ELIZA cgi-bash version rev. 1.91
- Medical English LInking keywords finder for the PubMed Zipped Archive (ELIZA) -

return kwic search for rate out of >500 occurrences
286534 occurrences (No.87 in the rank) during 5 years in the PubMed. [cache]
30) Prediction of the rate of COVID-19 spread and modeling of its course have critical impact on both health system and policy makers.
--- ABSTRACT ---
PMID:34660935 DOI:10.1016/j.heliyon.2021.e08143
2021 Heliyon
* Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review.
- COVID-19 has produced a global pandemic affecting all over of the world. Prediction of the rate of COVID-19 spread and modeling of its course have critical impact on both health system and policy makers. Indeed, policy making depends on judgments formed by the prediction models to propose new strategies and to measure the efficiency of the imposed policies. Based on the nonlinear and complex nature of this disorder and difficulties in estimation of virus transmission features using traditional epidemic models, artificial intelligence methods have been applied for prediction of its spread. Based on the importance of machine and deep learning approaches in the estimation of COVID-19 spreading trend, in the present study, we review studies which used these strategies to predict the number of new cases of COVID-19. Adaptive neuro-fuzzy inference system, long short-term memory, recurrent neural network and multilayer perceptron are among the mostly used strategies in this regard. We compared the performance of several machine learning methods in prediction of COVID-19 spread. Root means squared error (RMSE), mean absolute error (MAE), R2 coefficient of determination (R2), and mean absolute percentage error (MAPE) parameters were selected as performance measures for comparison of the accuracy of models. R2 values have ranged from 0.64 to 1 for artificial neural network (ANN) and Bidirectional long short-term memory (LSTM), respectively. Adaptive neuro-fuzzy inference system (ANFIS), Autoregressive Integrated Moving Average (ARIMA) and Multilayer perceptron (MLP) have also have R2 values near 1. ARIMA and LSTM had the highest MAPE values. Collectively, these models are capable of identification of learning parameters that affect dissimilarities in COVID-19 spread across various regions or populations, combining numerous intervention methods and implementing what-if scenarios by integrating data from diseases having analogous trends with COVID-19. Therefore, application of these methods would help in precise policy making to design the most appropriate interventions and avoid non-efficient restrictions.
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(1)172 of (10)5 that (19)3 step (28)2 increased
(2)40 and (11)5 the (20)2 (ESR), (29)2 it
(3)32 was (12)4 (HR), (21)2 (RMR) (30)2 lactate,
(4)31 *null* (13)4 among (22)2 after (31)2 or
(5)24 in (14)4 discrimination (23)2 at (32)2 respiratory
(6)8 variability (15)4 is (24)2 attributable (33)2 to
(7)6 constants (16)4 were (25)2 between (34)2 top
(8)6 for (17)3 enzyme (26)2 constant (35)2 which
(9)5 (HR) (18)3 on (27)2 equations

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--- WordNet output for rate --- =>価格を評定する, 割合, 率, 歩合, 料金, 値段, 相場, 速度, 度合, 人を〜を思う, みなす, 価値がある, 人を〜とみなす, 科金, 評価する Overview of noun rate The noun rate has 4 senses (first 3 from tagged texts) 1. (68) rate -- (a magnitude or frequency relative to a time unit; "they traveled at a rate of 55 miles per hour"; "the rate of change was faster than expected") 2. (39) rate, charge per unit -- (amount of a charge or payment relative to some basis; "a 10-minute phone call at that rate would cost $5") 3. (1) pace, rate -- (the relative speed of progress or change; "he lived at a fast pace"; "he works at a great rate"; "the pace of events accelerated") 4. rate -- (a quantity or amount or measure considered as a proportion of another quantity or amount or measure; "the literacy rate"; "the retention rate"; "the dropout rate") Overview of verb rate The verb rate has 3 senses (first 3 from tagged texts) 1. (9) rate, rank, range, order, grade, place -- (assign a rank or rating to; "how would you rank these students?"; "The restaurant is rated highly in the food guide") 2. (2) rate -- (be worthy of or have a certain rating; "This bond rates highly") 3. (1) rate, value -- (estimate the value of; "How would you rate his chances to become President?"; "Gold was rated highly among the Romans") --- WordNet end ---