ライフサイエンスコーパス: prediction rule

1 r studies are needed to establish a clinical prediction rule.

2 dentified in the validation cohort using the prediction rule.

3 ulation depends on a patient's status on the prediction rule.

4 at FT-based new feature can help sharpen the prediction rules.

5 rtional hazards models and standard clinical prediction rules.

6 machine learning approach unravels promising prediction rules.

7 Further studies are needed to confirm the prediction rules.

8 development, validation, and use of clinical prediction rules.

9 sses (1) the development and use of clinical prediction rules, (2) the European Respiratory Society T

10 Our objective was to develop a practical prediction rule able to identify patients with GNB infec

11 Currently available prediction rules aiming to identify preschool children h

12 Clinical prediction rules and D-dimer measurement allow stratific

13 ms of VTE as well as the utility of clinical prediction rules and D-dimer testing in the diagnosis of

14 diction rules; important differences between prediction rules and decision rules; how to assess the p

15 ional data can now be combined with clinical prediction rules and genomic data to enable expert antim

16 Clinical prediction rules and models are developed by applying st

17 n the development and validation of clinical prediction rules and models.

18 e accuracy of the minimum norm interpolating prediction rule approaches the best possible accuracy fo

19 In radiology, clinical prediction rules are an important method for determining

20 Prediction rules are currently created more frequently,

21 Three validated clinical prediction rules are described for adult and pediatric p

22 Clinical prediction rules are multifactorial tools used to aid in

23 Clinical prediction rules are used, but accuracy varies with stud

24 eding or ischemic events 1 year after PCI, a prediction rule assessing late ischemic and bleeding ris

25 The prediction rule assigned 1 point each for myocardial inf

26 The prediction rule assigns points based on age and the pres

27 We developed and validated a clinical prediction rule based on a set of electrocardiographic c

28 y can be estimated using a simple 4-variable prediction rule based on age, sex, smoking, and diabetes

29 to assess the potential clinical impact of a prediction rule before translating it into a decision ru

30 Our prediction rule can be used to estimate prN2/3 in patien

31 The spinal manipulation clinical prediction rule can be used to improve decision making f

32 Our prediction rule can be used to plan surveillance of new

33 nose strep throat, a well-validated clinical prediction rule can be useful and can help physicians ma

34 ow likelihood of significant stenoses, these prediction rules can help to substantially reduce health

35 Our findings indicate that use of prediction rules could help identify children at risk of

36 We aimed to develop a clinical prediction rule (CPR) to identify children likely to exp

37 Clinical prediction rules (CPRs) are commonly used tools to guide

38 ents were examined according to the clinical prediction rule criteria (symptom duration, symptom loca

39 The prediction rule demonstrated less utility in the validat

40 The prediction rule described here accurately identifies pat

41 on of wheeze at preschool age, (3) published prediction rules developed to identify preschool childre

42 The clinical prediction rule discriminated between patients with and

43 Clinical prediction rules do not incorporate real-time incidence

44 sed pathways, true pathways produced simpler prediction rules, emphasizing a smaller number of pathwa

45 A clinical prediction rule, followed by PCR screening, could be use

46 We derived a prediction rule for 1-year ischemic stroke risk post-TIA

47 onstruct a parsimonious model and a clinical prediction rule for 10-year all-cause mortality.

48 Results were compared with a previous prediction rule for all adults.

49 A prediction rule for asthma in preschool children might h

50 designed to devise and validate a practical prediction rule for atrial fibrillation/atrial flutter (

51 The prediction rule for children aged 2 years and older (nor

52 In the validation population, the prediction rule for children younger than 2 years (norma

53 for mild head injury can now be guided by a prediction rule for clinically important traumatic brain

54 A prediction rule for CO 3GC-R infection was validated in

55 A prediction rule for community-onset 3GC-R infection was

56 e present prospective study was to develop a prediction rule for delirium in a cardiac surgery cohort

