ライフサイエンスコーパス: decision support

1 and targets of 4 risk factors with tailored decision support.

2 udies and database development, and clinical decision support.

3 tion from thousands of patients for accurate decision support.

4 rch and practice for knowledge discovery and decision support.

5 al data analysis, knowledge engineering, and decision support.

6 r understanding of these beliefs may improve decision support.

7 ent may enable more customized and effective decision support.

8 sources for diagnosis and might benefit from decision support.

9 re, from post-operative debrief to real-time decision support.

10 w promise as important elements of physician decision support.

11 tware prototype that can facilitate clinical decision support.

12 ance improvement, resource use, and clinical decision support.

13 could be more relevant to individual patient decision support.

14 onic medical records augmented with clinical decision support.

15 te views; 112 users (38%) completed clinical decision support.

16 el fMRI and ERP results during memory-guided decisions support a key role of the PFC in top-down bias

17 as been implemented to allow straightforward decision support across a variety of decision-making con

18 erogeneity in the effect of the differential decision support across subgroups, which has implication

19 For Giemsa stained sections, the decision support algorithm achieved a sensitivity of 100

20 ith a tolerable specificity of 66.2% for the decision support algorithm compared to 92.6 (microscopic

21 We evaluated the effect of a computer-based decision support algorithm in delivering patient-specifi

22 Together, we provide the first evidence of a decision support algorithm proving as a sensitive screen

23 We designed a deep-learning-based decision support algorithm that can be applied on regula

24 With the help of our decision support algorithm, we show an increased sensiti

25 owing volume and complexity of imaging data, decision-support algorithms will be required to help phy

26 in interoperability, process automation, and decision support all promise to alter both the structure

27 can serve as a flexible framework for future decision support and can be a step toward more sustainab

28 ercent of the 236 studies evaluated clinical decision support and computerized provider order entry,

29 Strong evidence supports the use of clinical decision support and computerized provider order entry.

30 search is needed to facilitate point-of-care decision support and examine BREASTChoice's impact on pa

31 ssimilate the tools of ASPs into EMRs, using decision support and feedback.

32 orms have been developed, many with clinical decision support and informatics interoperability, but f

33 serve as a resource to evaluate new clinical decision support and monitoring algorithms.

34 ting clinics with the same EHR software with decision support and patient registry functionalities an

35 d findings could be used to improve clinical decision support and personalize trajectories, thereby d

36 Use of such risk scores may facilitate decision support and population health management effort

37 indicators/measures; (5) developing clinical decision support and quality improvement tools; and (6)

38 ut rtPA in hyperacute stroke were relational decision support and situationally-sensitive knowledge t

39 Policy decisions, support and interventions will need to be tar

40 critical illness, enable real-time clinical decision support, and improve both clinical outcomes and

41 ovel data presentation formats, computerized decision support, and smart alarms are used to enhance e

42 (3) computerized guideline application with decision support (APP).

43 en the basis for the development of clinical decision support applications that promise to integrate

44 Ns and propose a Lyapunov optimization based decision support approach - the Reputation-aware Task Su

45 It is an individual-level decision support approach which results in the emergence

46 Cognitive aids and other methods of decision support are receiving increased interest by the

47 s-which are the basis of day-to-day clinical decision support-are often used to guide the clinical ma

48 n requesting and conducting imaging studies; decision support at point of care; education of referrin

49 ovement intervention comprising computerized decision support, audit/feedback tools, and staff traini

50 variety of applications, including clinical decision support, automated workflow triage, clinical pr

51 Pending legislation specifies clinical decision support before performing advanced imaging at e

52 By advocating rational decisions, supported by accurate information, scientists

53 Computerized protocols used to deliver decision support can be configured to contain much more

54 In future, software for decision support can be developed based on the DW and it

55 Universal decision support can better standardize a decisional app

56 h the electronic medical record and clinical decision support capabilities has provided solutions to

57 cess in evolution to integrate ASP tools and decision support capacities directly into Epic's EMR.

58 Computerized clinical decision support (CCDS) significantly reduced Clostridio

59 cessary imaging by mandating use of clinical decision support (CDS) by all providers who order advanc

60 cal decision making, computer-based clinical decision support (CDS) could unlock widespread benefits

61 rovider overrides of evidence-based clinical decision support (CDS) for ordering computed tomographic

62 Clinical decision support (CDS) systems could be used to accompli

63 pport provided radiologists with interactive decision support (clicking on a breast region yields a l

64 Self-triage using web-based decision support could be a useful way to encourage appr

65 Decision support during medication administration provid

66 ve, 4-center, randomized controlled trial of decision support effectiveness.

