Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table

Clinical Treatment Human Disease Networks and Comparative Effectiveness Research: Analyses of the Medicare Administrative Data

As the nation’s largest healthcare payer, the Medicare program generates an unimaginable vast volume of medical data. With an increasing emphasis on evidence-based care, how to effectively handle and make inferences from the heterogeneous and noisy healthcare data remains an important question. High-quality analysis could improve the quality, planning, and administrations of health services, evaluate comparative therapies, and forward research on epidemiology and disease etiology. This is especially true for older adults since this population’s health condition is generally complicated with multimorbidity, and the healthcare system for older adults is riddled with administrative and regulatory complexities. Taking advantage of the scaled and comprehensive Medicare data, this dissertation focuses on outcome research, human disease networks, and comparative effectiveness research for older adults. Healthcare outcome measures such as mortality, readmission, length of stay (LOS), and medical costs have been extensively studied. However, existing analysis generally focuses on one single disease (or at most a few pre-selected and closely related diseases) or all diseases combined. It is increasingly evident that human diseases are interconnected with each other. Motivated by the emerging human disease network (HDN) analysis, we conduct network analysis of disease interconnections on healthcare outcomes measures. First, we propose a clinical treatment HDN that analyzes inpatient LOS data. In the network graph, one node represents one disease, and two nodes are linked with an edge if their disease-specific LOS are correlated (conditional on LOS of all other diseases). To accommodate zero-inflated LOS data, we propose a network construction approach based on the multivariate Hurdle model. We analyze the Medicare inpatient data for the period of January 2008 to December 2018. Based on the constructed network, key network properties such as connectivity, module/hub, and temporal variation are analyzed. The results are found to be biomedically sensible, especially from a treatment perspective. A closer examination also reveals novel findings that are less/not investigated in the individual-disease studies. This work has been published in Statistics in Medicine. Second, considering that many healthcare outcomes are closely related to each other, we propose a high-dimensional clinical treatment HDN that can incorporate multiple outcomes. We construct a clinical treatment HDN on LOS and readmission and note that the proposed method can be easily generalized to other outcomes of different data types. To deal with uniquely challenging data distributions (high-dimensionality and zero-inflation), a new network construction approach is developed based on the integrative analysis of generalized linear models. Data analysis is conducted using the Medicare inpatient data from January 2010 to December 2018. Network structure and properties are found to be similar to that of the LOS HDN (in Chapter 2) but provide additional insights into disease interconnections considering both LOS and readmission. The proposed clinical treatment of HDNs can promote a better understanding of human diseases and their interconnections, guide a more efficient disease management and healthcare resources allocation, and foster complex network analysis. The manuscript of this work has been drafted and is ready for submission. Comparative effectiveness research aims to directly compare the outcomes of two or more healthcare strategies to address a particular medical condition. Such analysis can provide information about the risks, benefits, and costs of different treatment options, thus guide better clinical decisions. While conducting a randomized controlled trial is the gold-standard approach, there are several limitations. Efforts have been made to utilize healthcare record data in comparative effectiveness research. To estimate and compare causal effects of treatments/interventions, we use the Medicare data to emulate target clinical trials and develop a deep learning-based analysis approach. Under emulation, target clinical trials are explicitly “assembled” using the Medicare data. As such, statistical methods for clinical trials can be directly applied to estimate causal effects. With emulation analysis, we evaluate the effectiveness and safety outcomes of rivaroxaban versus dabigatran for Medicare patients with atrial fibrillation. The results show that dabigatran is superior in terms of time to any primary event (including ischemic stroke, other thromboembolic events, major bleeding, and death), major bleeding, and mortality. This work has been submitted to Clinical Epidemiology. Considering that many regression-based statistical methods (e.g., Cox proportional hazards model for survival data) have too strict data assumptions, we further develop an innovative deep learning-based analysis strategy. With the “emulation + deep learning” approach, we study the survival outcomes of endovascular repair versus open aortic repair for Medicare patients with abdominal aortic aneurysms. It is found that endovascular repair has survival advantages in both short- and long-term mortality. This work has been published in Entropy. Significantly different and advancing from the existing literature, this dissertation extends the scope of outcome research, human disease networks, and comparative effectiveness research. The findings in this dissertation are shown to have scientific merits, and the methodological developments may have other applications and serve as prototypes for future analysis

Mining Themes in Clinical Notes to Identify Phenotypes and to Predict Length of Stay in Patients admitted with Heart Failure

