Predictive risk assessment in patient and health modeling
A patient's health is modeled through multiple scenarios. A base model incorporates rules that govern a response by a human being to one or more diseases, as well as a relation between health metrics and the diseases. Health attributes of the patient are obtained. From the base model and the health attributes, a patient model is generated. The patient model is modeled under different parameters to generate health metrics. From an analysis of the parameters and the resulting health metrics, occurrence probabilities for each of the sets of parameters are determined. Risks are identified, which indicate a likelihood of the patient transitioning from the initial state to an adverse outcome such as a diseased state. A report provides a diagnosis of the patient and one or more remedies/interventions that are predicted, based on the plural sets of parameters and resulting health metrics, to avoid or prevent an adverse outcome.
1 . A computer-implemented method of providing a digital computer simulation of health of a human patient, comprising:
obtaining in computer memory, a base model of a human being, the base model being a mathematical model incorporating rules that govern 1) a response by the human being to a disease, and 2) a relation between health metrics and the disease, the health metrics including a resistance metric indicating a measure of resistance to the disease, said obtaining being automatically performed by a processor;
obtaining in computer memory, by the processor, health attributes of the human patient, the health attributes including at least one of a genetic trait, a blood lipid profile, and a representation of a medical imaging report;
responsively generating in computer memory, by the processor, a patient model from the base model and the health attributes of the human patient, the patient model incorporating a patient-specific representation of the rules of the base model and a representation of the health attributes, the patient-specific representation of the rules of the base model including rules that govern response by the patient to a certain disease;
generating in computer memory, by the processor, plural sets of parameters, each of the plural sets of parameters indicating respective variables having a causal relation to the health metrics;
automatically generating a computer precision medicine evaluation of the patient by executing the patient model using a deterministic model, said executing being performed by the processor and producing: (a) respective simulations of the health metrics of the patient model under the plural sets of parameters over a given simulated time period,
(b) plural resistance metric results based on the deterministic model, each of the plural resistance metric results indicating a change in the resistance metric under a respective set of parameters over the given simulated time period, and
(c) an occurrence probability of the patient model transitioning from an initial state to each of a plurality of successive states during the given simulated time period, each of the successive states corresponding to a respective one of the plural sets of parameters, wherein the produced respective simulations, plural resistance metric results, and occurrence probability support the computer precision medicine evaluation of the patient with respect to at least the certain disease;
automatically determining a risk, by the processor, based on the produced simulations, the plural resistance metric results, and the occurrence probability of the generated precision medicine evaluation, the risk being of the patient model transitioning from the initial state to a diseased state, the diseased state representing the patient being afflicted by the certain disease;
automatically determining, by the processor, a diagnosis and a remedy, the remedy being determined by 1) identifying a subset of the health metrics that are negatively correlated with the diseased state, and 2) identifying the remedy as one of the respective variables associated with the identified subset of health metrics, the remedy identifying a modification to the patient model that reduces the determined risk;
generating as clinically-actionable output, by the processor, a map indicating 1) the processor determined diagnosis of the patient based on the determined risk, and 2) the determined remedy, said generating output being in a manner controlling computer diagnosis of patent-specific outcomes; and
responsive to the clinically-actionable output, treating the patient in accordance with the remedy by administering to the patient one or more of: a medication, physical therapy, and other therapy.
2 . The method of claim 1 , wherein the base model incorporates rules that govern a response by the human being to at least one of immune reactivations, irregular immune response, autoimmunity, preexisting conditions and environmental parameters.
3 . The method of claim 1 , wherein the health metrics of the base model in computer memory include indications of human health dependability (HHD), the HHD being a measure of an integrity of an immune system to counteract the disease.
4 . The method of claim 1 , wherein the certain disease is a first disease; and wherein the plural sets of parameters in computer memory include at least one set of parameters indicating comorbidity of the first disease and a second disease.
5 . The method of claim 1 , wherein the certain disease is one of a cancer, a virus, and a bacterial infection.
6 . The method of claim 1 , further comprising:
identifying, by the processor, a correspondence between a subset of the health attributes, the produced plural resistance metric results, and the determined risk; and
determining, by the processor, the diagnosis based on the identified correspondence, the diagnosis indicating the subset of health attributes that exceed a threshold correspondence with the determined risk.
7 . The method of claim 1 , further comprising:
updating the patient model in computer memory to incorporate the modification; and
determining an expected improvement to a measure of an integrity of the patient's immune system to counteract the certain disease, the updating and determining being automatically performed by the processor.
8 . The method of claim 7 , further comprising reporting the remedy to a user.
