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Ricci CA, Crysup B, Phillips NR, Ray WC, Santillan MK, Trask AJ, Woerner AE, Goulopoulou S. Machine learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research. Am J Physiol Heart Circ Physiol 2024; 327:H417-H432. [PMID: 38847756 DOI: 10.1152/ajpheart.00149.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 05/31/2024] [Accepted: 05/31/2024] [Indexed: 06/10/2024]
Abstract
The maternal cardiovascular system undergoes functional and structural adaptations during pregnancy and postpartum to support increased metabolic demands of offspring and placental growth, labor, and delivery, as well as recovery from childbirth. Thus, pregnancy imposes physiological stress upon the maternal cardiovascular system, and in the absence of an appropriate response it imparts potential risks for cardiovascular complications and adverse outcomes. The proportion of pregnancy-related maternal deaths from cardiovascular events has been steadily increasing, contributing to high rates of maternal mortality. Despite advances in cardiovascular physiology research, there is still no comprehensive understanding of maternal cardiovascular adaptations in healthy pregnancies. Furthermore, current approaches for the prognosis of cardiovascular complications during pregnancy are limited. Machine learning (ML) offers new and effective tools for investigating mechanisms involved in pregnancy-related cardiovascular complications as well as the development of potential therapies. The main goal of this review is to summarize existing research that uses ML to understand mechanisms of cardiovascular physiology during pregnancy and develop prediction models for clinical application in pregnant patients. We also provide an overview of ML platforms that can be used to comprehensively understand cardiovascular adaptations to pregnancy and discuss the interpretability of ML outcomes, the consequences of model bias, and the importance of ethical consideration in ML use.
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Affiliation(s)
- Contessa A Ricci
- College of Nursing, Washington State University, Spokane, Washington, United States
- IREACH: Institute for Research and Education to Advance Community Health, Washington State University, Seattle, Washington, United States
- Elson S. Floyd College of Medicine, Washington State University, Spokane, Washington, United States
| | - Benjamin Crysup
- Department of Microbiology, Immunology and Genetics, University of North Texas Health Science, Fort Worth, Texas, United States
- Center for Human Identification, University of North Texas Health Science Center, Fort Worth, Texas, United States
| | - Nicole R Phillips
- Department of Microbiology, Immunology and Genetics, University of North Texas Health Science, Fort Worth, Texas, United States
| | - William C Ray
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio, United States
| | - Mark K Santillan
- Department of Obstetrics and Gynecology, University of Iowa Carver College of Medicine, Iowa City, Iowa, United States
| | - Aaron J Trask
- Center for Cardiovascular Research, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, Ohio, United States
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio, United States
| | - August E Woerner
- Department of Microbiology, Immunology and Genetics, University of North Texas Health Science, Fort Worth, Texas, United States
- Center for Human Identification, University of North Texas Health Science Center, Fort Worth, Texas, United States
| | - Styliani Goulopoulou
- Lawrence D. Longo Center for Perinatal Biology, Departments of Basic Sciences, Gynecology and Obstetrics, Loma Linda University, Loma Linda, California, United States
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Hsieh WH, Ku CCY, Hwang HPC, Tsai MJ, Chen ZZ. Model for Predicting Complications of Hemodialysis Patients Using Data From the Internet of Medical Things and Electronic Medical Records. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 11:375-383. [PMID: 37435541 PMCID: PMC10332468 DOI: 10.1109/jtehm.2023.3234207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 10/24/2022] [Accepted: 12/28/2022] [Indexed: 09/30/2023]
Abstract
Intelligent models for predicting hemodialysis-related complications, i.e., hypotension and the deterioration of the quality or obstruction of the AV fistula, based on machine learning (ML) methods were established to offer early warnings to medical staff and give them enough time to provide pre-emptive treatment. A novel integration platform collected data from the Internet of Medical Things (IoMT) at a dialysis center and inspection results from electronic medical records (EMR) to train ML algorithms and build models. The selection of the feature parameters was implemented using Pearson's correlation method. Then, the eXtreme Gradient Boost (XGBoost) algorithm was chosen to create the predictive models and optimize the feature choice. 75% of collected data are used as a training dataset and the other 25% are used as a testing dataset. We adopted the prediction precision and recall rate of hypotension and AV fistula obstruction to measure the effectiveness of the predictive models. These rates were sufficiently high at approximately 71%-90%. In the context of hemodialysis, hypotension and the deterioration of the quality or obstruction of the arteriovenous (AV) fistula affect treatment quality and patient safety and may lead to a poor prognosis. Our prediction models with high accuracies can provide excellent references and signals for clinical healthcare service providers. Clinical and Translational Impact Statement-With the integrated dataset collected from IoMT and EMR, the superior predictive results of our models for complications of hemodialysis patients are demonstrated. We believe, after enough clinical tests are implemented as planned, these models can assist the healthcare team in making appropriate preparations in advance or adjusting the medical procedures to avoid these adverseevents.
