The invention discloses a
biological specimen-based special-type
interstitial lung disease association
analysis method, which comprises the following steps of: acquiring multi-dimensional
omics and
pathological characteristic data through a
biological specimen omics data mining platform, and realizing characteristic differentiation grouping through a
lung interstitial characteristic deep clustering identification
algorithm, inputting the grouped data into an
interstitial lung disease subtype
typing prediction model, completing preliminary
typing in combination with clinical phenotypes, capturing a
pathological feature dynamic change rule by using a specimen
pathological feature
time sequence evolution model, integrating clustering,
typing and
time sequence evolution results through a platform
feature fusion module to construct a
feature set, and carrying out classification on the
feature set; and establishing a parameter mapping relation of each dimension through a
correlation analysis module. According to the method, feature input optimization, sequential
sequence analysis and multi-dimensional feature efficient fusion are realized, the pertinence and reliability of
correlation analysis are comprehensively improved, and
technical support is provided for precise diagnosis and treatment of diseases.