": [{"role": "system", "content": "You are an AI tasked with predicting whether patients are likely to have heart disease. You will be given a description of the patient with relevant medical signals. Respond with only a 1 to signify if the patient is likely to have heart disease, or a 0 if the patient is not likely to have heart disease. Do not respond with any text or disclaimers, only respond with either 1 or 0."}, {"role": "user", "content": "Age: 40 years, Sex: Male, Chest pain type: Atypical Angina, Resting blood pressure: 140 mm Hg, Serum cholesterol: 289 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG results: Normal, Max heart rate achieved: 172, Exercise induced angina: No, Oldpeak: 0, ST Slope: Upsloping"}], "ideal": "0"} {"input": [{"role": "system", "content": "You are an AI tasked with predicting whether patients are likely to have heart disease. You will be given a description of the patient with relevant medical signals. Respond with only a 1 to signify if the patient is likely to have heart disease, or a 0 if the patient is not likely to have heart disease. Do not respond with any text or disclaimers, only respond with either 1 or 0."}, {"role": "user", "content": "Age: 49 years, Sex: Female, Chest pain type: Non-Anginal Pain, Resting blood pressure: 160 mm Hg, Serum cholesterol: 180 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG results: Normal, Max heart rate achieved: 156, Exercise induced angina: No, Oldpeak: 1, ST Slope: Flat"}], "ideal": "1"} {"input": [{"role": "system", "content": "You are an AI tasked with predicting whether patients are likely to have heart disease. You will be given a description of the patient with relevant medical signals. Respond with only a 1 to signify if the patient is likely to have heart disease, or a 0 if the patient is not likely to have heart disease. Do not respond with any text or disclaimers, only respond with either 1 or 0."}, {"role": "user", "content": "Age: 37 years, Sex: Male, Chest pain type: Atypical Angina, Resting blood pressure: 130 mm Hg, Serum cholesterol: 283 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG results: ST-T wave abnormality, Max heart rate achieved: 98, Exercise induced angina: No, Oldpeak: 0, ST Slope: Upsloping"}], "ideal": "0"} {"input": [{"role": "system", "content": "You are an AI tasked with predicting whether patients are likely to have heart disease. You will be given a description of the patient with relevant medical signals. Respond with only a 1 to signify if the patient is likely to have heart disease, or a 0 if the patient is not likely to have heart disease. Do not respond with any text or disclaimers, only respond with either 1 or 0."}, {"role": "user", "content": "Age: 48 years, Sex: Female, Chest pain type: Asymptomatic, Resting blood pressure: 138 mm Hg, Serum cholesterol: 214 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG results: Normal, Max heart rate achieved: 108, Exercise induced angina: Yes, Oldpeak: 1.5, ST Slope: Flat"}], "ideal": "1"} {"input": [{"role": "system", "content": "You are an AI tasked with predicting whether patients are likely to have heart disease. You will be given a description of the patient with relevant medical signals. Respond with only a 1 to signify if the patient is likely to have heart disease, or a 0 if the patient is not likely to have heart disease. Do not respond with any text or disclaimers, only respond with either 1 or 0."}, {"role": "user", "content": "Age: 54 years, Sex: Male, Chest pain type: Non-Anginal Pain, Resting blood pressure: 150 mm Hg, Serum cholesterol: 195 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG results: Normal, Max heart rate achieved: 122, Exercise induced angina: No, Oldpeak: 0, ST Slope: Upsloping"}], "ideal": "0"} ``` </details>

nickclyde · 2023-04-13T16:21:25Z

@andrew-openai Should I have gotten GPT-4 added to my account by now? I'm still only seeing gpt-3.5-turbo available on the Playground. Thanks for accepting my eval!

andrew-openai · 2023-04-13T17:11:41Z

please check again!

…

On Thu, Apr 13, 2023 at 9:21 AM Nick Clyde ***@***.***> wrote: @andrew-openai <https://github.com/andrew-openai> Should I have gotten GPT-4 added to my account by now? I'm still only seeing gpt-3.5-turbo available on the Playground. Thanks for accepting my eval! — Reply to this email directly, view it on GitHub <#538 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/A4WYH5DDXHR4F3F2OWAGVOTXBARZDANCNFSM6AAAAAAWPE4OYU> . You are receiving this because you were mentioned.Message ID: ***@***.***>

nickclyde · 2023-04-13T17:13:45Z

@andrew-openai Awesome, all good to go now. Thanks so much!