67 support from nonphysician care coordinators, decision support electronic health records facilitating

68 omprising nonphysician care coordinators and decision-support electronic health records.

69 er development, such a tool can provide fast decision support for designing surgical bioprosthetic ao

70 increase, it will become critical to provide decision support for genomic medicine.

71 NSC dataset for enhancing future oncological decision support for Head and Neck Squamous Cell Carcino

72 The results can be used as decision support for local farmers and authorities.

73 aking surrounding DT LVAD should incorporate decision support for patients and caregivers.

74 ent provided by trained nurses, and clinical decision support for PCPs by consulting physicians.

75 nge on environmental services and to provide decision support for policy.

76 f an institutional protocol and computerized decision support for red cell transfusion in the critica

77 The further development of clinical decision support for renal anemia requires the character

78 he results were bridged to humans to provide decision support for setting breakpoints for voriconazol

79 ps understand SOC dynamics but also provides decision support for sustainable biofuel development.

80 iscovery has great potential application for decision-support for basic research and clinical problem

81 A decision support framework for the management of lagoon

82 ibes the development and test of a potential decision support framework that combines elements from s

83 tion with existing systems and sophisticated decision support functionality are important decisive fa

84 A computerized, interactive decision-support guide and access to prenatal testing wi

85 guidelines using a computerized, interactive decision-support guide in the absence of financial barri

86 dardized approaches for introducing clinical decision support has been followed, describing the data

87 ance improvement, resource use, and clinical decision support has been less well studied.

88 Historically, probabilistic models for decision support have focused on discrimination, e.g., m

89 formation systems, delivery system redesign, decision support, health care organization, and communit

90 Decision support improved processes of care but not depr

91 h may be used to tailor therapies or provide decision support in a clinical setting where the choice

92 Decision support in acute care settings may require grea

93 gnostic evaluations, and to provide clinical decision support in cases of diagnostic uncertainty or c

94 Probability-based decision support in LCA is a way to help stakeholders in

95 n of functional genomics experiments and for decision support in precision medicine.

96 The possible role of decision support in preventing the emergence of resistan

97 al to revolutionize clinical prognostics and decision support in the care of the critically ill but a

98 ding an unprecedented opportunity to improve decision-support in cancer treatment at low cost.

99 rance range which is clinically relevant for decision-support in our holoendemic setting.

100 Computerized clinical decision support integrated with the electronic health r

101 When used as decision support, interactive use of CAD for malignant m

102 presented for formal classification using a decision-support interface based on published guidelines

103 ugh the use of an IH risk calculator app and decision-support interface.

104 1.65; 95% CI, 1.26 to 2.16), educational or decision support intervention (provided v not, OR = 0.21

105 n was implemented in a randomized trial of a decision support intervention to manage depression among

106 were randomized to the computerized clinical decision support intervention, aimed at physicians, and

107 will be used in further research to develop decision support interventions to assist with the assess

108 The early work in this field of decision support is encouraging but there are many quest

109 Background Clinical decision support is increasingly used to enhance clinici

110 spect is especially important when automated decision support is used for patient care over substanti

111 re crafted in part using the Guidelines Into Decision Support methodology.

112 his paper, we develop a regional water reuse decision-support model (RWRM) using the general algebrai

113 LED and ORBIT scores and should be useful as decision support on anticoagulation treatment in patient

114 omized to CDS, pediatric clinicians received decision support on obesity management, and patients and

115 ion and were randomly assigned to depression decision support or usual care.

116 an education, peer comparisons, and computer decision support order sets was directed against all ant

117 ian education, peer comparisons and computer decision support order sets was directed against all ant

118 formation systems, delivery system redesign, decision support, organizational support, and community

119 ology, patient-centered and -driven clinical decision support, patient engagement, culture change, cl

120 tic conservation planning (SCP) is a spatial decision support process used to identify the most cost-

121 We have developed a computerized decision-support program linked to computer-based patien

122 ics, aided by electrocardiograph (ECG)-based decision support, randomized 911 (871 enrolled) patients

123 urce for research in intraoperative clinical decision support, risk prediction, or outcomes studies.

