Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
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Tóm tắt
Dữ liệu lớn và y học chính xác, hai thách thức chính trong thời đại đương đại đối với dịch tễ học, được xem xét một cách nghiêm túc từ hai góc độ khác nhau. Ở phần 1, dữ liệu lớn được thu thập cho mục đích nghiên cứu (Dữ liệu nghiên cứu lớn) và dữ liệu lớn được sử dụng cho nghiên cứu mà mặc dù được thu thập cho các mục đích chính khác (Dữ liệu thứ cấp lớn) được thảo luận dưới ánh sáng của yêu cầu cơ bản chung về tính hợp lệ của dữ liệu, vượt lên trên "kích thước". Y học chính xác được đề cập phát triển điểm chính rằng các rủi ro tương đối cao thường là cần thiết để làm cho một biến hoặc sự kết hợp của các biến trở nên phù hợp cho việc dự đoán sự xuất hiện của bệnh, kết quả hoặc phản ứng với điều trị; sự phát triển thương mại của các xét nghiệm được cho là dự đoán mà có tính hợp lệ không rõ ràng hoặc kém cũng được đề cập. Phần 2 đề xuất một phương pháp "dịch tễ học thông thái" nhằm: (a) lựa chọn trong một bối cảnh được in dấu bởi Dữ liệu lớn và y học chính xác - các dự án nghiên cứu dịch tễ học thực sự liên quan đến sức khỏe cộng đồng, (b) đào tạo các nhà dịch tễ học, (c) điều tra tác động đến thực hành lâm sàng và mối quan hệ giữa bác sĩ và bệnh nhân của dòng dữ liệu lớn và y học điện tử, và (d) làm rõ rằng liệu ngày nay "sức khỏe" có thể được định nghĩa lại hay không - như một số người khẳng định, theo các thuật ngữ thuần túy công nghệ.
Từ khóa
#Dữ liệu lớn #y học chính xác #dịch tễ học #tính hợp lệ của dữ liệu #rủi ro tương đối.Tài liệu tham khảo
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