دانلود مقاله ISI انگلیسی شماره 9453
عنوان فارسی مقاله

درجه بندی مدل شبیه سازی از بیماری ها با استفاده از رویکرد مهندسی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
9453 2009 9 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Calibration of Disease Simulation Model Using an Engineering Approach
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Value in Health, Volume 12, Issue 4, June 2009, Pages 521–529

کلمات کلیدی
الگوریتم ها - کالیبراسیون - سرطان ریه - مدل شبیه سازی
پیش نمایش مقاله
پیش نمایش مقاله درجه بندی مدل شبیه سازی از بیماری ها با استفاده از رویکرد مهندسی

چکیده انگلیسی

Calibrating a disease simulation model's outputs to existing clinical data is vital to generate confidence in the model's predictive ability. Calibration involves two challenges: 1) defining a total goodness-of-fit (GOF) score for multiple targets if simultaneous fitting is required, and 2) searching for the optimal parameter set that minimizes the total GOF score (i.e., yields the best fit). To address these two prominent challenges, we have applied an engineering approach to calibrate a microsimulation model, the Lung Cancer Policy Model (LCPM). Methods First, 11 targets derived from clinical and epidemiologic data were combined into a total GOF score by a weighted-sum approach, accounting for the user-defined relative importance of the calibration targets. Second, two automated parameter search algorithms, simulated annealing (SA) and genetic algorithm (GA), were independently applied to a simultaneous search of 28 natural history parameters to minimize the total GOF score. Algorithm performance metrics were defined for speed and model fit. Results Both search algorithms obtained total GOF scores below 95 within 1000 search iterations. Our results show that SA outperformed GA in locating a lower GOF. After calibrating our LCPM, the predicted natural history of lung cancer was consistent with other mathematical models of lung cancer development. Conclusion An engineering-based calibration method was able to simultaneously fit LCPM output to multiple calibration targets, with the benefits of fast computational speed and reduced the need for human input and its potential bias.

مقدمه انگلیسی

Calibrating a disease simulation model's outputs to existing clinical data is vital to generate confidence in the model's predictive ability. Calibration involves two challenges: 1) defining a total goodness-of-fit (GOF) score for multiple targets if simultaneous fitting is required, and 2) searching for the optimal parameter set that minimizes the total GOF score (i.e., yields the best fit). To address these two prominent challenges, we have applied an engineering approach to calibrate a microsimulation model, the Lung Cancer Policy Model (LCPM).First, 11 targets derived from clinical and epidemiologic data were combined into a total GOF score by a weighted-sum approach, accounting for the user-defined relative importance of the calibration targets. Second, two automated parameter search algorithms, simulated annealing (SA) and genetic algorithm (GA), were independently applied to a simultaneous search of 28 natural history parameters to minimize the total GOF score. Algorithm performance metrics were defined for speed and model fit.

نتیجه گیری انگلیسی

An engineering-based calibration method was able to simultaneously fit LCPM output to multiple calibration targets, with the benefits of fast computational speed and reduced the need for human input and its potential bias.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.