Literature DB >> 26293849

Health Informatics via Machine Learning for the Clinical Management of Patients.

D A Clifton1, K E Niehaus, P Charlton, G W Colopy.   

Abstract

OBJECTIVES: To review how health informatics systems based on machine learning methods have impacted the clinical management of patients, by affecting clinical practice.
METHODS: We reviewed literature from 2010-2015 from databases such as Pubmed, IEEE xplore, and INSPEC, in which methods based on machine learning are likely to be reported. We bring together a broad body of literature, aiming to identify those leading examples of health informatics that have advanced the methodology of machine learning. While individual methods may have further examples that might be added, we have chosen some of the most representative, informative exemplars in each case.
RESULTS: Our survey highlights that, while much research is taking place in this high-profile field, examples of those that affect the clinical management of patients are seldom found. We show that substantial progress is being made in terms of methodology, often by data scientists working in close collaboration with clinical groups.
CONCLUSIONS: Health informatics systems based on machine learning are in their infancy and the translation of such systems into clinical management has yet to be performed at scale.

Entities:  

Keywords:  Health informatics; data mining; electronic health records; information systems

Mesh:

Year:  2015        PMID: 26293849      PMCID: PMC4587065          DOI: 10.15265/IY-2015-014

Source DB:  PubMed          Journal:  Yearb Med Inform        ISSN: 0943-4747


  52 in total

1.  Impact of cardiac telemetry on patient safety and cost.

Authors:  Evan M Benjamin; Robert A Klugman; Roger Luckmann; David G Fairchild; Susan A Abookire
Journal:  Am J Manag Care       Date:  2013-06-01       Impact factor: 2.229

Review 2.  Choosing wisely in adult hospital medicine: five opportunities for improved healthcare value.

Authors:  John Bulger; Wendy Nickel; Jordan Messler; Jenna Goldstein; James O'Callaghan; Moises Auron; Mangla Gulati
Journal:  J Hosp Med       Date:  2013-08-19       Impact factor: 2.960

3.  Demonstrating the accuracy of an in-hospital ambulatory patient monitoring solution in measuring respiratory rate.

Authors:  N Donnelly; T Hunniford; R Harper; A Flynn; A Kennedy; D Branagh; J McLaughlin
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2013

4.  Signal-quality indices for the electrocardiogram and photoplethysmogram: derivation and applications to wireless monitoring.

Authors:  Christina Orphanidou; Timothy Bonnici; Peter Charlton; David Clifton; David Vallance; Lionel Tarassenko
Journal:  IEEE J Biomed Health Inform       Date:  2014-07-23       Impact factor: 5.772

Review 5.  Novel wireless devices for cardiac monitoring.

Authors:  Joseph A Walsh; Eric J Topol; Steven R Steinhubl
Journal:  Circulation       Date:  2014-08-12       Impact factor: 29.690

6.  Integration of early physiological responses predicts later illness severity in preterm infants.

Authors:  Suchi Saria; Anand K Rajani; Jeffrey Gould; Daphne Koller; Anna A Penn
Journal:  Sci Transl Med       Date:  2010-09-08       Impact factor: 17.956

7.  APACHE-acute physiology and chronic health evaluation: a physiologically based classification system.

Authors:  W A Knaus; J E Zimmerman; D P Wagner; E A Draper; D E Lawrence
Journal:  Crit Care Med       Date:  1981-08       Impact factor: 7.598

Review 8.  Predicting cancer prognosis using functional genomics data sets.

Authors:  Jishnu Das; Kaitlyn M Gayvert; Haiyuan Yu
Journal:  Cancer Inform       Date:  2014-11-02

9.  Human disease classification in the postgenomic era: a complex systems approach to human pathobiology.

Authors:  Joseph Loscalzo; Isaac Kohane; Albert-Laszlo Barabasi
Journal:  Mol Syst Biol       Date:  2007-07-10       Impact factor: 11.429

10.  Design and anticipated outcomes of the eMERGE-PGx project: a multicenter pilot for preemptive pharmacogenomics in electronic health record systems.