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Affiliation(s)
- Wen-Huai Hsieh
- Department of SurgeryChang-Hua HospitalMinistry of Health and WelfareChanghua513007Taiwan
| | - Cooper Cheng-Yuan Ku
- Institute of Information Management, National Yang Ming Chiao Tung UniversityHsinchu300093Taiwan
| | - Humble Po-Ching Hwang
- Institute of Information Management, National Yang Ming Chiao Tung UniversityHsinchu300093Taiwan
| | - Min-Juei Tsai
- Department of NephrologyChang-Hua HospitalMinistry of Health and WelfareChanghua513007Taiwan
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Quantification and Visualization of Reliable Hemodynamics Evaluation Based on Non-Contact Arteriovenous Fistula Measurement. SENSORS 2022; 22:s22072745. [PMID: 35408356 PMCID: PMC9003522 DOI: 10.3390/s22072745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 03/29/2022] [Accepted: 03/31/2022] [Indexed: 02/04/2023]
Abstract
The condition of arteriovenous fistula (AVF) blood flow is typically checked by using auscultation; however, auscultation should require a qualitative judgment dependent on the skills of doctors, and further attention to contact infection is required. For these reasons, this study developed a non-contact and non-invasive medical device to measure the pulse wave of AVFs by applying optical imaging technology. As a first step toward realization of the quantification judgment based on non-contact AVF measurement, we experimentally validated the developed system, whereby the hemodynamics of 168 subjects were visually and quantitatively evaluated based on clinical tests. Based on the evaluation results, the fundamental statistical characteristics of the non-contact measurement, including the average and median values, and distribution of measured signal-to-noise power ratio, were demonstrated. The clinical test results contributed to the future construction of quantified criteria for the AVF condition with the non-contact measurement.
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Loh HW, Xu S, Faust O, Ooi CP, Barua PD, Chakraborty S, Tan RS, Molinari F, Acharya UR. Application of photoplethysmography signals for healthcare systems: An in-depth review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 216:106677. [PMID: 35139459 DOI: 10.1016/j.cmpb.2022.106677] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/30/2022] [Accepted: 01/30/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVES Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals. METHODS We applied PRISMA guidelines to systematically search various journal databases and identified 43 PPG studies that fit the criteria of this review. RESULTS Twenty-five health issues were identified from these studies that were classified into six categories: cardiac, blood pressure, sleep health, mental health, diabetes, and miscellaneous. Various routes were employed in these PPG studies to perform the diagnosis: machine learning, deep learning, and statistical routes. The studies were reviewed and summarized. CONCLUSIONS We identified limitations such as poor standardization of sampling frequencies and lack of publicly available PPG databases. We urge that future work should consider creating more publicly available databases so that a wide spectrum of health problems can be covered. We also want to promote the use of PPG signals as a potential precision medicine tool in both ambulatory and hospital settings.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Shuting Xu
- Cogninet Australia, Sydney, New South Wales 2010, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Australia
| | - Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, United Kingdom
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Prabal Datta Barua
- Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; School of Business (Information Systems), Faculty of Business, Education, Law and Arts, University of Southern Queensland, Australia
| | - Subrata Chakraborty
- School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia; Centre for Advanced Modelling and Geospatial lnformation Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, 169609, Singapore; Duke-NUS Medical School, 169857, Singapore
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, Italy
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Business (Information Systems), Faculty of Business, Education, Law and Arts, University of Southern Queensland, Australia; School of Engineering, Ngee Ann Polytechnic, 535 Clementi Road, 599489, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan.
