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Mullick T, Shaaban S, Radovic A, Doryab A. Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling. JMIR AI 2024; 3:e47805. [PMID: 38875667 PMCID: PMC11148522 DOI: 10.2196/47805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 09/16/2023] [Accepted: 04/09/2024] [Indexed: 06/16/2024]
Abstract
BACKGROUND Passive mobile sensing provides opportunities for measuring and monitoring health status in the wild and outside of clinics. However, longitudinal, multimodal mobile sensor data can be small, noisy, and incomplete. This makes processing, modeling, and prediction of these data challenging. The small size of the data set restricts it from being modeled using complex deep learning networks. The current state of the art (SOTA) tackles small sensor data sets following a singular modeling paradigm based on traditional machine learning (ML) algorithms. These opt for either a user-agnostic modeling approach, making the model susceptible to a larger degree of noise, or a personalized approach, where training on individual data alludes to a more limited data set, giving rise to overfitting, therefore, ultimately, having to seek a trade-off by choosing 1 of the 2 modeling approaches to reach predictions. OBJECTIVE The objective of this study was to filter, rank, and output the best predictions for small, multimodal, longitudinal sensor data using a framework that is designed to tackle data sets that are limited in size (particularly targeting health studies that use passive multimodal sensors) and that combines both user agnostic and personalized approaches, along with a combination of ranking strategies to filter predictions. METHODS In this paper, we introduced a novel ranking framework for longitudinal multimodal sensors (FLMS) to address challenges encountered in health studies involving passive multimodal sensors. Using the FLMS, we (1) built a tensor-based aggregation and ranking strategy for final interpretation, (2) processed various combinations of sensor fusions, and (3) balanced user-agnostic and personalized modeling approaches with appropriate cross-validation strategies. The performance of the FLMS was validated with the help of a real data set of adolescents diagnosed with major depressive disorder for the prediction of change in depression in the adolescent participants. RESULTS Predictions output by the proposed FLMS achieved a 7% increase in accuracy and a 13% increase in recall for the real data set. Experiments with existing SOTA ML algorithms showed an 11% increase in accuracy for the depression data set and how overfitting and sparsity were handled. CONCLUSIONS The FLMS aims to fill the gap that currently exists when modeling passive sensor data with a small number of data points. It achieves this through leveraging both user-agnostic and personalized modeling techniques in tandem with an effective ranking strategy to filter predictions.
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Affiliation(s)
- Tahsin Mullick
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, United States
| | | | - Ana Radovic
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Afsaneh Doryab
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, United States
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Tsang KCH, Pinnock H, Wilson AM, Shah SA. Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review. J Asthma Allergy 2022; 15:855-873. [PMID: 35791395 PMCID: PMC9250768 DOI: 10.2147/jaa.s285742] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 06/16/2022] [Indexed: 12/21/2022] Open
Abstract
Background Asthma is a variable long-term condition. Currently, there is no cure for asthma and the focus is, therefore, on long-term management. Mobile health (mHealth) is promising for chronic disease management but to be able to realize its potential, it needs to go beyond simply monitoring. mHealth therefore needs to leverage machine learning to provide tailored feedback with personalized algorithms. There is a need to understand the extent of machine learning that has been leveraged in the context of mHealth for asthma management. This review aims to fill this gap. Methods We searched PubMed for peer-reviewed studies that applied machine learning to data derived from mHealth for asthma management in the last five years. We selected studies that included some human data other than routinely collected in primary care and used at least one machine learning algorithm. Results Out of 90 studies, we identified 22 relevant studies that were then further reviewed. Broadly, existing research efforts can be categorized into three types: 1) technology development, 2) attack prediction, 3) patient clustering. Using data from a variety of devices (smartphones, smartwatches, peak flow meters, electronic noses, smart inhalers, and pulse oximeters), most applications used supervised learning algorithms (logistic regression, decision trees, and related algorithms) while a few used unsupervised learning algorithms. The vast majority used traditional machine learning techniques, but a few studies investigated the use of deep learning algorithms. Discussion In the past five years, many studies have successfully applied machine learning to asthma mHealth data. However, most have been developed on small datasets with internal validation at best. Small sample sizes and lack of external validation limit the generalizability of these studies. Future research should collect data that are more representative of the wider asthma population and focus on validating the derived algorithms and technologies in a real-world setting.
