Novel pediatric-automated respiratory score using physiologic data and machine learning in asthma

Respiratory Scores as a Tool to Reduce Bronchodilator Use in Children Hospitalized With Acute Viral Bronchiolitis

Hosp Pediatr.

Mussman GM, Sahay RD, Destino L, Lossius M, Shadman KA, Walley SC

doi: 10.1542/hpeds.2016-0090.

RECOMMENDED

Further Publications

Novel machine learning can predict acute asthma exacerbation

★ 2 Recommended by Dr Martin Frey, Faculty Member

Chest

Zein JG, Wu CP, Attaway AH, Zhang P, Nazha A

doi: 10.1016/j.chest.2020.12.051

COMMENTED

Lung Cancer

Machine Learning for Early Lung Cancer Identification Using Routine Clinical and Laboratory Data

★ 3 Recommended and commented by Dr Martin Frey, Faculty Member

Am J Respir Crit Care Med

Gould MK, Huang BZ, Tammemagi MC, Kinar Y, Shiff R

doi: 10.1164/rccm.202007-2791OC

Improving efficiency of pediatric emergency asthma treatment by using metered dose inhaler

J Asthma.

Abaya R, Delgado EM, Scarfone RJ, Reardon AM, Rodio B, Simpkins D, Mehta V, Hayes K, Zorc JJ

doi: 10.1080/

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Sleep Apnea

Reliability of Machine Learning to Diagnose Pediatric Obstructive Sleep Apnea: Systematic Review and Meta-Analysis

★ 2 Recommended by Dr Martin Frey, Faculty Member

Pediatr Pulmonol

Gutiérrez-Tobal GC, Álvarez D, Kheirandish-Gozal L, Del Campo F, Gozal D, Hornero R

doi: 10.1002/ppul.25423

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ARDS

Machine Learning Classifier Models Can Identify ARDS Phenotypes Using Readily Available Clinical Data

★ 2 Recommended by Dr Martin Frey, Faculty Member

Am J Respir Crit Care Med.

Sinha P, Churpek MM, Calfee CS

doi: 10.1164/rccm.202002-0347OC.

Asthma

The pediatric asthma yardstick: practical recommendations for a sustained step-up in asthma therapy for children with inadequately controlled asthma

Ann Allergy Asthma Immunol.

Chipps BE, Bacharier LB, Farrar JR, Jackson DJ, Murphy KR, Phipatanakul W, Szefler SJ, Teague WG, Zeiger RS

doi: 10.1016/j.anai.2018.04.002.

RECOMMENDED

Asthma

Frequency of Tiotropium Bromide Use and Clinical Features of Patients with Severe Asthma in a Real-Life Setting: Data from the Severe Asthma Network in Italy (SANI) Registry

★ 1 Recommended by Dr Martin Frey, Faculty Member

★ 1 Recommended by Dr Marcus Hesse, Faculty Member

J Asthma Allergy

Puggioni F, Brussino L, Canonica GW, Blasi F, Paggiaro P, Caminati M, Latorre M, Heffler E, Senna G, Severe Asthma Network in Italy (SANI) group.

doi: 10.2147/JAA.S274245

RECOMMENDED

Further Publications

Identification of potentially undiagnosed patients with nontuberculous mycobacterial lung disease using machine learning applied to primary care data in the UK

★ 2 Recommended by Dr Martin Frey, Faculty Member

★ 2 Recommended by Dr Marcus Hesse, Faculty Member

Eur Respir J.

Doyle OM, van der Laan R, Obradovic M, McMahon P, Daniels F, Pitcher A, Loebinger MR

doi: 10.1183/13993003.00045-2020.

Related content

Respiratory Scores as a Tool to Reduce Bronchodilator Use in Children Hospitalized With Acute Viral Bronchiolitis

Novel machine learning can predict acute asthma exacerbation

Machine Learning for Early Lung Cancer Identification Using Routine Clinical and Laboratory Data

Improving efficiency of pediatric emergency asthma treatment by using metered dose inhaler

Reliability of Machine Learning to Diagnose Pediatric Obstructive Sleep Apnea: Systematic Review and Meta-Analysis

Machine Learning Classifier Models Can Identify ARDS Phenotypes Using Readily Available Clinical Data

The pediatric asthma yardstick: practical recommendations for a sustained step-up in asthma therapy for children with inadequately controlled asthma

Frequency of Tiotropium Bromide Use and Clinical Features of Patients with Severe Asthma in a Real-Life Setting: Data from the Severe Asthma Network in Italy (SANI) Registry

Identification of potentially undiagnosed patients with nontuberculous mycobacterial lung disease using machine learning applied to primary care data in the UK