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Díez-Sanmartín C, Sarasa-Cabezuelo A, Andrés Belmonte A. The impact of artificial intelligence and big data on end-stage kidney disease treatments. EXPERT SYSTEMS WITH APPLICATIONS 2021; 180:115076. [DOI: 10.1016/j.eswa.2021.115076] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
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Arteriovenous Fistula Flow Dysfunction Surveillance: Early Detection Using Pulse Radar Sensor and Machine Learning Classification. BIOSENSORS 2021; 11:bios11090297. [PMID: 34562887 PMCID: PMC8471431 DOI: 10.3390/bios11090297] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 08/21/2021] [Accepted: 08/24/2021] [Indexed: 12/24/2022]
Abstract
Vascular Access (VA) is often referred to as the “Achilles heel” for a Hemodialysis (HD)-dependent patient. Both the patent and sufficient VA provide adequacy for performing dialysis and reducing dialysis-related complications, while on the contrary, insufficient VA is the main reason for recurrent hospitalizations, high morbidity, and high mortality in HD patients. A non-invasive Vascular Wall Motion (VWM) monitoring system, made up of a pulse radar sensor and Support Vector Machine (SVM) classification algorithm, has been developed to detect access flow dysfunction in Arteriovenous Fistula (AVF). The harmonic ratios derived from the Fast Fourier Transform (FFT) spectrum-based signal processing technique were employed as the input features for the SVM classifier. The result of a pilot clinical trial showed that a more accurate prediction of AVF flow dysfunction could be achieved by the VWM monitor as compared with the Ultrasound Dilution (UD) flow monitor. Receiver Operating Characteristic (ROC) curve analysis showed that the SVM classification algorithm achieved a detection specificity of 100% at detection thresholds in the range from 500 to 750 mL/min and a maximum sensitivity of 95.2% at a detection threshold of 750 mL/min.
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KHAZAEI SALMAN, NAJAFI-GhOBADI SOMAYEH, RAMEZANI-DOROH VAJIHE. Construction data mining methods in the prediction of death in hemodialysis patients using support vector machine, neural network, logistic regression and decision tree. JOURNAL OF PREVENTIVE MEDICINE AND HYGIENE 2021; 62:E222-E230. [PMID: 34322640 PMCID: PMC8283642 DOI: 10.15167/2421-4248/jpmh2021.62.1.1837] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 12/21/2021] [Indexed: 11/25/2022]
Abstract
Objectives Chronic kidney disease (CKD) is one of the main causes of morbidity and mortality worldwide. Detecting survival modifiable factors could help in prioritizing the clinical care and offers a treatment decision-making for hemodialysis patients. The aim of this study was to develop the best predictive model to explain the predictors of death in Hemodialysis patients by data mining techniques. Methods In this study, we used a dataset included records of 857 dialysis patients. Thirty-one potential risk factors, that might be associated with death in dialysis patients, were selected. The performances of four classifiers of support vector machine, neural network, logistic regression and decision tree were compared in terms of sensitivity, specificity, total accuracy, positive likelihood ratio and negative likelihood ratio. Results The average total accuracy of all methods was over 61%; the greatest total accuracy belonged to logistic regression (0.71). Also, logistic regression produced the greatest specificity (0.72), sensitivity (0.69), positive likelihood ratio (2.48) and the lowest negative likelihood ratio (0.43). Conclusions Logistic regression had the best performance in comparison to other methods for predicting death among hemodialysis patients. According to this model female gender, increasing age at diagnosis, addiction, low Iron level, C-reactive protein positive and low urea reduction ratio (URR) were the main predictors of death in these patients.
