Systems and methods for disease progression modeling

A method for detecting disease state transitions of glaucoma, said method comprising:

• receiving, by a first processor of a disease progression model, patient data comprising structural degeneration data and functional degeneration data related to a patient, via a secure interface, from a second processor associated with a healthcare provider terminal;
• identifying, by the first processor, based on the patient data, a first disease state of a plurality of non-overlapping disease states of glaucoma each associated with a predetermined range of one or both of functional and structural degeneration values;
• identifying, by the first processor, based on the patient data, a second disease state of the plurality of disease states, wherein the second disease state is non-adjacent to the first disease state;
• determining, with the first processor using a two dimensional continuous-time hidden Markov model, a most probable path between the first disease state and the second disease state, wherein:
• the most probable path comprises one or more intermediary disease states of the plurality of disease states, and
• each intermediary disease state is adjacent to one or more of the first disease state, the second disease state, and another intermediary disease state;
• determining, with the first processor, a most probable next disease state for one or more disease states along the most probable path using the hidden Markov model;
• comparing, by the first processor, the most probable next disease state with the second disease state;
• determining, with the first processor, that a transition between disease states along the most probable path has a fast structural and/or functional progression based on the comparison;
• when the transition is determined to be the fast structural and/or functional progression, training, by the first processor, the hidden Markov model based on patient progression data corresponding to a plurality of patients having the fast structural and/or functional progression;
• and
• redetermining, with the first processor, the most probable next disease state for one or more disease states along the most probable path using the hidden Markov model.

A system for determining a disease state transition path of glaucoma, said method comprising:

• a storage device for storing instructions;
• and
• a processor configured to execute the instructions in the storage device to:
• receive patient data comprising structural degeneration data and functional degeneration data related to a patient;
• identify, based on the patient data, two or more disease states of a plurality of nonoverlapping disease states of glaucoma each associated with a predetermined range of one or both of functional and structural degeneration values;
• predict, using a two dimensional continuous-time hidden Markov model, a most probable path between a sequential pair of non-adjacent disease states of the two or more disease states, wherein the most probable path comprises one or more intermediary disease states of the plurality of disease states,
• wherein each intermediary disease state is adjacent to at least one of the sequential pair of non-adjacent disease states and another intermediary disease state;
• train the hidden Markov model based on patient progression data corresponding to a plurality of patients having an attribute;
• predict, using a trained hidden Markov model, an updated most probable path between the sequential pair of non-adjacent disease states;
• predict a disease progression rate based on the updated most probable path;
• compare the predicted disease progression rate with a predetermined disease progression rate;
• and
• determine whether a patient has the attribute based on the comparison.