Mobile health systems in recent times, have notably improved the healthcare sector by empowering patients to actively participate in their health, and by facilitating access to healthcare professionals. Effective operation of these mobile systems nonetheless, requires high level of intelligence and expertise implemented in the form of decision support systems (DSS). However, common challenges in the implementation include generalization and reliability, due to the dynamics and incompleteness of information presented to the inference models. In this paper, we advance the use of ad hoc mobile decision support system to monitor and detect triggers and early symptoms of respiratory distress provoked by strenuous physical exertion. The focus is on the application of certainty theory to model inexact reasoning by the mobile monitoring system. The aim is to develop a mobile tool to assist patients in managing their conditions, and to provide objective clinical data to aid physicians in the screening, diagnosis, and treatment of the respiratory ailments. We present the proposed model architecture and then describe an application scenario in a clinical setting. We also show implementation of an aspect of the system that enables patients in the self-management of their conditions.

Clinical characterization and interpretation of respiratory sound symptoms have remained a challenge due to the similarities in the audio properties that manifest during auscultation in medical diagnosis. The misinterpretation and conflation of these sounds coupled with the comorbidity cases of the associated ailments - particularly, exercised-induced respiratory conditions; result in the under-diagnosis and undertreatment of the conditions. Though several studies have proposed computerized systems for objective classification and evaluation of these sounds, most of the algorithms run on desktop and backend systems. In this study, we leverage the improved computational and storage capabilities of modern smartphones to distinguish the respiratory sound symptoms using machine learning algorithms namely: Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbour (k-NN). The appreciable performance of these classifiers on a mobile phone shows smartphone as an alternate tool for recognition and discrimination of respiratory symptoms in real-time scenarios. Further, the objective clinical data provided by the machine learning process could aid physicians in the screening and treatment of a patient during ambulatory care where specialized medical devices may not be readily available.

Cough audio signal classification has been successfully used to diagnose a variety of respiratory conditions, and there has been significant interest in leveraging Machine Learning (ML) to provide widespread COVID-19 screening. The COUGHVID dataset provides over 25,000 crowdsourced cough recordings representing a wide range of participant ages, genders, geographic locations, and COVID-19 statuses. First, we contribute our open-sourced cough detection algorithm to the research community to assist in data robustness assessment. Second, four experienced physicians labeled more than 2,800 recordings to diagnose medical abnormalities present in the coughs, thereby contributing one of the largest expert-labeled cough datasets in existence that can be used for a plethora of cough audio classification tasks. Finally, we ensured that coughs labeled as symptomatic and COVID-19 originate from countries with high infection rates. As a result, the COUGHVID dataset contributes a wealth of cough recordings for training ML models to address the world’s most urgent health crises. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14377019

When Joe Brew worked for the Florida Department of Health as an epidemiologist for two years starting in 2013, he helped with syndromic surveillance, meaning he had the arduous job of reviewing the symptoms of patients coming into the emergency departments from all across the state. The goal of such work: to detect an abnormal spike of symptoms in an area that may indicate there's a public health concern. Public health authorities worldwide continue to use this type of surveillance. The outbreak of a novel pathogen in Wuhan, China in late 2019, for instance, was in part detected by a large uptick of patients coming to the hospital with symptoms of a respiratory infection, with unknown etiology. But Brew says this system fails to prevent the transmission of a virus like SARS-CoV-2 because by the time patients arrive at the hospital, they have likely already been infectious for a matter of days.

In paediatrics, respiratory disorders represent the second most common reason for attendance at Emergency Departments (ED) [1, 2] and are a significant global disease burden [3]. Common conditions in childhood include croup, upper respiratory tract infections (URTI), and lower respiratory tract diseases (LRTDs) such as asthma/reactive airway disease (RAD), bronchiolitis, pneumonitis and pneumonia [2, 4]. Lower respiratory tract infections are a significant cause of mortality in children aged under 5 years and a leading cause of disability-adjusted life years lost worldwide [5–7]. Asthma represents the leading cause of non-fatal disease burden in Australian children under age 14 years [8, 9]. The differential diagnosis of respiratory disorders can be challenging even for experienced clinicians with access to diagnostic support services.

Brisbane, Australia-based ResApp is planning to re-do its big US trial soon, but in the meantime the smartphone respiratory diagnosis company is continuing to collect data in its native country. The company released data yesterday from a clinical study of more than 1,300 adult patients at Joondalup Health Campus in Perth and Wesley Hospital in Brisbane. While the company's previous studies have focused on a particular condition, this is the first real-world study of patients with a wide variety of diagnoses. Patients presented with a range of respiratory conditions, including some with no condition at all. "Delivering accurate results within an adult intended use population is an excellent step forward, further demonstrating that ResApp's algorithms can be applied effectively in a group of patients with a very broad range of respiratory illnesses," Tony Keating, CEO and managing director of ResApp Health, said in a statement.

Backed by sleek devices and powered by intelligent software and apps, smartphones are shaping the future of healthcare. The latest development in medical apps is the use of artificial intelligence and machine learning to analyze data and offer a diagnosis within seconds. ResApp Health, a digital healthcare solutions company based in Australia, is developing an app that can diagnose respiratory conditions with a smartphone's microphone, which acts as a stethoscope, according to MobiHealthNews. The ResAppDx app applies specially developed machine learning algorithms to the sounds, including cough sounds, which automatically identify potential respiratory conditions, including pneumonia, asthma, bronchiolitis and chronic obstructive pulmonary disease (COPD). Another company utilizing artificial intelligence advances is Beyond Verbal, which has launched a research platform that's attempting to identify biomarkers in users' voices to detect a range of health issues, including heart problems, ALS and even Parkinson's disease.