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from Clinical Trials Data Extracted from a Database pending to Jacob Barhak. ... Barhak has a patent US Patent 9,858,390 -Reference model for disease progression issued to Jacob Barhak, and a patent US patent Utility application #15466535 -Analysis and Verification of Models Derived ...doi:10.7759/cureus.9455 pmid:32760637 pmcid:PMC7392354 fatcat:qbufkwhhhbfidpopy4s7bj6hmi
Computers allow describing the progress of a disease using computerized models. These models allow aggregating expert and clinical information to allow researchers and decision makers to forecast disease progression. To make this forecast reliable, good models and therefore good modeling tools are required. This paper will describe a new computer tool designed for chronic disease modeling. The modeling capabilities of this tool were used to model the Michigan model for diabetes. The modelingdoi:10.1016/j.jbi.2010.06.003 pmid:20558320 pmcid:PMC2934868 fatcat:hwvltlktt5cv7aw6bx7s4yzurm
more »... roach and its advantages such as simplicity, availability, and transparency are discussed.
Precision inspection of free-form surface is difficult with current industry practices that rely on accurate fixtures. Alternatively, the measurements can be aligned to the part model using a geometry-based registration method, such as the iterative closest point (ICP) method, to achieve a fast and automatic inspection process. This paper discusses various techniques that accelerate the registration process and improve the efficiency of the ICP method. First, the data structures of approximateddoi:10.1007/s00170-005-0370-9 fatcat:go3vo5zgkvacnmpojwmagqh4qq
more »... nearest nodes and topological neighbor facets are combined to speed up the closest point calculation. The closest point calculation is further improved with the cached facets across iteration steps. The registration efficiency can also be enhanced by incorporating signal-to-noise ratio into the transformation of correspondence sets to reduce or remove the noise of outliers. Last, an acceleration method based on linear or quadratic extrapolation is fine-tuned to provide the fast yet robust iteration process. These techniques have been implemented on a four-axis blade inspection machine where no accurate fixture is required. The tests of measurement simulations and inspection case studies indicated that the presented registration method is accurate and efficient.
With the increasing burden of chronic diseases on the health care system, Markov-type models are becoming popular to predict the long-term outcomes of early intervention and to guide disease management. However, statisticians have not been actively involved in the development of these models. Typically, the models are developed by using secondary data analysis to find a single "best" study to estimate each transition in the model. However, due to the nature of secondary data analysis, theredoi:10.1016/j.inffus.2010.08.003 pmid:22563307 pmcid:PMC3341173 fatcat:rl2uyht77jg5nigs4chlo75skq
more »... uently are discrepancies between the theoretical model and the design of the studies being used. This paper illustrates a likelihood approach to correctly model the design of clinical studies under the conditions where 1) the theoretical model may include an instantaneous state of distinct interest to the researchers, and 2) the study design may be such that study data can not be used to estimate a single parameter in the theoretical model of interest. For example, a study may ignore intermediary stages of disease. Using our approach, not only can we accommodate the two conditions above, but more than one study may be used to estimate model parameters. In the spirit of "If life gives you lemon, make lemonade", we call this method "Lemonade Method". Simulation studies are carried out to evaluate the finite sample property of this method. In addition, the method is demonstrated through application to a model of heart disease in diabetes.
BARHAK AND W. YE ( 0 ) 0 01 (a, Z, t = 1) = p 01 f |Gender = 0 p 01m |Gender = 1 T (0) = 1 i.e. Consider the simple case where the study period is 1 year. ... BARHAK AND W. YE ( 3 ) 3 = [0.0112 1.0590 0.5250 0.3900 1.3500 1.0880 1.078] R (3) = diag(2.0408 * 10 −6 , 3.1497 * 10 −5 , 32 1− p 32 − p 34 p Copyright q 2009 John Wiley & Sons, Ltd. Statist. ...doi:10.1002/sim.3855 fatcat:dzpaz6nsqffwhlvghakz2rkjf4
BARHAK AND W. YE ( 0 ) 0 01 (a, Z, t = 1) = p 01 f |Gender = 0 p 01m |Gender = 1 T (0) = 1 i.e. Consider the simple case where the study period is 1 year. ... BARHAK AND W. YE ( 3 ) 3 = [0.0112 1.0590 0.5250 0.3900 1.3500 1.0880 1.078] R (3) = diag(2.0408 * 10 −6 , 3.1497 * 10 −5 , 32 1− p 32 − p 34 p Copyright q 2009 John Wiley & Sons, Ltd. Statist. ...doi:10.1002/sim.3599 pmid:19455575 pmcid:PMC4621762 fatcat:et2ontx2hrbzzlmrcxcoa6mrxu
An example of a less strict approach to model credibility are new ensemble techniques, such as in (Barhak, 2016; Barhak, 2017) , which allow judging a model by its performance in a group of models. ... Even open source licenses are quite restricted since they are based on copyright laws, which give the owner rights to restrict usage (Barhak, 2020b) . ...doi:10.3389/fsysb.2022.822606 fatcat:7orhjcczd5bljhghmzgxb6rfh4
Proceedings of the 11th Python in Science Conference
The Reference Model for disease progression is based on a modeling framework written in Python. It is a prototype that demonstrates the use of computing power to aid in chronic disease forecast. The model uses references to publicly available data as a source of information, hence the name for the model. The Reference Model also holds this name since it is designed to be used by other models as a reference. The model uses parallel processing and computing power to create a competition amongdoi:10.25080/majora-54c7f2c8-007 fatcat:ygkl4odhrbbdzc3i2btooim4rm
more »... thesis of disease progression. The system runs on a multi core machine and is scalable to a SLURM cluster.
The authors would like to thank Jacob Barhak and Lee Markosian for useful comments. ...doi:10.2312/sgp/sgp05/217-226 fatcat:7p35qtrs25aqllzrhdxjwmrdeq
topic/ public-scientific-reviews/zF5cxJir8Fs), and by Jacob Barhak (https://groups.google.com/forum/#!topic/ public-scientific-reviews/p5svew7S9q8). ...doi:10.13016/m2qz22m7v fatcat:3uutgl4uzfh4jgi7lgr7siit4y
TEL: 301-344-8648 TLX: CON: SMITE BARHAK W DR LOS ALAMOS NATIONAL LAB KS D-436 LOS ALANCS NN 87545 U.S.A. ... AUSTRIA TEL: 222-34-53-605 TLX: 133099 VIAST A CON: 25,27V,30 BREGNAN JACOB D IX NETHERLANDS FOUNDATION XADIOASTRONONY POSTBUS 2 NL-7990 AA DWINGELOO NETHERLANDS TEL: 05219-7244 TLX: 42043 ...doi:10.1017/s0251107x0002647x fatcat:bs2ubhbh5zdkxj4ctqdlhf62ge
Jacob Wagner at ERDC-CERL for conversations and input on this paper. The authors would also like to thank Dr. ... Neural networks have also been proposed, and demonstrated as superior to PDE-based methods in (Barhak & Fisher 2001) . ...fatcat:kl6hjslmrnf2ji7ckxqzqcafh4