Abstract: Methods and systems for generating a report that identifies wound and fall risks for patients are disclosed. A method includes obtaining historical patient data via one or more databases communicatively coupled with the computer system. The method includes generating a training set including a subset of the historical patient data, training the injury detection system using the training set, and determining, based on new patient data provided to the injury detection system, a patient's fall risk and/or wound risk. The method further includes generating, based on the patient's fall risk and/or wound risk, a patient report and providing caretakers remote access to the patient report. The patient report presents the patient's fall risk and/or wound risk and includes one or more user interface elements for receiving additional information about the patient.

Abstract: Apparatus for automatic segmentation of non-aligned data sequences comprising structural domains to identify and construct models of the structural domains. The apparatus comprises a soft clustering unit, a refinement unit and an annealing unit. The soft clustering unit iteratively partitions the data sequences and trains variable memory Markov sources, created using a prediction suffix tree data structure, on the data until convergence is reached. The clustering unit also eliminates sources showing low relationships with the data. The refinement unit is connected to the soft clustering unit and splits and perturbs the sources following convergence, to repeat the iterative partitioning at the soft clustering unit, thereby to refine the model.