57 The BRUE prediction rule for discerning serious diagnoses display

58 escribe the development of a simple clinical prediction rule for estimating the risk of NFI occurrenc

59 prediction models and to develop a clinical prediction rule for identifying moderate-to-severe fibro

60 A prediction rule for P(LA) from DT(D) was developed in 50

61 A clinical prediction rule for patients with a score greater than 0

62 New risk factors and a prediction rule for postthoracotomy atrial fibrillation

63 ospectively derived and validated a clinical prediction rule for recurrent CDI that is simple, reliab

64 This study aimed to develop a prediction rule for recurrent CDI using the above deriva

65 erebral Hemorrhage score is a valid clinical prediction rule for short-term mortality in intracerebra

66 ed predictive ability of the CHA(2)DS(2)VASc prediction rule for stroke and death in a nonanticoagula

67 To date, no clinical prediction rule for TB risk exists for use as a guide du

68 A recent study introduces a validated prediction rule for use in mild CHI, to limit the number

69 s study was to develop and validate clinical prediction rules for bacteremia and subtypes of bacterem

70 We derived and validated age-specific prediction rules for ciTBI (death from traumatic brain i

71 We externally validate 2 previously derived prediction rules for community-onset (CO) and hospital-o

72 e externally validate two previously derived prediction rules for community-onset (CO) and hospital-o

73 g data mining, and decision trees to produce prediction rules for functional class.

74 Most clinical prediction rules for percutaneous coronary intervention

75 Reported prediction rules for postoperative AF have suffered from

76 oo enthusiastic acceptance of it to evaluate prediction rules for primary prevention of cardiovascula

77 Clinical prediction rules for severe CAP do not appear adequately

78 he most recent literature regarding clinical prediction rules for the use of cranial computed tomogra

79 , an important next step would be to develop prediction rules for use in clinical practice, so that o

80 New BRUE prediction rules from a US cohort of 3283 infants showed

81 The prediction rule had a sensitivity of 94.2% (95% CI, 85.6

82 ems for which the minimum norm interpolating prediction rule has near-optimal prediction accuracy.

83 The introduction of several prediction rules has helped to guide clinicians in the u

84 Low-risk clinical prediction rules have been developed but need to be furt

85 Several clinical prediction rules have been developed to aid the clinicia

86 c hepatitis B (CHB), but previously proposed prediction rules have shown limited external validity.

87 These validated prediction rules identified children at very low risk of

88 Delirium prediction rules identify patients at risk for delirium

89 ds of evidence for developing and evaluating prediction rules; important differences between predicti

90 udy validates the Bacterial Meningitis Score prediction rule in the era of conjugate pneumococcal vac

91 Prospective validation of a clinical prediction rule in this population is warranted.

92 A simple prediction rule incorporating determinants of 30-day mor

93 A clinical prediction rule is being developed.

94 The kTSP prediction rule is the aggregation of voting among such

95 This prediction rule may help physicians make more rational d

96 The findings of this study suggest that this prediction rule may help prognosticate upper limb functi

97 Clinical prediction rules may allow for select testing, but limit

98 Prediction rules may assist clinicians when weighing the

99 mechanism of injury, suggests that low-risk prediction rules may be safely utilized by prehospital p

100 Clinical prediction rules, most notably the PECARN rules, can be

101 s-box physics rule learner (GPRL) leading to prediction rules of future bubble array.