Authors:  L J Rasmussen-Torvik; S C Stallings; A S Gordon; B Almoguera; M A Basford; S J Bielinski; A Brautbar; M H Brilliant; D S Carrell; J J Connolly; D R Crosslin; K F Doheny; C J Gallego; O Gottesman; D S Kim; K A Leppig; R Li; S Lin; S Manzi; A R Mejia; J A Pacheco; V Pan; J Pathak; C L Perry; J F Peterson; C A Prows; J Ralston; L V Rasmussen; M D Ritchie; S Sadhasivam; S A Scott; M Smith; A Vega; A A Vinks; S Volpi; W A Wolf; E Bottinger; R L Chisholm; C G Chute; J L Haines; J B Harley; B Keating; I A Holm; I J Kullo; G P Jarvik; E B Larson; T Manolio; C A McCarty; D A Nickerson; S E Scherer; M S Williams; D M Roden; J C Denny
Journal:  Clin Pharmacol Ther       Date:  2014-06-24       Impact factor: 6.875

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  7 in total

1.  2021 ISHNE/HRS/EHRA/APHRS Expert Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia-Pacific Heart Rhythm Society.

Authors:  Niraj Varma; Iwona Cygankiewicz; Mintu P Turakhia; Hein Heidbuchel; Yu-Feng Hu; Lin Yee Chen; Jean-Philippe Couderc; Edmond M Cronin; Jerry D Estep; Lars Grieten; Deirdre A Lane; Reena Mehra; Alex Page; Rod Passman; Jonathan P Piccini; Ewa Piotrowicz; Ryszard Piotrowicz; Pyotr G Platonov; Antonio Luiz Ribeiro; Robert E Rich; Andrea M Russo; David Slotwiner; Jonathan S Steinberg; Emma Svennberg
Journal:  Circ Arrhythm Electrophysiol       Date:  2021-02-12

2.  2021 ISHNE/HRS/EHRA/APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society.

Authors:  Niraj Varma; Iwona Cygankiewicz; Mintu P Turakhia; Hein Heidbuchel; Yufeng Hu; Lin Yee Chen; Jean-Philippe Couderc; Edmond M Cronin; Jerry D Estep; Lars Grieten; Deirdre A Lane; Reena Mehra; Alex Page; Rod Passman; Jonathan P Piccini; Ewa Piotrowicz; Ryszard Piotrowicz; Pyotr G Platonov; Antonio Luiz Ribeiro; Robert E Rich; Andrea M Russo; David Slotwiner; Jonathan S Steinberg; Emma Svennberg
Journal:  Cardiovasc Digit Health J       Date:  2021-01-29

3.  Influence of metabolic profiles on the safety of drug therapy in routine care in Germany: protocol of the cohort study EMPAR.

Authors:  Tatjana Huebner; Michael Steffens; Roland Linder; Jochen Fracowiak; Daria Langner; Marco Garling; Felix Falkenberg; Christoph Roethlein; Willy Gomm; Britta Haenisch; Julia Stingl
Journal:  BMJ Open       Date:  2020-04-27       Impact factor: 2.692

4.  Clinician checklist for assessing suitability of machine learning applications in healthcare.

Authors:  Ian Scott; Stacey Carter; Enrico Coiera
Journal:  BMJ Health Care Inform       Date:  2021-02

Review 5.  Machine learning in patient flow: a review.

Authors:  Rasheed El-Bouri; Thomas Taylor; Alexey Youssef; Tingting Zhu; David A Clifton
Journal:  Prog Biomed Eng (Bristol)       Date:  2021-02-22

6.  Artificial intelligence and nuclear medicine.

Authors:  Margaret Hall
Journal:  Nucl Med Commun       Date:  2019-01       Impact factor: 1.690

7.  Automated prediction of mastitis infection patterns in dairy herds using machine learning.

Authors:  Robert M Hyde; Peter M Down; Andrew J Bradley; James E Breen; Chris Hudson; Katharine A Leach; Martin J Green
Journal:  Sci Rep       Date:  2020-03-09       Impact factor: 4.379

  7 in total

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