124 The fraction of low-yield (decision support score, 1-3) examinations requested thro

125 implemented: Examinations that had low-yield decision support scores could not be scheduled when the

126 equests for examinations with higher initial decision support scores that were not affected by the po

127 Results were correlated with user status and decision support scores.

128 ented with the Veterans Heath Administration Decision Support Services hemoglobin A1c (HbA(c)) and se

129 d differential diagnosis, patient and family decision support, shared decision making and triage, tre

130 tive algorithm used to target a subgroup for decision support, should occur before randomization (so

131 knowledge base, and development of clinical decision support software are needed in addition to auto

132 ss the impact of smart pumps with integrated decision support software on the incidence and nature of

133 telephone and was supported by computerized decision support software.

134 ive effect of computer-generated prompts and decision support software.

135 Real-time decision-support software has been developed to detect a

136 s reproducible sampling methods and examines decision support strategies that lead to quality decisio

137 ure was higher with CDSS alerts than with no decision support system (adjusted odds ratio 3.18, 95% C

138 y an automated artificial intelligence-based decision support system (AI-DSS) is as effective and saf

139 ted the effectiveness of a computer clinical decision support system (CDSS) for reducing the risk of

140 A clinical decision support system (CDSS) is a computer program tha

141 A clinical decision support system (CDSS) is a computer program, wh

142 Use of a clinical decision support system (CDSS) might improve outcomes.

143 Could a clinical decision support system (CDSS), interposed at the time o

144 ducted to determine the effect of a tailored decision support system (DSS) that provides individualiz

145 The Veterans Health Administration Decision Support System and VA Surgical Quality Improvem

146 loped an open-source, generalizable clinical decision support system called Electronic Support for Pu

147 rce, electronic health record-based clinical decision support system can increase AE detection and re

148 A decision support system evaluated the effects of various

149 This research proposes an intelligent decision support system for acute lymphoblastic leukaemi

150 protocols and enabled analyses, we apply the Decision Support System for Agrotechnology Transfer (DSS

151 ture CO2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop

152 yze the efficacy of a computerized open-loop decision support system for burn resuscitation compared

153 Implementation of a computer decision support system for burn resuscitation in the in

154 he Veterans Affairs Medical SAS Datasets and Decision Support System for entire cohort and separately

155 on-invasive, non-contact and tele-diagnostic decision support system for its reliable detection and p

156 Real-time computerized decision support system for prescribing drugs in patient

157 his platform demonstrate EMMA's promise as a decision support system for therapeutic management of mu

158 sequent patients with severe burns (computer decision support system group) and compared with the Mod

159 nary output goals was higher in the computer decision support system group.

160 rface area) were also lower for the computer decision support system group.

161 e unit volume were all lower in the computer decision support system group.

162 This clinical decision support system identified and utilized holistic

163 which clinicians must interact with clinical decision support system may either exceed or fall short

164 Quality Improvement Program database and the Decision Support System pharmacy database were linked to

165 mapping, Geographic Information System, and Decision Support System technologies, and progress in sp

166 The addition of computer decision support system technology improved patient care

167 Use of a computerized clinical decision support system to automate the identification a

168 Using a computerized clinical decision support system to automate the screening of chi

169 e potential role for a computerized clinical decision support system to enable non-specialists rapidl

170 pic medication guidelines and a computerized decision support system to facilitate shared decision ma

171 and effectiveness of a computerized clinical decision support system to identify pediatric patients a

172 igned to be integrated within a computerized decision support system to improve patient flow manageme

173 The computer decision support system was used to resuscitate 32 subse

174 involving a PSP and its associated web-based Decision Support System.

175 adding a DSS module to an existing computer decision support system.

176 levels were regulated by a computer-assisted decision support system.

177 omputerized open-loop algorithm and computer decision support system.

178 adding a T2D module to an existing computer decision support system.

179 ted in guideline adherence by a computerized decision-support system.

