Methods for determining treatment for cancer patients

A computer-implemented method comprising:

• obtaining model training data, wherein the model training data involves a first set of cancer patients that underwent cancer treatments, wherein the model training data associates (i) results from laboratory tests conducted on the first set of cancer patients and tumor types of the first set of cancer patients with (ii) whether individuals from the first set of cancer patients died within a threshold number of weeks from initiation of the cancer treatments;
• generate a plurality of variations of a gradient boosting machine learning model, wherein generating each of the variations comprises:
• training, based on the model training data, a sequential series of decision trees until a stopping condition is reached using a respective set of hyperparameters, wherein each subsequent decision tree of the sequential series of decision trees is constructed based on residual values of its preceding decision tree of the sequential series of decision trees, and wherein the respective sets of hyperparameters are different for each of the variations;
• and
• forming an additive function of the sequential series of decision trees, wherein each of the variations of the gradient boosting machine learning model is usable to predict whether a further cancer patient dies within the threshold number of weeks based on the additive function applied to results from the laboratory tests as conducted on the further cancer patient and a tumor type of the further cancer patient;
• applying each of the variations of the gradient boosting machine learning model to the model training data;
• and
• selecting, from among the variations of the gradient boosting machine learning model, a particular variation that provides predictions of whether the first set of cancer patients die within the threshold number of weeks from initiation of the cancer treatments within a threshold degree of accuracy.

A computer-implemented method comprising:

• obtaining a gradient boosting machine learning model, wherein the gradient boosting machine learning model was trained on model training data, wherein the model training data involves a first set of cancer patients that underwent cancer treatments, wherein the model training data associates (i) results from laboratory tests conducted on the first set of cancer patients and tumor types of the first set of cancer patients with (ii) whether individuals from the first set of cancer patients died within a threshold number of weeks from initiation of the cancer treatments, wherein training the gradient boosting machine learning model involved:
• (i) generating a plurality of variations of a gradient boosting machine learning model, wherein generating each of the variations involved:
• training, based on the model training data, a sequential series of decision trees until a stopping condition was reached using a respective set of hyperparameters, wherein each subsequent decision tree of the sequential series of decision trees was constructed based on residual values of its preceding decision tree of the sequential series of decision trees, and wherein the respective sets of hyperparameters are different for each of the variations;
• and
• forming an additive function of the sequential series of decision trees;
• (ii) applying each of the variations of the gradient boosting machine learning model to the model training data;
• and
• (iii) selecting, from among the variations of the gradient boosting machine learning model, a particular variation that provides predictions of whether the first set of cancer patients die within the threshold number of weeks from initiation of the cancer treatments within a threshold degree of accuracy;
• obtaining results from the laboratory tests as conducted on a further cancer patient;
• obtaining a tumor type of the further cancer patient;
• applying the gradient boosting machine learning model to results from the laboratory tests as conducted on a further cancer patient and the tumor type of the further cancer patient;
• and
• receiving, from the gradient boosting machine learning model, a prediction of whether the further cancer patient dies within the threshold number of weeks.