Heart failure is a syndrome which occurs when the heart is not able to pump blood and oxygen to support other organs in the body. Identifying the underlying themes in the diagnostic codes and procedure reports of patients admitted for heart failure could reveal the clinical phenotypes associated with heart failure and to group patients based on their similar characteristics which could also help in predicting patient outcomes like length of stay. These clinical phenotypes usually have a probabilistic latent structure and hence, as there has been no previous work on identifying phenotypes in clinical notes of heart failure patients using a probabilistic framework and to predict length of stay of these patients using data-driven artificial intelligence-based methods, we apply natural language processing technique, topic modeling, to identify the themes present in diagnostic codes and in procedure reports of 1,200 patients admitted for heart failure at the University of Illinois Hospital and Health Sciences System (UI Health). Topic modeling identified twelve themes each in diagnostic codes and procedure reports which revealed information about different phenotypes related to various perspectives about heart failure, to study patients\u27 profiles and to discover new relationships among medical concepts. Each theme had a set of keywords and each clinical note was labeled with two themes - one corresponding to its diagnostic code and the other corresponding to its procedure reports along with their percentage contribution. We used these themes and their percentage contribution to predict length of stay. We found that the themes discovered in diagnostic codes and procedure reports using topic modeling together were able to predict length of stay of the patients with an accuracy of 61.1% and an Area under the Receiver Operating Characteristic Curve (ROC AUC) value of 0.828

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI

Diagnóstico no invasivo de patologías humanas combinando análisis de aliento y modelización con redes neuronales

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Químicas, leída el 09-09-2016It is currently known that there is a direct relation between the moment a disease is detected or diagnosed and the consequences it will have on the patient, as an early detection is generally linked to a more favorable outcome. This concept is the basis of the present research, due to the fact that its main goal is the development of mathematical tools based on computational artificial intelligence to safely and non-invasively attain the detection of multiple diseases. To reach these devices, this research has focused on the breath analysis of patients with diverse diseases, using several analytical methodologies to extract the information contained in these samples, and multiple feature selection algorithms and neural networks for data analysis. In the past, it has been shown that there is a correlation between the molecular composition of breath and the clinical status of a human being, proving the existence of volatile biomarkers that can aid in disease detection depending on their presence or amount. During this research, two main types of analytical approaches have been employed to study the gaseous samples, and these were cross-reactive sensor arrays (based on organically functionalized silicon nanowire field-effect transistors (SiNW FETs) or gold nanoparticles (GNPs)) and proton transfer reaction-mass spectrometry (PTR-MS). The cross-reactive sensors analyze the bulk of the breath samples, offering global, fingerprint-like information, whereas PTR-MS quantifies the volatile molecules present in the samples. All of the analytical equipment employed leads to the generation of large amounts of data per sample, forcing the need of a meticulous mathematical analysis to adequately interpret the results. In this work, two fundamental types of mathematical tools were utilized. In first place, a set of five filter-based feature selection algorithms (χ2 (chi2) score, Fisher’s discriminant ratio, Kruskal-Wallis test, Relief-F algorithm, and information gain test) were employed to reduce the amount of independent in the large databases to the ones which contain the greatest discriminative power for a further modeling task. On the other hand, and in relation to mathematical modeling, artificial neural networks (ANNs), algorithms that are categorized as computational artificial intelligence, have been employed. These non-linear tools have been used to locate the relations between the independent variables of a system and the dependent ones to fulfill estimations or classifications. The type of ANN that has been used in this thesis coincides with the one that is more commonly employed in research, which is the supervised multilayer perceptron (MLP), due to its proven ability to create reliable models for many different applications...Actualmente es sabido que existe una relación directa entre el momento en el cual se detecta o diagnostica una enfermedad y las consecuencias que tendrá sobre el paciente, ya que una detección temprana va generalmente ligada a un desarrollo más favorable. Este concepto es el cimiento de la presente investigación, cuyo objetivo fundamental es el desarrollo de herramientas basadas en inteligencia artificial computacional que consigan, mediante medios seguros y no invasivos, la detección de diversas enfermedades. Para alcanzar dichos sistemas, los estudios han sido enfocados en el análisis de muestras de aliento de pacientes de diversas enfermedades, empleando varias técnicas para extraer información, y diversos algoritmos de selección de variables y redes neuronales para el procesamiento matemático. En el pasado, se ha comprobado que hay una correlación entre la composición molecular del aliento y el estado clínico de una persona, evidenciando la existencia de biomarcadores volátiles que pueden ayudar a detectar enfermedades, ya sea por su presencia o por su cantidad. Durante el transcurso de esta investigación, se han empleado esencialmente dos tipos de técnicas analíticas para estudiar las muestras gaseosas, y estas son conjuntos de sensores de reactividad cruzada (basados en transistores de efecto de campo con nanocables de silicio (SiNW FETs) o en nanopartículas de oro (GNPs), ambos funcionalizados con cadenas orgánicas) y equipos de reacción de transferencia de protones con espectrometría de masas (PTR-MS). Los sensores de reactividad cruzada analizan el aliento en su conjunto, extrayéndose información de la muestra global, mientras que usando PTR-MS, se cuantifican las moléculas volátiles presentes en las muestras analizadas. Todas las técnicas empleadas desembocan en la generación de grandes cantidades de datos por muestra, por lo que un análisis matemático exhaustivo es necesario para poder sacar el máximo rendimiento de los estudios. En este trabajo, se emplearon principalmente dos tipos de herramientas matemáticas. Las primeras son un grupo de cinco algoritmos de selección de variables, concretamente, filtros de variables (cálculos basados en estadística de χ2 (chi2), ratio discriminante de Fisher, análisis de Kruskal-Wallis, algoritmo relief-F y test de ganancia de información), que se han empleado en las bases de datos con grandes cantidades de variables independientes para localizar aquellas con mayor importancia o poder discriminativo para una tarea de modelización matemática posterior. Por otro lado, en cuando a dicha modelización, se ha empleado un tipo de algoritmo que se cataloga dentro del área de la inteligencia artificial computacional: las redes neuronales artificiales (ANNs). Estas herramientas matemáticas de naturaleza no lineal se han utilizado para localizar las relaciones existentes entre las variables independientes de un sistema y las variables dependientes o parámetros a estimar o clasificar. Se ha empleado el tipo de ANN supervisada más extensamente usado en investigación, que son los perceptrones multicapa (MLPs), debido a su habilidad contrastada para originar modelos fiables para numerosas aplicaciones...Fac. de Ciencias QuímicasTRUEunpu

