The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

Provided are methods, system and software for diagnosis, prediction and prognosis of a cancer patient based on the quantitative level of a set of biomarkers. Also provided is a database for the purpose of recording the quantitative level of a set of biomarkers.

Provided are methods, system and software for diagnosis, prediction and prognosis of a cancer patient based on the quantitative level of a set of biomarkers. Also provided is a database for the purpose of recording the quantitative level of a set of biomarkers.

A system for assessing risks of T2DM complications includes: a data acquisition module obtaining and inputting assessment parameters of a patient with T2DM into a risk assessment module; and the risk assessment module inputting the assessment parameters into a number of risk equations and using it to calculate risk values of the complication occurring after a period of time. The risk equation for all diabetic complications (i,j) is:

r.sub.a(t,i,j)=1−exp{[H(t.sub.0)−H(t.sub.1)]C.sub.a(t,i,j)}

r.sub.a(t, i, j) is the risk value for the patient to develop the complication j from the current disease i at age t. t.sub.0 is an age of one patient at a state of the disease i. t.sub.1 is an age of the patient after the period of time. t is an age between t.sub.0 and t.sub.1. H(t.sub.0) and H(t.sub.1) are hazards of the complication occurring at the age t.sub.0 and the age t.sub.1, respectively. C.sub.a(t, i, j) is a Cox proportional hazards regression expression, and is represented by:

C.sub.a(t,i,j)=exp(R.sub.a(t,i,j))

R.sub.a(t, i, j) is an influence degree of risk factors X on the complication j.

A system for assessing risks of T2DM complications includes: a data acquisition module obtaining and inputting assessment parameters of a patient with T2DM into a risk assessment module; and the risk assessment module inputting the assessment parameters into a number of risk equations and using it to calculate risk values of the complication occurring after a period of time. The risk equation for all diabetic complications (i,j) is:

r.sub.a(t,i,j)=1−exp{[H(t.sub.0)−H(t.sub.1)]C.sub.a(t,i,j)}

r.sub.a(t, i, j) is the risk value for the patient to develop the complication j from the current disease i at age t. t.sub.0 is an age of one patient at a state of the disease i. t.sub.1 is an age of the patient after the period of time. t is an age between t.sub.0 and t.sub.1. H(t.sub.0) and H(t.sub.1) are hazards of the complication occurring at the age t.sub.0 and the age t.sub.1, respectively. C.sub.a(t, i, j) is a Cox proportional hazards regression expression, and is represented by:

C.sub.a(t,i,j)=exp(R.sub.a(t,i,j))

R.sub.a(t, i, j) is an influence degree of risk factors X on the complication j.

A pain management system for treating pain and/or detecting potential drug abuse in a patient suffering from pain, the system comprising at least one human machine interface (HMI), operable to acquire data generated by a patient responsive to pain that the patient experiences and data responsive to the patients intake of a drug for controlling the pain; at least one processor operable to process the pain and drug intake data to generate a pain control regimen; and at least one communication interface operable to support communications between an attending medical professional and the at least one HMI and/or the processor to enable the attending medical professional to access the pain and drug intake data and the pain control regimen

A pain management system for treating pain and/or detecting potential drug abuse in a patient suffering from pain, the system comprising at least one human machine interface (HMI), operable to acquire data generated by a patient responsive to pain that the patient experiences and data responsive to the patients intake of a drug for controlling the pain; at least one processor operable to process the pain and drug intake data to generate a pain control regimen; and at least one communication interface operable to support communications between an attending medical professional and the at least one HMI and/or the processor to enable the attending medical professional to access the pain and drug intake data and the pain control regimen

Systems and methods for providing a universal platform for at-home health testing and diagnostics are provided herein. In particular, a health testing and diagnostic platform is provided to connect medical providers with patients and to generate a unique, private testing environment. In some embodiments, the testing environment may facilitate administration of a medical test to a patient with the guidance of a proctor. In some embodiments, the patient may be provided with step-by-step instructions for test administration by the proctor within a testing environment. The platform may display unique, dynamic testing interfaces to the patient and proctor to ensure proper testing protocols and accurate test result verification.

Systems and methods for providing a universal platform for at-home health testing and diagnostics are provided herein. In particular, a health testing and diagnostic platform is provided to connect medical providers with patients and to generate a unique, private testing environment. In some embodiments, the testing environment may facilitate administration of a medical test to a patient with the guidance of a proctor. In some embodiments, the patient may be provided with step-by-step instructions for test administration by the proctor within a testing environment. The platform may display unique, dynamic testing interfaces to the patient and proctor to ensure proper testing protocols and accurate test result verification.