The present application relates to the technical field of
machine learning, and particularly relates to a preclinical
drug experiment quality evaluation method based on
machine learning, comprising: obtaining a plurality of experiment samples, each experiment sample comprising a
dose level and an observation value of at least one biological index; determining a trend residual of each biological index of each experiment sample; determining a biological index evaluation weight sequence composed of quality evaluation weights of all biological indexes; determining a composite distance between any two experiment samples; determining a quality evaluation index of each experiment sample; and evaluating the quality of preclinical
drug experiments based on the quality evaluation index of each experiment sample. By constructing a
dose effect trend function, combining the trend residual and the weight, establishing a composite
distance model fusing pharmacological trends, realizing
adaptive selection of neighborhood parameters, improving the accuracy and stability of
anomaly detection, overcoming the limitations of traditional
machine learning LOF
algorithm, and enhancing the evaluation reliability.