Background:Since the late 1980s, several taxonomies have been developed to help map and describe the interrelationships of complementary and alternative medicine (CAM) modalities. In these taxonomies, several issues are often incompletely addressed: A simple categorization process that clearly isolates a modality to a single conceptual categoryClear delineation of verticality—that is, a differentiation of scale being observed from individually applied techniques, through modalities (therapies), to whole medical systemsRecognition of CAM as part of the general field of health careMethods:Development of the Integrated Taxonomy of Health Care (ITHC) involved three stages: Development of a precise, uniform health glossaryAnalysis of the extant taxonomiesUse of an iterative process of classifying modalities and medical systems into categories until a failure to singularly classify a modality occurred, requiring a return to the glossary and adjustment of the classifying protocolResults:A full vertical taxonomy was developed that includes and clearly differentiates between techniques, modalities, domains (clusters of similar modalities), systems of health care (coordinated care system involving multiple modalities), and integrative health care.Domains are the classical primary focus of taxonomies. The ITHC has eleven domains: chemical/substance-based work, device-based work, soft tissue–focused manipulation, skeletal manipulation, fitness/movement instruction, mind–body integration/classical somatics work, mental/emotional–based work, bio-energy work based on physical manipulation, bio-energy modulation, spiritual-based work, unique assessments. Modalities are assigned to the domains based on the primary mode of interaction with the client, according the literature of the practitioners.Conclusions:The ITHC has several strengths: little interpretation is used while successfully assigning modalities to single domains; the issue of taxonomic verticality is fully resolved; and the design fully integrates the complementary health care fields of biomedicine and CAM.
The Department of Defense (DOD) has recognized the importance of improving asset management and has created Item Unique Identification numbers (IUIDs) to improve the situation. IUIDs will be used to track financial and contract records and obtain location and status information about parts in DoD inventory. IUIDs will also support data collection for weapon systems from build, test, operations, maintenance, repair, and overhaul histories. In addition to improving the overall logistics process, lUIDs offer an opportunity to utilize asset-specific data to improve system maintenance and support. An Office of the Secretary of Defense (OSD) Pilot Project to implement IUID on a Navy weapon system presents an immediate opportunity to evaluate this use of IUID data. This paper reports on experiments conducted to see if a set of asset-specific diagnostic classifiers trained on subsets of data is more accurate than a general, composite classifier trained on all of the data. In general, it is determined that the set is more accurate than the single classifier given enough data. However, other factors play an important role such as system complexity and noise levels in the data. Additionally, the improvements found do not arise until larger amounts of data are available. This suggests that future work should concentrate on tying the process of data collection to the estimation of the associated probabilities.
Problems in accuracy and effectiveness in system diagnosis and prognosis arise from constructing models from design data that do not match implementation, failing to account for inherent uncertainty in test data, and failing to account for characteristics unique to specific units due to variations in usage, environment, or other factors. Large sums of money have been expended by owners of these systems, but little improvement in measures such as retest-OK rate and cannot duplicate rate has been reported. In fact, simply losing track of where specific units are located has resulted in substantial losses of money. In this paper, we study the problem of performing diagnosis and prognosis on systems and describe an approach to building models based on data collected about specific units. We rely on the emerging Department of Defense (DoD) Unique Identification (UID) program that is focusing on obtaining this data and apply Bayesian methods for constructing such diagnostic models. Specifically, we discuss an alternative class of Bayesian model that we call the "not-so-naive" Bayesian network (NBN). We also discuss the concept of the NBN in the context of the UID program as a means of tracking and deriving probabilities for creating the network. Finally, we focus on the specific problems encountered and lessons learned from working with a large, real-world database for the US Navy's STANDARD Missile.
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