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📢 The 2023 AI CUP Autumn Competition is here! 📢

2023 AI CUP Autumn Competition - "Deciphering Clinical Cases, Letting Data Tell the Story" is now live!


In an effort to reduce repetitive and costly medical tests 🩻

and minimize preventable errors in the healthcare system,

the use of electronic health records (EHR) has emerged.📇



EHR digitally stores medical information,

such as patient paper-based medical records,

in order to reduce physician examination time and improve efficiency. 📈


For example,

in the case of sepsis models adopted by the North Oak Tree Healthcare System in Louisiana in 2017,

when a patient's score reaches a specific value,

EHR alerts the physician. ⚠️


This allows physicians to closely monitor patients,

resulting in an 18% reduction in deaths caused by sepsis. ⬇️


EHR is a significant advancement compared to paper-based records. 🧗



However,

more than half of the physicians find EHR's difficult interface and the challenge of integrating different medical services burdensome 😵‍💫,

leading to increased physician burnout 😵.


The adoption of EHR is not as straightforward as one might imagine.


For instance,

some physicians may label strawberry allergies in clinical records rather than in the allergy section,

making it difficult for predictive models to accurately predict patient allergy information 😧.



Apart from the confusion in patient data registration,

which leads to inaccurate disease prediction ❗️,

there is a certain possibility that training large language models can potentially compromise patient privacy due to the memory and interaction capabilities of these models. 🗣️


However,

in the era of digital systems,

the application of EHR has become mainstream. 📇


Therefore,

the removal of patient privacy information from EHR has become an unavoidable issue. 😾


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Would you like to practice how to extract patient privacy data ✂️?

Do you want to know how to normalize time information 📈?

Hurry and sign up for the Ministry of Education's competition:

"Clinical Text Deidentification and Temporal Information Normalization Competition: Deciphering Clinical Cases, Letting Data Tell the Story" 😼.


🖱️ Click here to register 🖱️:


In addition to the Clinical Text Deidentification and Temporal Information Normalization Competition,

there are also competitions such as The Go Strength Imitation and Go Style Recognition Competition waiting for your registration 😸.








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