180 rehensive PBM programs and to use electronic decision support systems (both conditional recommendatio

181 Computerized clinical decision support systems (CCDSSs) have been implemented

182 Clinical decision support systems (CDSS) can scan the patient's e

183 ed provider order entry (CPOE) with clinical decision support systems (CDSS) in transfusion medicine

184 Antibiotic computerised decision support systems (CDSSs) are shown to improve an

185 lectronic health records (EHRs) and clinical decision support systems (CDSSs) have the potential to e

186 Clinical decision support systems (CDSSs) might be beneficial for

187 the Chernobyl Nuclear Power Plant accident, decision support systems (DSS) for supporting response o

188 Computer-based diagnostic decision support systems (DSSs) were developed to improv

189 n of smarter, more actionable monitoring and decision support systems and aligned financial incentive

190 Computerized decision support systems are becoming increasingly preva

191 n demonstrates a way in which computer-based decision support systems can improve care.

192 scalable data-driven algorithmic management decision support systems for crowdsourcing.

193 The development of decision support systems for pathology and their deploym

194 undation for the deployment of computational decision support systems in clinical practice.

195 Demand for clinical decision support systems in medicine and self-diagnostic

196 plication of Convex-NMF in computer assisted decision support systems is expected to facilitate furth

197 Patients' educational needs and decision support systems need a lot more research.

198 Most studies addressed decision support systems or electronic health records.

199 Meaningful decision support systems rely on accurate data analysis

200 Depending on the particular clinical decision support systems selected within a health system

201 ts of interventions--Therapy, Harm, Clinical Decision Support Systems, and Summarizing the Evidence g

202 From early-stage drug discovery to clinical decision support systems, we have seen examples of how A

203 uce or prevent alert fatigue within clinical decision support systems.

204 uce or prevent alert fatigue within clinical decision support systems.

205 nance play in modern medical information and decision support systems; it also discusses the implicat

206 port improved patient outcomes from clinical decision-support systems (CDSSs) are lacking in HIV care

207 increasing emphasis on the role of clinical decision-support systems (CDSSs) for improving care and

208 Decision-support systems (DSSs) are interactive computer

209 The depression decision support team, which consisted of a psychiatrist

210 ata must be interpreted by multidisciplinary decision-support teams to determine mutation actionabili

211 soning is the basis of an explicit method of decision support that allows the rigorous evaluation of

212 we propose a model of "universal" electronic decision support that can be easily adapted by clinician

213 Here we develop an approach to decision support that leverages artificial intelligence

214 sive visual analytics tool for breast cancer decision support that provides a holistic assessment of

215 ce, precisely when they felt they had made a decision, supports the idea that conscious awareness occ

216 nd the subsequent analysis of these data for decision support; this practice is termed radiomics.

217 lementation of an institutional protocol and decision support through a computerized provider order e

218 and MRI) that would require use of clinical decision support to achieve Protecting Access to Medicar

219 on on e-prescribing; and (3) use of clinical decision support to assist with clinical decision making

220 f LDKT and may benefit from patient-centered decision support to facilitate a risk-benefit assessment

221 This pilot study suggests that web-based decision support to help parents and adult caregivers se

222 id diet screener tools with coupled clinical decision support to identify actionable modifications fo

223 inicians provide effective and compassionate decision support to patients with advanced dementia and

224 Mental health care for women includes decision support to prepare for major life events, inclu

225 decision analysis (MCDA) move beyond ad hoc decision support to quantitatively and holistically rank

226 er of processed food products and to provide decision support to the stakeholders.

227 ler, with the aim of providing environmental decision-support to the retailer and establishing life c

228 oad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of publi

229 of a multimodality-appropriate use criteria decision support tool (AUC-DST) on rates of appropriate

230 emented and studied the impact of a clinical decision support tool (CDST) to decrease the number of u

231 ous intravenous insulin using a computerized decision support tool (target blood glucose concentratio

232 This study examined the impact of a video decision support tool and patient checklist on advance c

233 and evaluation in the context of a clinical decision support tool are warranted.

234 thians where brown bears occur, we develop a decision support tool based on models that assess the la

235 with 1 previous cesarean delivery, use of a decision support tool compared with usual care did not s

236 A decision support tool could help increase trial-of-labor

237 CONCLUSIONS/SIGNIFICANCE: The diagnostic decision support tool covered the majority of diagnoses

238 , difficulties, and gaps in genomic clinical decision support tool development.

