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Post #545
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⛑⛑⛑Medical data: what to observe when working with the health service
The main problem with health data is its vulnerability. They contain confidential information protected by the Health Insurance Portability and Accountability Act (HIPAA) and may not be used without express consent. In the medical field, sensitive details are referred to as protected health information (PHI). Here are a few factors to consider when working with medical datasets:
Protected Health Information (PHI) is contained in various medical documents: emails, clinical notes, test results, or CT scans. While diagnoses or medical prescriptions are not considered sensitive information in and of themselves, they are subject to HIPAA when matched against so-called identifiers: names, dates, contacts, social security or account numbers, photographs of individuals, or other elements that can be used to locate or identify a particular patient, as well as contact him.
Anonymization of medical data and removal of personal information from them. Personal identifiers and even parts of them (such as initials) must be disposed of before medical data can be used for research or business purposes. There are two ways to do this - anonymization and deletion of personal information. Anonymization is the permanent elimination of all sensitive data. Removing personal information (de-identification) only encrypts personal information and hides it in separate datasets. Later, identifiers can be re-associated with health information.
Medical data markup. Any unstructured data (texts, images or audio files) for training machine learning models requires markup or annotation. This is the process of adding descriptive elements (labels or tags) to data blocks so that the machine can understand what is in the image or text. When working with healthcare data, healthcare professionals should perform the markup. The hourly cost of their services is much higher than that of annotators who do not have domain knowledge. This creates another barrier to the generation of high-quality medical datasets.
In summary, preparing medical data for machine learning typically requires more money and time than the average for other industries due to strict regulation and the involvement of highly paid subject matter experts. Consequently, we are seeing a situation where public medical datasets are relatively rare and are attracting serious attention from researchers, data scientists, and companies working on AI solutions in the field of medicine.
The main problem with health data is its vulnerability. They contain confidential information protected by the Health Insurance Portability and Accountability Act (HIPAA) and may not be used without express consent. In the medical field, sensitive details are referred to as protected health information (PHI). Here are a few factors to consider when working with medical datasets:
Protected Health Information (PHI) is contained in various medical documents: emails, clinical notes, test results, or CT scans. While diagnoses or medical prescriptions are not considered sensitive information in and of themselves, they are subject to HIPAA when matched against so-called identifiers: names, dates, contacts, social security or account numbers, photographs of individuals, or other elements that can be used to locate or identify a particular patient, as well as contact him.
Anonymization of medical data and removal of personal information from them. Personal identifiers and even parts of them (such as initials) must be disposed of before medical data can be used for research or business purposes. There are two ways to do this - anonymization and deletion of personal information. Anonymization is the permanent elimination of all sensitive data. Removing personal information (de-identification) only encrypts personal information and hides it in separate datasets. Later, identifiers can be re-associated with health information.
Medical data markup. Any unstructured data (texts, images or audio files) for training machine learning models requires markup or annotation. This is the process of adding descriptive elements (labels or tags) to data blocks so that the machine can understand what is in the image or text. When working with healthcare data, healthcare professionals should perform the markup. The hourly cost of their services is much higher than that of annotators who do not have domain knowledge. This creates another barrier to the generation of high-quality medical datasets.
In summary, preparing medical data for machine learning typically requires more money and time than the average for other industries due to strict regulation and the involvement of highly paid subject matter experts. Consequently, we are seeing a situation where public medical datasets are relatively rare and are attracting serious attention from researchers, data scientists, and companies working on AI solutions in the field of medicine.
