scispace - formally typeset
J

J. Wanger

Researcher at University of Rochester

Publications -  40
Citations -  32042

J. Wanger is an academic researcher from University of Rochester. The author has contributed to research in topics: Lung volumes & Spirometry. The author has an hindex of 25, co-authored 40 publications receiving 27948 citations. Previous affiliations of J. Wanger include University of Genoa.

Papers
More filters
Journal ArticleDOI

Standardisation of spirometry

TL;DR: This research presents a novel and scalable approach called “Standardation of LUNG FUNCTION TESTing” that combines “situational awareness” and “machine learning” to solve the challenge of integrating nanofiltration into the energy system.
Journal ArticleDOI

Interpretative strategies for lung function tests

TL;DR: This section is written to provide guidance in interpreting pulmonary function tests (PFTs) to medical directors of hospital-based laboratories that perform PFTs, and physicians who are responsible for interpreting the results of PFTS most commonly ordered for clinical purposes.
Journal ArticleDOI

Standardisation of the measurement of lung volumes

TL;DR: This research presents a novel and scalable approach called “Standardation of LUNG FUNCTION TESTing” that combines “situational awareness” and “machine learning” to solve the challenge of integrating nanofiltration into the energy system.
Journal ArticleDOI

Guidelines for methacholine and exercise challenge testing-1999. This official statement of the American Thoracic Society was adopted by the ATS Board of Directors, July 1999.

TL;DR: This study presents a meta-analysis of the Methacholine Challenge Pretest Questionnaire results to assess the response of the participants and recommend further studies to investigate its application in clinical practice.
Journal ArticleDOI

Standardisation of the single-breath determination of carbon monoxide uptake in the lung

TL;DR: This research presents a novel and scalable approach called “Standardation of LUNG FUNCTION TESTing” that combines “situational awareness” and “machine learning” to solve the challenge of integrating nanofiltration into the energy system.