102 multicenter cohort study show that the BRUE prediction rules outperformed the AAP higher-risk criter

103 The prediction rule potentially reduced IAT to 62% (60/97) w

104 tween 2012 and 2014, we developed a clinical prediction rule predicting the probability of MRSA trans

105 The derived prediction rule ranged from -2 to +2 with higher scores

106 Conjoint PRIM prediction rules recover approximately twice as many dif

107 We divided clinical prediction rule scoring into 4 tiers.

108 To insure generalizability, clinical prediction rules should also be validated in subjects di

109 To be useful, clinical prediction rules should be clinically important, have fa

110 found that the previously published suicide prediction rule significantly predicted post-deployment

111 Clinical prediction rules, sometimes called clinical decision rul

112 The AUC of the prediction rule stood at 0.67 (95% CI, 0.62-0.72; calibr

113 The clinical prediction rule stratified patients into mortality risk

114 The prediction rule stratified patients into risk groups wit

115 We present a simple echocardiographic prediction rule that accurately defines PH hemodynamics,

116 eatment decisions may be aided by a clinical prediction rule that identifies residents at low and hig

117 and c) to develop and internally validate a prediction rule that may be used in the emergency depart

118 h community-acquired pneumonia, we derived a prediction rule that stratifies patients into five class

119 h cancer-associated VTE to derive a clinical prediction rule that stratifies VTE recurrence risk.

120 ignificant predictors of AF and to develop a prediction rule that was evaluated through jackknifing.

121 ing the different representations DMP learnt prediction rules that were more accurate than default at

122 of a decline from baseline was compared to a prediction-rule that uses HBsAg levels of <1,500 IU/mL a

123 essed the predictive characteristics of four prediction rules (the original and revised American Thor

124 We previously developed a clinical prediction rule, the Bacterial Meningitis Score, that cl

125 We derived a simple echocardiographic prediction rule to allow hemodynamic differentiation of

126 We developed a robust prediction rule to assist clinicians in identifying pati

127 risk, we developed and validated a clinical prediction rule to determine the risk of violent offendi

128 ) and piperacillin-tazobactam and a clinical prediction rule to guide anti-vancomycin-resistant Enter

129 characteristics were used to choose the best prediction rule to identify patients with Q fever pneumo

130 evalence of Q fever pneumonia and to build a prediction rule to identify patients with Q fever pneumo

131 old, and to develop and validate a clinical prediction rule to predict the risk of lymph node metast

132 ests exceeds capacity, the use of a clinical prediction rule to prioritize diagnostic testing can hav

133 1: Clinicians should use validated clinical prediction rules to estimate pretest probability in pati

134 d tomography (CT) imaging risks in children, prediction rules to guide decisions on CT scan use, and

135 (2) Create a clinical prediction rule using geriatric markers from preoperativ

136 In a population-based cohort, the score on a prediction rule using out-of-hospital factors was signif

137 In unsupervised analysis, a prediction rule was built from the expression profiles o

138 A prediction rule was created; patients were categorized i

139 from 11 countries (August 2009-May 2014), a prediction rule was derived stratifying patients into gr

140 The clinical prediction rule was developed by multivariate logistic r

141 A clinical prediction rule was developed on the training set, and v

142 A clinical prediction rule was developed using penalized logistic r

143 A clinical prediction rule was generated.

144 The prediction rule was used to stratify patients into group

145 Using the prediction rule we defined three risk categories for AF:

146 The prediction rule we describe accurately identifies the pa

147 k of deep vein thrombosis (DVT) by the Wells prediction rule were performed, and levels of fibrin deg

148 The following prediction rules were developed: The absence of severe c

149 Prediction rules were formulated by using multiple logis

150 0.68 and 0.74, respectively) but none of the prediction rules were particularly good.

151 0.75, respectively), but again, none of the prediction rules were particularly good.

152 gths and weaknesses of the multiple proposed prediction rules, when to measure D-dimer, and which cut

153 viously derived and validated STONE clinical prediction rule, which includes five elements: sex, timi

154 ysis, clinicians cannot know whether using a prediction rule will be beneficial or harmful.

155 arch Network (PECARN) derived 2 age-specific prediction rules with 6 variables for clinically importa

156 ls built on the true pathway mappings led to prediction rules with fewer influential pathways than th

157 gorithms can be used to obtain interpretable prediction rules with high prediction accuracies and to