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Affiliation(s)
- Kevin C H Tsang
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Hilary Pinnock
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Andrew M Wilson
- Asthma UK Centre for Applied Research, and Norwich Medical School, University of East Anglia, Norwich, UK
| | - Syed Ahmar Shah
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
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Sheela MS, Arun CA. Hybrid PSO-SVM algorithm for Covid-19 screening and quantification. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY : AN OFFICIAL JOURNAL OF BHARATI VIDYAPEETH'S INSTITUTE OF COMPUTER APPLICATIONS AND MANAGEMENT 2022; 14:2049-2056. [PMID: 35036828 PMCID: PMC8752331 DOI: 10.1007/s41870-021-00856-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 12/27/2021] [Indexed: 11/17/2022]
Abstract
Corona Virus Disease (COVID) 19 has shaken the earth at its root and the devastation has increased the diagnostic burden of radiologists by large. At this crucial juncture, Artificial Intelligence (AI) will go a long way in decreasing the workload of physicians working in the outbreak zone, aiding them to accurately diagnose the new disease. In this work, a hybrid Particle Swarm Optimization–Support Vector Machine based AI algorithm is deployed to analyze the Computed Tomography images automatically providing a high probability in determining the presence of pneumonia due to COVID19. This paper presents a model for training the system to segregate and classify the presence of pneumonia which will in turn save around 50% of the time frame for physicians. This will be especially useful in places of outbreaks where a team of people are working together with the aid of artificial intelligence and/or medical background. The AI incorporated system was distributed in all areas of across the globe. It has been observed that challenges such as data security, testing time effectiveness of model, data discrepancy etc. were positively handled using the deployed system. Moreover, since the AI integrated system identifies the infected patients immediately physicians can confirm the infection and segregate the patients at the right period. A total of 200 training cases have been observed of which 150 were identified to be infected. The proposed work shows specificity of 0.85, a sensitivity of 0.956 and an accuracy of 95.78%.
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Affiliation(s)
| | - C A Arun
- Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, India
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Jung SY, Liao CH, Wu YS, Yuan SM, Sun CT. Efficiently Classifying Lung Sounds through Depthwise Separable CNN Models with Fused STFT and MFCC Features. Diagnostics (Basel) 2021; 11:732. [PMID: 33924146 PMCID: PMC8074359 DOI: 10.3390/diagnostics11040732] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/07/2021] [Accepted: 04/13/2021] [Indexed: 01/18/2023] Open
Abstract
Lung sounds remain vital in clinical diagnosis as they reveal associations with pulmonary pathologies. With COVID-19 spreading across the world, it has become more pressing for medical professionals to better leverage artificial intelligence for faster and more accurate lung auscultation. This research aims to propose a feature engineering process that extracts the dedicated features for the depthwise separable convolution neural network (DS-CNN) to classify lung sounds accurately and efficiently. We extracted a total of three features for the shrunk DS-CNN model: the short-time Fourier-transformed (STFT) feature, the Mel-frequency cepstrum coefficient (MFCC) feature, and the fused features of these two. We observed that while DS-CNN models trained on either the STFT or the MFCC feature achieved an accuracy of 82.27% and 73.02%, respectively, fusing both features led to a higher accuracy of 85.74%. In addition, our method achieved 16 times higher inference speed on an edge device and only 0.45% less accuracy than RespireNet. This finding indicates that the fusion of the STFT and MFCC features and DS-CNN would be a model design for lightweight edge devices to achieve accurate AI-aided detection of lung diseases.
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Affiliation(s)
- Shing-Yun Jung
- Department of Computer Science, National Chiao Tung University, Hsinchu 300, Taiwan; (C.-H.L.); (Y.-S.W.); (C.-T.S.)
| | - Chia-Hung Liao
- Department of Computer Science, National Chiao Tung University, Hsinchu 300, Taiwan; (C.-H.L.); (Y.-S.W.); (C.-T.S.)
| | - Yu-Sheng Wu
- Department of Computer Science, National Chiao Tung University, Hsinchu 300, Taiwan; (C.-H.L.); (Y.-S.W.); (C.-T.S.)
| | - Shyan-Ming Yuan
- Department of Computer Science, National Chiao Tung University, Hsinchu 300, Taiwan; (C.-H.L.); (Y.-S.W.); (C.-T.S.)
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Chuen-Tsai Sun
- Department of Computer Science, National Chiao Tung University, Hsinchu 300, Taiwan; (C.-H.L.); (Y.-S.W.); (C.-T.S.)
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
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Belkacem AN, Ouhbi S, Lakas A, Benkhelifa E, Chen C. End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19: A Theoretical Framework. Front Med (Lausanne) 2021; 8:585578. [PMID: 33869239 PMCID: PMC8044874 DOI: 10.3389/fmed.2021.585578] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 03/08/2021] [Indexed: 01/10/2023] Open
Abstract
Respiratory symptoms can be 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: 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 COVID-19 pandemic. Among the factors that contributed to the spread of the COVID-19 pandemic were the late diagnosis or misinterpretation of COVID-19 symptoms as regular flu-like symptoms. Research has shown that one of the possible differentiators of the underlying causes of different respiratory diseases could be the cough sound, which comes in different types and forms. A reliable lab-free tool for early and accurate diagnosis, which can differentiate between different respiratory diseases is therefore very much needed, particularly during the current pandemic. This concept paper discusses a medical hypothesis of an end-to-end portable system that can record data from patients with symptoms, 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 theoretical solution could play an important part in the early diagnosis.
