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An interpretable machine learning model for the prevention of contrast-induced nephropathy in patients undergoing lower extremity endovascular interventions for peripheral arterial disease.

Autor: Cox M; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: mcox12@mgh.harvard.edu., Panagides JC; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: john_panagides@hms.harvard.edu., Di Capua J; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: jdicapua@partners.org., Dua A; Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: adua1@mgh.harvard.edu., Kalva S; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: skalva@partners.org., Kalpathy-Cramer J; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: jkalpathy-cramer@mgh.harvard.edu., Daye D; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: ddaye@mgh.harvard.edu.

Publikováno v: Clinical imaging [Clin Imaging] 2023 Sep; Vol. 101, pp. 1-7. Date of Electronic Publication: 2023 May 24.

Risk stratification with explainable machine learning for 30-day procedure-related mortality and 30-day unplanned readmission in patients with peripheral arterial disease.

Autor: Cox M; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America., Panagides JC; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America., Tabari A; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America., Kalva S; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America., Kalpathy-Cramer J; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America., Daye D; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America.

Publikováno v: PloS one [PLoS One] 2022 Nov 21; Vol. 17 (11), pp. e0277507. Date of Electronic Publication: 2022 Nov 21 (Print Publication: 2022).

Interpretable Machine Learning for the Prediction of Amputation Risk Following Lower Extremity Infrainguinal Endovascular Interventions for Peripheral Arterial Disease.

Autor: Cox M; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB #290, Boston, MA, 02114, USA., Reid N; Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA., Panagides JC; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB #290, Boston, MA, 02114, USA., Di Capua J; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB #290, Boston, MA, 02114, USA., DeCarlo C; Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA., Dua A; Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA., Kalva S; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB #290, Boston, MA, 02114, USA., Kalpathy-Cramer J; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB #290, Boston, MA, 02114, USA., Daye D; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB #290, Boston, MA, 02114, USA. ddaye@mgh.harvard.edu.

Publikováno v: Cardiovascular and interventional radiology [Cardiovasc Intervent Radiol] 2022 May; Vol. 45 (5), pp. 633-640. Date of Electronic Publication: 2022 Mar 23.

Novel co-culture plate enables growth dynamic-based assessment of contact-independent microbial interactions.

Autor: Moutinho TJ Jr; Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America., Panagides JC; Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America., Biggs MB; Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America., Medlock GL; Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America., Kolling GL; Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America., Papin JA; Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.

Publikováno v: PloS one [PLoS One] 2017 Aug 02; Vol. 12 (8), pp. e0182163. Date of Electronic Publication: 2017 Aug 02 (Print Publication: 2017).