Methods of graft versus host disease diagnosis

A method of classifying a sample as originating from a subject suffering from GVHD or a subject not suffering from GVHD, the method comprising:

• a. receiving measurements of at least two parameters in a fluid sample, wherein said parameters comprise at least one clinical parameter and at least one cfDNA parameter, and wherein said clinical parameter is selected from the group consisting of:
• liver enzyme level, white blood cell (WBC) count, and hemoglobin level and said cfDNA parameter is selected from total cfDNA quantity and quantity of cfDNA from a specific tissue or cell type;
• b. applying a trained machine learning (ML) model to said at least two received parameters;
• and
• c. treating a subject diagnosed with GVHD with an anti-GVHD therapeutic agent or tapering immunosuppression treatment to a subject that is not diagnosed with GVHD;
• thereby classifying a sample.

A method comprising:

• training a machine learning model to predict the presence of graft versus host disease (GVHD) in a subject, on a training set, the method comprising:
• i. extracting cfDNA from a fluid sample;
• ii. quantifying the amount of cfDNA in said fluid sample;
• and
• iii. determining at least one clinical parameter in said fluid sample;
• wherein said training set is generated by labeling cfDNA quantities in samples and at least one clinical parameter as coming from a subject that suffers from GVHD or does not suffer from GVHD and compiling a plurality of cfDNA quantities, clinical parameters and their labels together to form said training set, wherein said plurality comprises labels of both subjects that suffer from GVHD and subjects that do not suffer from GVHD and wherein said clinical parameter is selected from the group consisting of:
• liver enzyme level, white blood cell (WBC) count and hemoglobin level.