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Affiliation(s)
- Abdelkader Nasreddine Belkacem
- Department of Computer and Network Engineering, College of Information Technology, UAE University, Al Ain, United Arab Emirates
| | - Sofia Ouhbi
- Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al Ain, United Arab Emirates
| | - Abderrahmane Lakas
- Department of Computer and Network Engineering, College of Information Technology, UAE University, Al Ain, United Arab Emirates
| | - Elhadj Benkhelifa
- Cloud Computing and Applications Research Lab, Staffordshire University, Stoke-on-Trent, United Kingdom
| | - Chao Chen
- Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, China
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Tabatabaei SAH, Fischer P, Schneider H, Koehler U, Gross V, Sohrabi K. Methods for Adventitious Respiratory Sound Analyzing Applications Based on Smartphones: A Survey. IEEE Rev Biomed Eng 2021; 14:98-115. [PMID: 32746364 DOI: 10.1109/rbme.2020.3002970] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Detection and classification of adventitious acoustic lung sounds plays an important role in diagnosing, monitoring, controlling and, caring the patients with lung diseases. Such systems can be presented as different platforms like medical devices, standalone software or smartphone application. Ubiquity of smartphones and widespread use of the corresponding applications make such a device an attractive platform for hosting the detection and classification systems for adventitious lung sounds. In this paper, the smartphone-based systems for automatic detection and classification of the adventitious lung sounds are surveyed. Such adventitious sounds include cough, wheeze, crackle and, snore. Relevant sounds related to abnormal respiratory activities are considered as well. The methods are shortly described and the analyzing algorithms are explained. The analysis includes detection and/or classification of the sound events. A summary of the main surveyed methods together with the classification parameters and used features for the sake of comparison is given. Existing challenges, open issues and future trends will be discussed as well.
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Trifan A, Oliveira M, Oliveira JL. Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations. JMIR Mhealth Uhealth 2019; 7:e12649. [PMID: 31444874 PMCID: PMC6729117 DOI: 10.2196/12649] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 05/24/2019] [Accepted: 05/28/2019] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Technological advancements, together with the decrease in both price and size of a large variety of sensors, has expanded the role and capabilities of regular mobile phones, turning them into powerful yet ubiquitous monitoring systems. At present, smartphones have the potential to continuously collect information about the users, monitor their activities and behaviors in real time, and provide them with feedback and recommendations. OBJECTIVE This systematic review aimed to identify recent scientific studies that explored the passive use of smartphones for generating health- and well-being-related outcomes. In addition, it explores users' engagement and possible challenges in using such self-monitoring systems. METHODS A systematic review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, to identify recent publications that explore the use of smartphones as ubiquitous health monitoring systems. We ran reproducible search queries on PubMed, IEEE Xplore, ACM Digital Library, and Scopus online databases and aimed to find answers to the following questions: (1) What is the study focus of the selected papers? (2) What smartphone sensing technologies and data are used to gather health-related input? (3) How are the developed systems validated? and (4) What are the limitations and challenges when using such sensing systems? RESULTS Our bibliographic research returned 7404 unique publications. Of these, 118 met the predefined inclusion criteria, which considered publication dates from 2014 onward, English language, and relevance for the topic of this review. The selected papers highlight that smartphones are already being used in multiple health-related scenarios. Of those, physical activity (29.6%; 35/118) and mental health (27.9; 33/118) are 2 of the most studied applications. Accelerometers (57.7%; 67/118) and global positioning systems (GPS; 40.6%; 48/118) are 2 of the most used sensors in smartphones for collecting data from which the health status or well-being of its users can be inferred. CONCLUSIONS One relevant outcome of this systematic review is that although smartphones present many advantages for the passive monitoring of users' health and well-being, there is a lack of correlation between smartphone-generated outcomes and clinical knowledge. Moreover, user engagement and motivation are not always modeled as prerequisites, which directly affects user adherence and full validation of such systems.
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Affiliation(s)
- Alina Trifan
- Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal
- Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro, Aveiro, Portugal
| | - Maryse Oliveira
- Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal
- Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro, Aveiro, Portugal
| | - José Luís Oliveira
- Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal
- Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro, Aveiro, Portugal
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