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Mashup Score: 0Development and Validation of Explainable Machine Learning Models for Risk of Mortality in TAVI - TRIM Scores - 1 year(s) ago
AbstractBackground. Identification of high-risk patients and individualized decision support based on objective criteria for rapid discharge after transcatheter
Source: OUP AcademicCategories: Cardiology News and Journals, Latest HeadlinesTweet
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Mashup Score: 3External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients - PubMed - 2 year(s) ago
This external validation cohort study suggests that the ESM has poor discrimination and calibration in predicting the onset of sepsis. The widespread adoption of the ESM despite its poor performance raises fundamental concerns about sepsis management on a national level.
Source: PubMedCategories: Cardiologists, Latest HeadlinesTweet-
RT @drjessilyn: The #epic failure (excuse the pun) of #sepsis prediction modeling for #decisionSupport https://t.co/ecwisMwRB4 shows us "ty…
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Mashup Score: 09 Top Applications of Artificial Intelligence in Business - 3 year(s) ago
Enterprise use of AI is increasing and expanding into new areas. Read about nine top applications of AI in business and the benefits they bring companies.
Source: SearchEnterpriseAICategories: Cardiologists, Latest HeadlinesTweet-
#artificialintelligence in #healthcare: #research analyzing vast #data collected to uncover patterns and insights. #algorithms in diagnostic tools for clinicians #decisionsupport. #personalisedmedicine developing more individualized treatment plans. https://t.co/piEgNkrnfw
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Mashup Score: 2Association of Clinician Diagnostic Performance With Machine Learning–Based Decision Support Systems - 3 year(s) ago
This systematic review examines the association between use of machine learning–based clinical decision support systems and clinician performance.
Source: jamanetwork.comCategories: Cardiologists, Latest HeadlinesTweet-
Association of Clinician Diagnostic Performance With Machine Learning–Based Decision Support Systems https://t.co/yymM2FHLEK via @JAMANetworkOpen #digitalhealth #machinelearning #decisionsupport
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Mashup Score: 0AI-Enabled Clinical Decision Support Software: A “Trust and Value Checklist” for Clinicians - 3 year(s) ago
As clinical decision support software becomes more sophisticated, clinicians must be able to evaluate how it works, and how well it works, so that they will be comfortable trusting machine learning…
Source: catalyst.nejm.orgCategories: General Medicine Journals and Societies, Latest HeadlinesTweet-
As clinical #DecisionSupport software becomes more sophisticated, clinicians must be able to evaluate how it works, and how well it works: https://t.co/zoinDs3ocU @christinasilcox @SusanDentzer @DBatesSafety
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Mashup Score: 24AI-Enabled Clinical Decision Support Software: A “Trust and Value Checklist” for Clinicians - 4 year(s) ago
As clinical decision support software becomes more sophisticated, clinicians must be able to evaluate how it works, and how well it works, so that they will be comfortable trusting machine learning…
Source: catalyst.nejm.orgCategories: General Medicine Journals and Societies, Latest HeadlinesTweet-
As clinical #DecisionSupport becomes more sophisticated, clinicians need to evaluate how/how well this #MachineLearning & #ArtificialIntelligence works to help care for patients: https://t.co/sY7TPJp2A0 @christinasilcox @SusanDentzer @DukeMargolis @DBatesSafety @BrighamWomens
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Mashup Score: 0Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials - 4 year(s) ago
Objectives To identify factors that differentiate between effective and ineffective computerised clinical decision support systems in terms of improvements in the process of care or in patient outcomes. Design Meta-regression analysis of randomised controlled trials. Data sources A database of features and effects of these support systems derived from 162 randomised controlled trials identified…
Source: The BMJCategories: Cardiologists, Latest HeadlinesTweet-
@gcfmd @JAMACardio @JAMANetwork Interesting! #Decisionsupport has mixed results depending on type. Work led by P Roshanov, R Haynes in our group showed that systems more likely to succeed provided advice to patients + clinicians or req’d clinicians to enter reason for overriding advice https://t.co/qaQY0ZrgTD
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Mashup Score: 0Coming out of stealth mode, new startup Spesana reveals molecular diagnostic decision support tool - 4 year(s) ago
The new tool helps synthesize information from EHRs, payers, clinical trials and reports.
Source: MobiHealthNewsCategories: Healthcare Professionals, Latest HeadlinesTweet-
Coming out of stealth mode, new #startup @SpesanaCo reveals molecular diagnostic #decisionsupport tool #CDS - The new tool helps synthesize information from #EHR, payers, #clinicaltrials & reports https://t.co/wcIDB2bLdE
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Mashup Score: 1
As the population ages, the prevalence of chronic disease increases. As a result, more drugs are prescribed. Around 25% of the population aged 65+ are affected by so-called “polypharmacy,” i.e., they regularly take at least five drugs. This results in an increased risk for drug interactions and adverse drug events. The international, EU-funded PRIMA-e-DS project led by Andreas Sönnichsen, head of…
Source: medicalxpress.comCategories: General Medicine News, Latest HeadlinesTweet-
Electronic #decisionsupport #tool reduces the risk of overmedication @bmj_latest https://t.co/JjRacksfxp
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Development and validation of explainable #MachineLearning models for risk of mortality in #TAVI - TRIM Scores https://t.co/FlwwribYPK #EHJDigital #DecisionSupport @BruiningNico @rafavidalperez @GerdHindricks @EACVIPresident https://t.co/PYyNxwH51C