In the healthcare industry, researchers have been developing machine learning models to automate diagnosing patients with respiratory illnesses based on their breathing patterns. However, these models do not consider the demographic biases, particularly sex bias, that often occur when models are trained with a skewed patient dataset. Hence, it is essential in such an important industry to reduce this bias so that models can make fair diagnoses. In this work, we examine the bias in models used to detect breathing patterns of two major respiratory diseases, i.e., chronic obstructive pulmonary disease (COPD) and COVID-19. Using decision tree models trained with audio recordings of breathing patterns obtained from two open-source datasets consisting of 29 COPD and 680 COVID-19-positive patients, we analyze the effect of sex bias on the models. With a threshold optimizer and two constraints (demographic parity and equalized odds) to mitigate the bias, we witness 81.43% (demographic parity difference) and 71.81% (equalized odds difference) improvements. These findings are statistically significant.

Objective: The study aimed to determine the prevalence of several non-communicable diseases (NCD) and analyze risk factors among adult patients seeking nutritional guidance in Dhaka, Bangladesh. Result: Our study observed the relationships between gender, age groups, obesity, and NCDs (DM, CKD, IBS, CVD, CRD, thyroid). The most frequently reported NCD was cardiovascular issues (CVD), which was present in 83.56% of all participants. CVD was more common in male participants. Consequently, male participants had a higher blood pressure distribution than females. Diabetes mellitus (DM), on the other hand, did not have a gender-based inclination. Both CVD and DM had an age-based progression. Our study showed that chronic respiratory illness was more frequent in middle-aged participants than in younger or elderly individuals. Based on the data, every one in five hospitalized patients was obese. We analyzed the co-morbidities and found that 31.5% of the population has only one NCD, 30.1% has two NCDs, and 38.3% has more than two NCDs. Besides, 86.25% of all diabetic patients had cardiovascular issues. All thyroid patients in our study had CVD. Using a t-test, we found a relationship between CKD and thyroid (p-value 0.061). Males under 35 years have a statistically significant relationship between thyroid and chronic respiratory diseases (p-value 0.018). We also found an association between DM and CKD among patients over 65 (p-value 0.038). Moreover, there has been a statistically significant relationship between CKD and Thyroid (P < 0.05) for those below 35 and 35-65. We used a two-way ANOVA test to find the statistically significant interaction of heart issues and chronic respiratory illness, in combination with diabetes. The combination of DM and RTI also affected CKD in male patients over 65 years old.

How many times do you cough a day? Do you cough more when you're indoors or outside? Or more often after you eat? Chances are, your cough memory might not be that accurate. But all of that information about your coughing patterns could be an untapped resource to better understand your health. Coughs may be benign ways to clear a little extra phlegm, or they could be early signs of more serious conditions such as asthma, GERD (gastroesophageal reflux disease), or even lung cancer.

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. Science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis.

Researchers from the University of Massachusetts Amherst have created an AI that listens for coughing and sneezing sounds to estimate what percentage of people in a public space have a respiratory illness. The device, called FluSense, was initially tested over an eight month period in four clinic waiting rooms on the university's campus. In addition to recording'non-speech' audio samples, FluSense is also equipped with a thermal camera to scan for people with elevated temperatures. According to its co-creator, Tauhidur Rahman, the device isn't meant to single out individual cases of illness but capture trends at the population level to see if something is developing that may not yet have been picked up in medical testing. 'I thought if we could capture coughing or sneezing sounds from public spaces where a lot of people naturally congregate, we could utilize this information as a new source of data for predicting epidemiologic trends,' he told UMass Amherst's news blog.