Systems and methods for predicting pain level

A system for predicting a pain level of an osteoarthritis (OA) patient, the system comprising:

• a processor;
• and
• a machine-readable medium in operable communication with the processor and having instructions stored thereon that, when executed by the processor, perform the following steps:
• developing a set of classifiers, the set of classifiers comprising three classifiers corresponding to a first category, a second category, and a third category, respectively;
• training the set of classifiers;
• testing the set of classifiers;
• and
• using the set of classifiers to predict the pain level of the OA patient at a future visit intended to assess the pain level,
• the first category being that pain has improved for the OA patient since a previous visit, the second category being that pain has remained unchanged for the OA patient since the previous visit, and the third category being that pain has worsened for the OA patient since the previous visit,
• the developing, training, testing, and using of the set of classifiers comprising using a machine learning (ML) technique that factors in sex, age, body mass index, injury factors, occupation factors, medical history, strength performance measures, and physical activity factors for the OA patient,
• the ML technique being an RNN with three single class, multi-label RNN classifiers,
• the training of the set of classifiers comprising training the set of classifiers using a dataset with known values,
• the testing of the set of classifiers comprising testing the set of classifiers using the dataset with known values,
• the dataset being broken into a first sub-dataset to be used for the training of the set of classifiers and a second sub-dataset to be used for the testing of the set of classifiers,
• the developing of the set of classifiers comprising feature selection,
• the training of the set of classifiers comprising normalization of data obtained from the dataset,
• the using of the set of classifiers to predict the pain level of the OA patient comprising using the set of classifiers to predict the pain level of the OA patient at an Nth visit based on features of the set of classifiers reported on all visits up to an (N−1)th visit,
• the feature selection comprising utilizing a Fisher coefficient and a squared Euclidean distance on the features of the set of classifiers,
• the training of the set of classifiers comprising rescaling the age and body mass index for the OA patient to a unified range from 0 to 1,
• the feature selection comprising decomposing first features, of the features of the set of classifiers, into independent constituents and aggregating second features, of the features of the set of classifiers, into an aggregated group of features, and
• the feature selection further comprising binarizing all of the features of the set of classifiers using a binary threshold function where a feature value of 1 is assigned to a respective feature if it is higher than a threshold of the binary threshold function and a feature value of 0 is assigned to the respective feature if it is lower than the threshold of the binary threshold function.