9 . The method of claim 7 , wherein determining the remedy includes the processor:
generating an additional set of parameters in computer memory, the additional set of parameters indicating a health intervention, the health intervention including at least one of a medication, a therapy, and a diet;
responsively modeling the health metrics of the patient model in computer memory under the additional set of parameters to generate a resistance metric result; and
automatically identifying the remedy based on the health metrics associated with the health intervention.
10 . The method of claim 1 , wherein:
the map relates the diseased state to 1) corresponding instances of the plural sets of parameters and 2) the occurrence probability of the patient model transitioning from the initial state to the plurality of successive states; and
further comprising the processor determining, based on the map, at least one risk for at least one of the successive states of the patient model in computer memory, the at least one risk defining a probability of an outcome of the patient model including the diseased state.
11 . The method of claim 1 , further comprising the processor generating in computer memory a lookup table cross-referencing states of the patient model to corresponding ones of the risk.
12 . The method of claim 11 , wherein determining the risk includes the processor accessing the lookup table in computer memory using information on a given state of the patient or patient model.
13 . The method of claim 11 , further comprising the processor:
responsively analyzing a state of the patient; and
wherein determining the risk includes accessing the lookup table in computer memory using information on the state of the patient.
14 . The method of claim 11 , wherein the lookup table cross-references the states of the patient model to the corresponding ones of the risk and at least one remedy.
15 . The method of claim 1 , wherein the generating a patient model in computer memory includes the processor:
generating a dependency graph having a plurality of nodes representing the health attributes; and configuring dependencies between the plurality of nodes based on the rules.
16 . A non-transitory computer-readable medium comprising instructions that, when executed by a computer, provide a digital computer simulation of health of a human patient including causing the computer to:
obtain in computer memory, by a processor, a base model of a human being, the base model being a mathematical model incorporating rules that govern 1) a response by the human being to a disease, and 2) a relation between health metrics and the disease, the health metrics including a resistance metric indicating a measure of resistance to the disease;
obtain in computer memory, by the processor, health attributes of a human patient, the health attributes including at least one of a genetic trait, a blood lipid profile, and a representation of a medical imaging report;
responsively generate in computer memory, by the processor, a patient model from the base model and the health attributes of the human patient, the patient model incorporating a patient-specific representation of the rules of the base model and a representation of the health attributes, the patient-specific representation of the rules of the base model including rules that govern response by the patient to a certain disease;
generate in computer memory, by the processor, plural sets of parameters, each of the plural sets of parameters indicating respective variables having a causal relation to the health metrics;
automatically generate a computer precision medicine evaluation of the patient by executing the patient model using a deterministic model, said executing being performed by the processor, and producing: (a) respective computer simulations of the health metrics of the patient model under the plural sets of parameters over a given simulated time period,
(b) plural resistance metric results based on the deterministic model, each of the plural resistance metric results indicating a change in the resistance metric under a respective set of parameters over the given simulated time period, and
(c) an occurrence probability of the patient model transitioning from an initial state to each of a plurality of successive states during the given simulated time period, each of the successive states corresponding to a respective one of the plural sets of parameters, wherein the produced respective simulations, plural resistance metric results, and occurrence probability support the computer precision medicine evaluation of the patent with respect to at least the certain disease;
automatically determine a risk, by the processor, based on the produced simulations, the plural resistance metric results, and the occurrence probability of the generated precision medicine evaluation, the risk being of the patient model transitioning from the initial state to a diseased state, the diseased state representing the patient being afflicted by the certain disease;
automatically determine, by the processor, a diagnosis and a remedy, the remedy being determined by 1) identifying a subset of the health metrics that are negatively correlated with the diseased state, and 2) identifying the remedy as one of the respective variables associated with the identified subset of the health metrics, the remedy identifying a modification to the patient model that reduces the determined risk;
generate as clinically-actionable output, by the processor, a map indicating 1) the processor determined diagnosis of the patient based on the determined risk, and 2) the determined remedy, said generating output being in a manner controlling computer diagnosis of patient-specific outcomes; and
responsive to the clinically-actionable output, provide treatment to the patient in accordance with the remedy by administering one or more of: a medication, physical therapy, and other therapy.
17 . The computer-readable medium of claim 16 , further comprising instructions causing the computer to:
identify a correspondence between a subset of the health attributes, the produced plural resistance metric results, and the determined risk; and
determine the diagnosis based on the identified correspondence, the diagnosis indicating the subset of health attributes that exceed a threshold correspondence with the determined risk.
18 . The computer-readable medium of claim 16 , further comprising instructions causing the computer to:
automatically update in computer memory the patient model to incorporate the modification.