The use of artificial intelligence systems in diagnosis of pneumonia via signs and symptoms : a systematic review

Artificial Intelligence (AI) systems using symptoms/signs to detect respiratory diseases may improve diagnosis especially in limited resource settings. Heterogeneity in such AI systems creates an ongoing need to analyse performance to inform future research. This systematic literature review aimed to investigate performance and reporting of diagnostic AI systems using machine learning (ML) for pneumonia detection based on symptoms and signs, and to provide recommendations on best practices for designing and implementing predictive ML algorithms. This article was conducted following the PRISMA protocol, 876 articles were identified by searching PubMed, Scopus, and OvidSP databases (last search 5th May 2021). For inclusion, studies must have differentiated clinically diagnosed pneumonia from controls or other diseases using AI. Risk of Bias was evaluated using The STARD 2015 tool. Information was extracted from 16 included studies regarding study characteristics, ML-model features, reference tests, study population, accuracy measures and ethical aspects. All included studies were highly heterogenous concerning the study design, setting of diagnosis, study population and ML algorithm. Study reporting quality in methodology and results was low. Ethical issues surrounding design and implementation of the AI algorithms were not well explored. Although no single performance measure was used in all studies, most reported an accuracy measure over 90%. There is strong evidence to support further investigations of ML to automatically detect pneumonia based on easily recognisable symptoms and signs. To help improve the efficacy of future research, recommendations for designing and implementing AI tools based on the findings of this study are provided

Respiratory issues in patients with multiple sclerosis as a risk factor during SARS-CoV-2 infection: a potential role for exercise

Coronavirus disease-2019 (COVID-19) is associated with cytokine storm and is characterized by acute respiratory distress syndrome (ARDS) and pneumonia problems. The respiratory system is a place of inappropriate activation of the immune system in people with multiple sclerosis (MS), and this may cause damage to the lung and worsen both MS and infections.The concerns for patients with multiple sclerosis are because of an enhance risk of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The MS patients pose challenges in this pandemic situation, because of the regulatory defect of autoreactivity of the immune system and neurological and respiratory tract symptoms. In this review, we first indicate respiratory issues associated with both diseases. Then, the main mechanisms inducing lung damages and also impairing the respiratory muscles in individuals with both diseases is discussed. At the end, the leading role of physical exercise on mitigating respiratory issues inducing mechanisms is meticulously evaluated. Keywords: COVID-19; Exercise training; Immune system; Multiple sclerosis; Renin–angiotensin system; Respiratory system