239 The ACET Program decision support tool facilitated standardized asthma as

240 utbreak dynamics and outbreak control into a decision support tool for mitigating infectious disease

241 In conclusion, use of an automated decision support tool for optimizing insulin pump settin

242 ificial intelligence as a promising clinical decision support tool for supine chest radiograph examin

243 tment recommended by the Atrial Fibrillation Decision Support Tool in 931 patients.

244 e ABC score should be considered an improved decision support tool in the care of patients with atria

245 This study examined the effect of a video decision support tool on CPR preferences among patients

246 ipants were randomized to use a tablet-based decision support tool prior to 25 weeks' gestation (n=74

247 A computerized decision support tool scored asthma control and assigned

248 Use of a decision support tool that integrates patient-specific s

249 We then created a decision support tool that recommends performing no bloo

250 tation and could be a useful educational and decision support tool to assist physicians in the diagno

251 n the electronic health record as a clinical decision support tool to enforce protocol compliance wit

252 protocol was designed using the EVA tool, a decision support tool to help in the development of an e

253 evaluated the predictive validity of a brief decision support tool to screen veterans for problems wi

254 : We evaluated whether a low-cost diagnostic decision support tool would lead to changes in clinical

255 Use of the decision support tool would result in 38% fewer blood cu

256 ht to evaluate performance of a computerized decision support tool, the Asthma Control Evaluation and

257 Such an ultra-early, ECG-based clinical decision support tool, when combined with the judgment o

258 tential transplant recipient with a valuable decision support tool.

259 s adjusted every 3 months by using an online decision support tool.

260 that recommended by the Atrial Fibrillation Decision Support Tool.

261 atment recommended by an Atrial Fibrillation Decision Support Tool.

262 onjugate pneumococcal vaccine as an accurate decision support tool.

263 sibly providing a laboratory-based objective decision-support tool for determination of an APE diagno

264 elp in the evaluation of each case we used a decision-support tool that was previously developed for

265 gical risk calculator has been proposed as a decision-support tool to predict complication risk after

266 r-writing applications, and a computer-based decision-support tool, were available both in the ICU an

267 licts have highlighted the need for clinical decision support tools (CDST) to decrease time to wound

268 orting clinician order entry and of clinical decision support tools (CDSTs) has provided expanded opp

269 th OHCA highlight the importance of clinical decision support tools and treatment algorithms in the c

270 In addition, a new generation of decision support tools are needed to further the practic

271 Decision support tools are required to assess the relati

272 serve as the engine for a new generation of decision support tools for individuals with diabetes.

273 ldren with BMI >/= 85th percentile, clinical decision support tools for pediatric weight management,

274 ting practices of BCID results with clinical decision support tools providing interpretation guidance

275 Decision support tools such as life cycle assessment (LC

276 e quantitative output of these algorithms as decision support tools to aid with image interpretation.

277 the strengthening of primary care practice, decision support tools to avoid inappropriate services,

278 Use of clinical decision support tools to enable substitution of the mos

279 es for tPA administration, provided clinical decision support tools, facilitated hospital participati

280 Interventions included clinical decision support tools, structured improvement strategie

281 For computerized methods to be useful as decision support tools, they need to be resilient to dat

282 ons supports use of cognitive aids and other decision support tools.

283 certainty and without the help of structured decision support tools.

284 Decision-support tools are commonly used to maximize ret

285 The use of decision-support tools is a response to both the informa

286 The assessment of land use impacts in decision-support tools such as life cycle assessment (LC

287 hese data may inform clinical guidelines and decision-support tools to improve empiric antibiotic pre

288 Better data, decision-support tools, and incentives will be needed to

289 To provide decision support toward a green process design, a resear

290 Solutions include decision-support trees based on molecular best practices

291 ts of EHR-based research: improving clinical decisions, supporting triage decisions, enabling collabo

292 In 5 practices randomized to CDS + coaching, decision support was augmented by individualized family

293 consecutive orders for examinations in which decision support was provided that were placed from Apri

294 d hence to inform development of appropriate decision support we interviewed patients, their family a

295 ocumentation tools, order sets, and clinical decision support were designed to improve efficiency, op

296 ordering prophylaxis vs routine care without decision support were included.

297 ystems (eg, increase patient activation with decision support) whereas other levers must be tailored

298 der different treatments enables therapeutic decision support, which is available in exploratory form

299 Decision support will guide adherence to achievement and

300 clinical information systems, and integrated decision support, with accompanying changes in delivery