# Thank you for contributing an eval! ♥️ 🚨 Please make sure your PR follows these guidelines, __failure to follow the guidelines below will result in the PR being closed automatically__. Note that even if the criteria are met, that does not guarantee the PR will be merged nor GPT-4 access granted. 🚨 __PLEASE READ THIS__: In order for a PR to be merged, it must fail on GPT-4. We are aware that right now, users do not have access, so you will not be able to tell if the eval fails or not. Please run your eval with GPT-3.5-Turbo, but keep in mind as we run the eval, if GPT-4 gets higher than 90% on the eval, we will likely reject since GPT-4 is already capable of completing the task. We plan to roll out a way for users submitting evals to see the eval performance on GPT-4 soon. Stay tuned! Until then, you will not be able to see the eval performance on GPT-4. We encourage partial PR's with ~5-10 example that we can then run the evals on and share the results with you so you know how your eval does with GPT-4 before writing all 100 examples. ## Eval details 📑 ### Eval name Heart Disease Prediction ### Eval description This eval tests the models ability to correctly predict the probability of a patient to have heart disease. The dataset is constructed from the [Heart Failure Prediction Dataset](https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction) on Kaggle. The data includes the patient's age, sex, and a number of medical signals relevant to the diagnosis of heart disease. The data is provided under the Open Database License (ODbL). ``` fedesoriano. (September 2021). Heart Failure Prediction Dataset. Retrieved [Mar 31, 2023] from https://www.kaggle.com/fedesoriano/heart-failure-prediction. ``` ### What makes this a useful eval? This assesses the model's ability to correctly predict adverse medical events. Correctly predicting heart disease shows the model's capability for a strong understanding of medicine. The GPT-3.5-turbo models currently receives an accuracy of 0.778. <img width="1250" alt="Screenshot 2023-03-31 at 2 24 13 PM" src="https://user-images.githubusercontent.com/9121162/229234376-9cdd1315-5df0-48bf-9328-ac31aabec3cc.png"> ## Criteria for a good eval ✅ Below are some of the criteria we look for in a good eval. In general, we are seeking cases where the model does not do a good job despite being capable of generating a good response (note that there are some things large language models cannot do, so those would not make good evals). Your eval should be: - [x] Thematically consistent: The eval should be thematically consistent. We'd like to see a number of prompts all demonstrating some particular failure mode. For example, we can create an eval on cases where the model fails to reason about the physical world. - [x] Contains failures where a human can do the task, but either GPT-4 or GPT-3.5-Turbo could not. - [x] Includes good signal around what is the right behavior. This means either a correct answer for `Basic` evals or the `Fact` Model-graded eval, or an exhaustive rubric for evaluating answers for the `Criteria` Model-graded eval. - [x] Include at least 100 high quality examples (it is okay to only contribute 5-10 meaningful examples and have us test them with GPT-4 before adding all 100) If there is anything else that makes your eval worth including, please document it below. ### Unique eval value > Insert what makes your eval high quality that was not mentioned above. (Not required) As far as I can tell, this is the only eval so far related to making medical diagnoses. To make sure it was a high quality eval, I tried to find a dataset with a lot of observations and created by doctors with the relevant expertise. ## Eval structure 🏗️ Your eval should - [x] Check that your data is in `evals/registry/data/{name}` - [x] Check that your yaml is registered at `evals/registry/evals/{name}.yaml` - [x] Ensure you have the right to use the data you submit via this eval (For now, we will only be approving evals that use one of the existing eval classes. You may still write custom eval classes for your own cases, and we may consider merging them in the future.) ## Final checklist 👀 ### Submission agreement By contributing to Evals, you are agreeing to make your evaluation logic and data under the same MIT license as this repository. You must have adequate rights to upload any data used in an Eval. OpenAI reserves the right to use this data in future service improvements to our product. Contributions to OpenAI Evals will be subject to our usual Usage Policies (https://platform.openai.com/docs/usage-policies). - [x] I agree that my submission will be made available under an MIT license and complies with OpenAI's usage policies. ### Email address validation If your submission is accepted, we will be granting GPT-4 access to a limited number of contributors. Access will be given to the email address associated with the merged pull request. - [x] I acknowledge that GPT-4 access will only be granted, if applicable, to the email address used for my merged pull request. ### Limited availability acknowledgement We know that you might be excited to contribute to OpenAI's mission, help improve our models, and gain access to GPT-4. However, due to the requirements mentioned above and high volume of submissions, we will not be able to accept all submissions and thus not grant everyone who opens a PR GPT-4 access. We know this is disappointing, but we hope to set the right expectation before you open this PR. - [x] I understand that opening a PR, even if it meets the requirements above, does not guarantee the PR will be merged nor GPT-4 access