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Affiliation(s)
- SALMAN KHAZAEI
- Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran
| | - SOMAYEH NAJAFI-GhOBADI
- Department of Industrial Engineering, Faculty of Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
| | - VAJIHE RAMEZANI-DOROH
- Department of Health Management and Economics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
- Modeling of Non-communicable diseases research center, Hamadan University of Medical Sciences, Hamadan, Iran
- Correspondence: Vajihe Ramezani-Doroh, Hamadan University of Medical Sciences, Shahid Fahmide St., Pazhuhesh Square., Hamadan, Iran - Tel.: +98 9175375707 - E-mail:
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Kumar S, Buckley JL, Barton J, Pigeon M, Newberry R, Rodencal M, Hajzeraj A, Hannon T, Rogers K, Casey D, O’Sullivan D, O’Flynn B. A Wristwatch-Based Wireless Sensor Platform for IoT Health Monitoring Applications. SENSORS 2020; 20:s20061675. [PMID: 32192204 PMCID: PMC7147171 DOI: 10.3390/s20061675] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 03/06/2020] [Accepted: 03/16/2020] [Indexed: 11/16/2022]
Abstract
A wristwatch-based wireless sensor platform for IoT wearable health monitoring applications is presented. The paper describes the platform in detail, with a particular focus given to the design of a novel and compact wireless sub-system for 868 MHz wristwatch applications. An example application using the developed platform is discussed for arterial oxygen saturation (SpO2) and heart rate measurement using optical photoplethysmography (PPG). A comparison of the wireless performance in the 868 MHz and the 2.45 GHz bands is performed. Another contribution of this work is the development of a highly integrated 868 MHz antenna. The antenna structure is printed on the surface of a wristwatch enclosure using laser direct structuring (LDS) technology. At 868 MHz, a low specific absorption rate (SAR) of less than 0.1% of the maximum permissible limit in the simulation is demonstrated. The measured on-body prototype antenna exhibits a −10 dB impedance bandwidth of 36 MHz, a peak realized gain of −4.86 dBi and a radiation efficiency of 14.53% at 868 MHz. To evaluate the performance of the developed 868 MHz sensor platform, the wireless communication range measurements are performed in an indoor environment and compared with a commercial Bluetooth wristwatch device.
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Affiliation(s)
- Sanjeev Kumar
- Tyndall National Institute, University College Cork, Dyke Parade, T12R5CP Cork, Ireland; (J.L.B.); (J.B.); (M.P.); (A.H.); (K.R.); (D.C.); (D.O.); (B.O.)
- Correspondence: ; Tel.: +353-212-346-109
| | - John L. Buckley
- Tyndall National Institute, University College Cork, Dyke Parade, T12R5CP Cork, Ireland; (J.L.B.); (J.B.); (M.P.); (A.H.); (K.R.); (D.C.); (D.O.); (B.O.)
| | - John Barton
- Tyndall National Institute, University College Cork, Dyke Parade, T12R5CP Cork, Ireland; (J.L.B.); (J.B.); (M.P.); (A.H.); (K.R.); (D.C.); (D.O.); (B.O.)
| | - Melusine Pigeon
- Tyndall National Institute, University College Cork, Dyke Parade, T12R5CP Cork, Ireland; (J.L.B.); (J.B.); (M.P.); (A.H.); (K.R.); (D.C.); (D.O.); (B.O.)
| | - Robert Newberry
- Sanmina Corporation, 13000 S. Memorial Parkway, Huntsville, AL 35803, USA; (R.N.); (M.R.); (T.H.)
| | - Matthew Rodencal
- Sanmina Corporation, 13000 S. Memorial Parkway, Huntsville, AL 35803, USA; (R.N.); (M.R.); (T.H.)
| | - Adhurim Hajzeraj
- Tyndall National Institute, University College Cork, Dyke Parade, T12R5CP Cork, Ireland; (J.L.B.); (J.B.); (M.P.); (A.H.); (K.R.); (D.C.); (D.O.); (B.O.)
| | - Tim Hannon
- Sanmina Corporation, 13000 S. Memorial Parkway, Huntsville, AL 35803, USA; (R.N.); (M.R.); (T.H.)
| | - Ken Rogers
- Tyndall National Institute, University College Cork, Dyke Parade, T12R5CP Cork, Ireland; (J.L.B.); (J.B.); (M.P.); (A.H.); (K.R.); (D.C.); (D.O.); (B.O.)
| | - Declan Casey
- Tyndall National Institute, University College Cork, Dyke Parade, T12R5CP Cork, Ireland; (J.L.B.); (J.B.); (M.P.); (A.H.); (K.R.); (D.C.); (D.O.); (B.O.)
| | - Donal O’Sullivan
- Tyndall National Institute, University College Cork, Dyke Parade, T12R5CP Cork, Ireland; (J.L.B.); (J.B.); (M.P.); (A.H.); (K.R.); (D.C.); (D.O.); (B.O.)
| | - Brendan O’Flynn
- Tyndall National Institute, University College Cork, Dyke Parade, T12R5CP Cork, Ireland; (J.L.B.); (J.B.); (M.P.); (A.H.); (K.R.); (D.C.); (D.O.); (B.O.)
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