For required domain knowledge to be incorporated into their systems, participants can access publicly available biomedical repositories such as PubMed abstracts, other web resources.
For Example:
You can refer to this step-by-step guide for accessing Pubmed: https://www.kaggle.com/code/binitagiri/extract-data-from-pubmed-using-python
PI MED (Patient Information Medical) API is an application programming interface designed to facilitate the handling, processing, and analysis of medical and healthcare-related data.
Kaggle hosts various datasets and repositories that can be used with the PI MED API (Patient Information Medical API). Here’s how you can find and utilize relevant Kaggle datasets for this purpose:
1. **Search for Medical Datasets on Kaggle**: Go to the Kaggle website and use the search bar to find medical datasets. Keywords like "medical records," "patient data," "healthcare," "EHR (Electronic Health Records)," or specific medical conditions (e.g., "diabetes," "cardiology") can help you find relevant datasets.
2. **Popular Medical Datasets on Kaggle**:
**MIMIC-III Clinical Database**: Contains de-identified health-related data associated with over 40,000 critical care patients. (Complete mandotory certification before using MIMIC data to buid your models)
**COVID-19 Open Research Dataset (CORD-19)**: A dataset of scholarly articles about COVID-19 and related coronaviruses.
**Hospital Readmissions**: Contains data about patient admissions and readmissions.
3. **Using Kaggle Datasets with PI MED API**:
Once you find a relevant dataset, you can download it from Kaggle. Make sure to comply with any licensing or usage restrictions specified by the dataset authors. Preprocess the data as required by the PI MED API. This might involve cleaning the data, converting formats, or extracting specific fields.
Additional Resources:
NCBI Documentation: Review the NCBI E-utilities documentation for detailed information on using the PubMed API.
BioPython: Consider using the BioPython library for more advanced biological data processing, which includes interfaces to various bioinformatics databases including PubMed.
Please note, the above examples are just to aid the participants, who are new to this domain, with some pointers on how to access public data sources. This is by NO MEANS a guideline on specific data and procedures to be used for the task.
(Some examples are generated by ChatGPT)
We will provide required Question and Answers data to the registered participants. It will be shared in two parts:
A small set to aid in the training/model building
Test set for evaluation