granted. ### Submit eval - [x] I have filled out all required fields in the evals PR form - [x] (Ignore if not submitting code) I have run `pip install pre-commit; pre-commit install` and have verified that `black`, `isort`, and `autoflake` are running when I commit and push Failure to fill out all required fields will result in the PR being closed. ### Eval JSON data Since we are using Git LFS, we are asking eval submitters to add in as many Eval Samples (at least 5) from their contribution here: <details> <summary>View evals in JSON</summary> ### Eval ```jsonl {"input": [{"role": "system", "content": "You are an AI tasked with predicting whether patients are likely to have heart disease. You will be given a description of the patient with relevant medical signals. Respond with only a 1 to signify if the patient is likely to have heart disease, or a 0 if the patient is not likely to have heart disease. Do not respond with any text or disclaimers, only respond with either 1 or 0."}, {"role": "user", "content": "Age: 40 years, Sex: Male, Chest pain type: Atypical Angina, Resting blood pressure: 140 mm Hg, Serum cholesterol: 289 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG results: Normal, Max heart rate achieved: 172, Exercise induced angina: No, Oldpeak: 0, ST Slope: Upsloping"}], "ideal": "0"} {"input": [{"role": "system", "content": "You are an AI tasked with predicting whether patients are likely to have heart disease. You will be given a description of the patient with relevant medical signals. Respond with only a 1 to signify if the patient is likely to have heart disease, or a 0 if the patient is not likely to have heart disease. Do not respond with any text or disclaimers, only respond with either 1 or 0."}, {"role": "user", "content": "Age: 49 years, Sex: Female, Chest pain type: Non-Anginal Pain, Resting blood pressure: 160 mm Hg, Serum cholesterol: 180 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG results: Normal, Max heart rate achieved: 156, Exercise induced angina: No, Oldpeak: 1, ST Slope: Flat"}], "ideal": "1"} {"input": [{"role": "system", "content": "You are an AI tasked with predicting whether patients are likely to have heart disease. You will be given a description of the patient with relevant medical signals. Respond with only a 1 to signify if the patient is likely to have heart disease, or a 0 if the patient is not likely to have heart disease. Do not respond with any text or disclaimers, only respond with either 1 or 0."}, {"role": "user", "content": "Age: 37 years, Sex: Male, Chest pain type: Atypical Angina, Resting blood pressure: 130 mm Hg, Serum cholesterol: 283 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG results: ST-T wave abnormality, Max heart rate achieved: 98, Exercise induced angina: No, Oldpeak: 0, ST Slope: Upsloping"}], "ideal": "0"} {"input": [{"role": "system", "content": "You are an AI tasked with predicting whether patients are likely to have heart disease. You will be given a description of the patient with relevant medical signals. Respond with only a 1 to signify if the patient is likely to have heart disease, or a 0 if the patient is not likely to have heart disease. Do not respond with any text or disclaimers, only respond with either 1 or 0."}, {"role": "user", "content": "Age: 48 years, Sex: Female, Chest pain type: Asymptomatic, Resting blood pressure: 138 mm Hg, Serum cholesterol: 214 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG results: Normal, Max heart rate achieved: 108, Exercise induced angina: Yes, Oldpeak: 1.5, ST Slope: Flat"}], "ideal": "1"} {"input": [{"role": "system", "content": "You are an AI tasked with predicting whether patients are likely to have heart disease. You will be given a description of the patient with relevant medical signals. Respond with only a 1 to signify if the patient is likely to have heart disease, or a 0 if the patient is not likely to have heart disease. Do not respond with any text or disclaimers, only respond with either 1 or 0."}, {"role": "user", "content": "Age: 54 years, Sex: Male, Chest pain type: Non-Anginal Pain, Resting blood pressure: 150 mm Hg, Serum cholesterol: 195 mg/dl, Fasting blood sugar: <= 120 mg/dl, Resting ECG results: Normal, Max heart rate achieved: 122, Exercise induced angina: No, Oldpeak: 0, ST Slope: Upsloping"}], "ideal": "0"} ``` </details>

Heart Disease Prediction

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andrew-openai approved these changes Apr 11, 2023

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andrew-openai merged commit fabca8c into openai:main Apr 11, 2023

nickclyde deleted the heart-disease branch April 13, 2023 16:19

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Heart Disease Prediction #538

Heart Disease Prediction #538

nickclyde commented Mar 31, 2023

andrew-openai left a comment

nickclyde commented Apr 13, 2023

andrew-openai commented Apr 13, 2023 via email

nickclyde commented Apr 13, 2023

Heart Disease Prediction #538

Heart Disease Prediction #538

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nickclyde commented Mar 31, 2023

Thank you for contributing an eval! ♥️

Eval details 📑

Eval name

Eval description

What makes this a useful eval?

Criteria for a good eval ✅

Unique eval value

Eval structure 🏗️

Final checklist 👀

Submission agreement

Email address validation

Limited availability acknowledgement

Submit eval

Eval JSON data

Eval

andrew-openai left a comment

Choose a reason for hiding this comment

nickclyde commented Apr 13, 2023

andrew-openai commented Apr 13, 2023 via email

nickclyde commented Apr 13, 2023