Automated management of medical data using expert knowledge and applied complexity science for risk assessment and diagnoses

#This disclosure relates generally to the application of complexity science and expert knowledge to analyses of medical data for evaluation of risk for emergent diseases and diagnoses. More particularly, this disclosure relates to a method, a system, or a storage medium wherein medical data of a person is obtained, feature-sets of associated features of medical conditions are accessed from a medical knowledgebase, and values of the medical data are compared to ranges of values of the features of relevant feature-sets to identify any at-risk medical conditions of the person.

#Diseases associated with age will reach pandemic proportions as the population both increases and ages simply because older humans are exposed to potential risk for longer periods of time. For instance, by 2020, age-associated cardiovascular disease alone will cause approximately 25 to 30 percent of all deaths in the world. Presently, treatment of age-associated diseases is predominately directed toward secondary management of observed clinical manifestations, risk factors, and associated adverse events. Risk factors are often considered consequences of underlying physiologic perturbations but these risk factors in and of themselves are not the primary cause of the disease manifestations ascribed to them. Risk factors are more likened to a consequence and not a cause.

#Historically, patients have been assessed and managed on the basis of the presence or absence of these clinical risk factors and overt manifestations. By definition, risk factor management requires the patient to have a disease. The severity of the disease, moreover, may be indeterminate and an individual's response to therapy or the degree of preexisting disease burden is uncertain. This kind of risk management is not only user-unfriendly but in some instances is a disservice to the patient and the medical community. As an example, diabetes having a duration of one week most likely has a completely different risk burden than diabetes of 10 years' duration, so classification and treatment of these two disease states should be different and more importantly individualized.

#FIG. 1 is a block diagram of a computer system and a network in accordance with an embodiment;

#FIG. 2 is a schematic illustration of using complexity signs with respect to an embodiment;

#FIGS. 3-5 are flow-charts of the methods by which medical data is analyzed relative to identifying at-risk medical condition, in accordance with an embodiment;

#The description includes reference to the accompanying drawings. The invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather the illustrated embodiments are provided so that this disclosure is thorough and complete, and fully conveys the scope of the invention to those skilled in the art. Like numbers refer to like elements throughout.

#As will be appreciated by one of skill in the art, the embodiments described herein are a method, a data processing system, a computer program product and a service that maintain a knowledgebase 140, a plurality of associative algorithms 150 and a plurality of feature-sets 160 each having a set of highly-associated features that identify a medical condition and that apply one or more associative algorithms that are applied to evaluate the input medical data representing the magnitude of the features, wherein an individual's risk of a medical condition or disease is identified and determined. Determining the individual's risk of a medical condition or disease includes quantifying a physiologic variance from normal. Accordingly, components of the embodiments may take the form of a hardware aspect or an embodiment combining software and hardware aspects. Furthermore, components of the embodiments may take the form of a computer program product on a computer-usable storage medium having computer-usable code embodied in the medium. Any suitable computer readable medium may be utilized including solid-state storage devices, hard disks, CD-ROMs, optical storage devices, portable memory, a transmission media such as those supporting the Internet or an intranet, or magnetic storage devices.

#Computer program source code of the software components that maintain a knowledgebase 140, a plurality of associative algorithms 150 and a plurality of feature-sets 160 as described herein may be written in an object-oriented programming language such as C, Java, Smalltalk or C++. Object code of the components comprising the knowledgebase 140, a plurality of associative algorithms 150 and a plurality of feature-sets 160 may execute entirely on an individual server or client, partly on an individual or a backup server or client, partly on the individual or backup server or client and partly on a remote server or client or entirely on the remote server or client. In the latter scenario, the remote server or client may be connected to the individual or backup server or client through a local area network (LAN) or a wide area network (WAN), or the connection may be made to the remote server or client via the Internet using an Internet Service Provider.