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  • 1
    ISSN: 1432-0584
    Keywords: Minimally differentiated acute myeloid leukemia ; Chemotherapy
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Notes: Summary With the objective of establishing the optimal therapy for minimally differentiated acute myeloid leukemia (AML-M0), we examined the therapeutic results of five AML-M0 cases and reviewed the literature. In a series of 63 patients with newly diagnosed acute leukemia who were admitted to the Main Hospital of Nippon Medical School, five patients fit the criteria for AML-M0: negative myeloperoxidase (MPO) and Sudan black B reaction by light microscopy, negative for B- and T-lineage markers, and positive for myeloid markers. They were treated by means of AdVP [adriamycin, vincristine, and prednisolone (PSL)] therapy and/or BHAC-DMP [behenoylcytosine arabinoside (BHAC), daunorubicin (DNR), 6-mercaptopurine (6-MP), and PSL] therapy. The AdVP therapy was unsuccessful in the two patients who received it, while a complete remission (CR) was achieved with the BHAC-DMP therapy in three of four patients. Although one patient treated with BHAC-DMP did not achieve CR, his blasts were apparently sensitive to the therapy. In assessable cases in the literature where leukemic blasts were MPO-negative, myeloid marker-positive and B- and T-lineage marker-negative, CR was achieved in 54.5% and 44.4% with anti-acute myeloid leukemia therapy and anti-acute lymphocytic leukemia therapy, respectively. Five cases in the literature were treated with a chemotherapeutic regimen containing BHAC [or cytosine arabinoside (Ara-C)], DNR, and 6-MP, and all achieved CR. The regimen containing BHAC (or Ara-C), DNR, and 6-MP may be useful as induction chemotherapy for AML-MO.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    User modeling and user adapted interaction 8 (1998), S. 5-47 
    ISSN: 1573-1391
    Keywords: Plan recognition ; Bayesian Belief Networks ; language learning ; abstraction ; performance evaluation.
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We present an approach to keyhole plan recognition which uses a dynamic belief (Bayesian) network to represent features of the domain that are needed to identify users' plans and goals. The application domain is a Multi-User Dungeon adventure game with thousands of possible actions and locations. We propose several network structures which represent the relations in the domain to varying extents, and compare their predictive power for predicting a user's current goal, next action and next location. The conditional probability distributions for each network are learned during a training phase, which dynamically builds these probabilities from observations of user behaviour. This approach allows the use of incomplete, sparse and noisy data during both training and testing. We then apply simple abstraction and learning techniques in order to speed up the performance of the most promising dynamic belief networks without a significant change in the accuracy of goal predictions. Our experimental results in the application domain show a high degree of predictive accuracy. This indicates that dynamic belief networks in general show promise for predicting a variety of behaviours in domains which have similar features to those of our domain, while reduced models, obtained by means of learning and abstraction, show promise for efficient goal prediction in such domains.
    Type of Medium: Electronic Resource
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