Amazon Comprehend Medical to Extract Medical Data using Machine Learning


By MYBRANDBOOK


Amazon Comprehend Medical to Extract Medical Data using Machine Learning

 

On Tuesday, Amazone has lunched a new comprehensive service, named as Amazon Comprehend Medical, which uses machine learning algorithm to extract the key medical data from patients records and thus can help researchers and healthcare providers to save money and time, make advanced treatment decisions and after all manage clinical trials buy AI systems reported by the Wall street Journal on it.

 

The Amazon's cloud API services, extract the unstructured text data and combines text analysis with machine learning to read patient records that often consist of prescriptions, notes, audio interviews, and test reports. Once these digitized records are fed to the systems and then uploaded to the Comprehend Medical Environment, it picks out and re-organizes information about diagnoses, treatments medication dosages and symptoms associated with the particular patient.

 

Now a days other large tech companies that are increasingly focused on healthcare. Earlier this year Apple launched a feature that lets customers view their hospital medical records on their iPhones, while Google recently hired former Geisinger CEO David Feinberg to unify and lead the healthcare initiatives across its businesses, including search, Google Brain, Google Fit, and Nest.

 

Likely Amazon also moving forward into healthcare include paying almost $1 billion to acquire online prescription service PillPack and new joint venture with Berkshire Hathaway and JP Morgan Chase to improve employee healthcare. In its announcement, Amazon said “identifying this information today is a manual and time-consuming process, which either requires data entry by high skilled medical experts, or teams of developers writing custom code and rules to try and extract the information automatically.” The company claimed that Comprehend Medical can accurately identify “medical conditions, anatomic terms, details of medical tests, treatments, and procedures.” In turn, patients can use the service to help manage different aspects of their treatment, including scheduling healthcare visits and prescription medicines or determining insurance eligibility.

 

To ensure the privacy defined in Comprehend Medical Service Module by uploading of medical records to the cloud for machine-learning analysis might questions from patients, Amazon says patient data is encrypted and can only be unlocked by customers who have a key, and that no data processed will be stored or used for training its algorithms. Comprehend Medical complies with the Health Insurance Portability and Accountability Act (HIPAA).

 

Comprehend Medical is already being previewed by Roche Diagnostics, the Switzerland-headquartered pharmaceutical and diagnostics equipment company, and Fred Hutchison Cancer Research Center in Seattle, which is using it to identify patients for clinical trials. By using the software to analyze “millions of clinical notes,” Amazon says the center was able to reduce the time it needed to process each document “from hours, to seconds.”

 

In a statement, Matthew Trunnell, the CIO of Fred Hutchinson Cancer Research Center, which studies cancer and conducts clinical trials and volunteer studies on new treatments, said “For cancer patients and the researchers dedicated to curing them, time is the limiting resource. The process of developing clinical trials and connecting them with the right patients requires research teams to sift through and label mountains of unstructured medical record data. Amazon Comprehend Medical will reduce this time burden from hours per record to seconds. This is a vital step toward getting researchers rapid access to the information they need when they need it so they can find actionable insights to advance lifesaving therapies for patients.”

 

Improving patient care through technology is a passion and with extremely excitement about the role that Comprehend Medical can play in supporting that mission.

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