Literature DB >> 12957431

Clinical prediction models: are we building better mousetraps?

Lawrence Liao, Daniel B Mark.   

Abstract

Mesh：

Year:  2003        PMID： 12957431     DOI： 10.1016/s0735-1097(03)00836-2

Source DB:  PubMed          Journal:  J Am Coll Cardiol        ISSN： 0735-1097            Impact factor:   24.094


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  19 in total

1.  Development of two artificial neural network models to support the diagnosis of pulmonary tuberculosis in hospitalized patients in Rio de Janeiro, Brazil.

Authors:  Fábio S Aguiar; Rodrigo C Torres; João V F Pinto; Afrânio L Kritski; José M Seixas; Fernanda C Q Mello
Journal:  Med Biol Eng Comput       Date:  2016-03-25       Impact factor: 2.602

2.  Comparing clinical judgment with the MySurgeryRisk algorithm for preoperative risk assessment: A pilot usability study.

Authors:  Meghan Brennan; Sahil Puri; Tezcan Ozrazgat-Baslanti; Zheng Feng; Matthew Ruppert; Haleh Hashemighouchani; Petar Momcilovic; Xiaolin Li; Daisy Zhe Wang; Azra Bihorac
Journal:  Surgery       Date:  2019-02-18       Impact factor: 3.982

3.  Predicting virologic failure in an HIV clinic.

Authors:  Gregory K Robbins; Kristin L Johnson; Yuchiao Chang; Katherine E Jackson; Paul E Sax; James B Meigs; Kenneth A Freedberg
Journal:  Clin Infect Dis       Date:  2010-03-01       Impact factor: 9.079

4.  Can ACS-NSQIP score be used to predict postoperative mortality in Saudi population?

Authors:  Anwar U Huda; Mohammad Yasir; Nasrullah Sheikh; Asad Z Khan
Journal:  Saudi J Anaesth       Date:  2022-03-17

5.  Developing a machine learning model to identify delirium risk in geriatric internal medicine inpatients.

Authors:  Qinzheng Li; Yanli Zhao; Yu Chen; Jirong Yue; Yan Xiong
Journal:  Eur Geriatr Med       Date:  2021-09-23       Impact factor: 1.710

6.  ABCD2, ABCD2-I, and OTTAWA scores for stroke risk assessment: a direct retrospective comparison.

Authors:  Francesco Franceschi; Roberto De Giorgio; Michele Domenico Spampinato; Marcello Covino; Angelina Passaro; Matteo Guarino; Beatrice Marziani; Caterina Ghirardi; Adelina Ricciardelli; Irma Sofia Fabbri; Andrea Strada; Antonio Gasbarrini
Journal:  Intern Emerg Med       Date:  2022-08-20       Impact factor: 5.472

7.  A coronary heart disease risk score based on patient-reported information.

Authors:  Arch G Mainous; Richelle J Koopman; Vanessa A Diaz; Charles J Everett; Peter W F Wilson; Barbara C Tilley
Journal:  Am J Cardiol       Date:  2007-03-13       Impact factor: 2.778

8.  Comparison of screening scores for diabetes and prediabetes.

Authors:  Eduard Poltavskiy; Dae Jung Kim; Heejung Bang
Journal:  Diabetes Res Clin Pract       Date:  2016-06-18       Impact factor: 5.602

9.  Development and validation of the Surgical Outcome Risk Tool (SORT).

Authors:  K L Protopapa; J C Simpson; N C E Smith; S R Moonesinghe
Journal:  Br J Surg       Date:  2014-12       Impact factor: 6.939

10.  Placing clinical variables on a common linear scale of empirically based risk as a step towards construction of a general patient acuity score from the electronic health record: a modelling study.

Authors:  Steven I Rothman; Michael J Rothman; Alan B Solinger
Journal:  BMJ Open       Date:  2013-05-14       Impact factor: 2.692
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