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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Abstract:
Programs for the early detection of cancer in the past have been beset with problems of low yield, inability to recruit high risk populations, poor utilization of physicians, and inadequate end results evaluation. Nevertheless, for twelve forms of cancer representing 80% of all cancers and 70% of all cancer mortality there is a useful form of early detection or prevention. Furthermore, the ultimate benefit of early diagnosis to patients is supported by long-term relative survival rate studies, and for certain forms of cancer by reduction in mortality rates as well. Efforts are now underway to develop risk factor detection programs which will overcome some of the aforementioned problems. One such program is the risk factor analysis project, operating in the CANSCREEN clinics, which was designed to detect high risk indicators for cancer which are amenable to intervention by primary prevention or diagnosis and treatment. A triage system with linkages to comprehensive cancer centers has been established and a medical information system prepared for centralized statistical and epidemiological studies. The clinics are operated entirely by nurse examiners and health educators. Results of pilot studies are encouraging with regard to the feasibility of implementing such programs.
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