Article
Economics
Dweepobotee Brahma, Debasri Mukherjee
Summary: This study utilized nationwide household survey data from India and various machine learning techniques to identify important predictors of neonatal and infant mortality. The findings suggest effective policy interventions and highlight the importance of close monitoring for at-risk babies, providing insights for policy relevance.
Article
Computer Science, Theory & Methods
Daniel McDuff
Summary: This article explores the need for remote tools in healthcare monitoring, specifically focusing on camera measurement of vital signs. It highlights the advancements made in optics, machine learning, computer vision, and medicine that have enabled the progress in this field. The article provides a comprehensive survey of the techniques used for camera measurement of physiological vital signs, covering both clinical and non-clinical applications and discussing the challenges that need to be overcome for further development.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Information Systems
Fengyu Wang, Xiaolu Zeng, Chenshu Wu, Beibei Wang, K. J. Ray Liu
Summary: As automobiles have become an essential part of our daily lives, advanced driver assistance systems (ADASs) are gaining attention in enhancing driver safety and convenience. In this study, researchers propose a novel system that uses commercial millimeter-wave radio technology to estimate the driver's vital signs even in the presence of motion artifacts. Experimental results show that the proposed system achieves high accuracy in real driving environments.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Chemistry, Analytical
Nikos Mitro, Katerina Argyri, Lampros Pavlopoulos, Dimitrios Kosyvas, Lazaros Karagiannidis, Margarita Kostovasili, Fay Misichroni, Eleftherios Ouzounoglou, Angelos Amditis
Summary: This work presents the development of a low-cost wearable device for monitoring passengers' physiological state and stress levels during emergency evacuations of large passenger ships. It uses preprocessed PPG signals to provide essential biometric data and utilizes a machine learning pipeline for stress detection. The system was trained using the WESAD dataset and achieved an accuracy score of 91% in the evaluation stage and 76% in the external validation stage with real-life stressors.
Article
Computer Science, Interdisciplinary Applications
Li Yijing, Ye Wenyu, Yang Kang, Zhang Shengyu, He Xianliang, Jin Xingliang, Wang Cheng, Sun Zehui, Liu Mengxing
Summary: This study developed and validated a real-time, interpretable machine learning model called the Cardiac Arrest Prediction Index (CAPI) to predict cardiac arrest in critically ill patients based on bedside vital signs monitoring. The CAPI accurately predicted 95% of cardiac arrest events on the test set, with 80% identified more than 25 minutes in advance.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Chemistry, Analytical
Srikrishna Iyer, Leo Zhao, Manoj Prabhakar Mohan, Joe Jimeno, Mohammed Yakoob Siyal, Arokiaswami Alphones, Muhammad Faeyz Karim
Summary: This study presents a non-contact monitoring system that utilizes FMCW radar to measure the heart and breathing rate of humans, and employs an artificial neural network model to predict the presence of arrhythmia. The experimental results demonstrate high measurement accuracy of the system under specific orientations, distances, and movement situations.
Article
Engineering, Electrical & Electronic
Giacomo Paterniani, Daria Sgreccia, Alessandro Davoli, Giorgio Guerzoni, Pasquale Di Viesti, Anna Chiara Valenti, Marco Vitolo, Giorgio M. Vitetta, Giuseppe Boriani
Summary: In recent years, the use of radar systems in health monitoring has received significant attention due to the availability of low-cost radar devices and computationally efficient signal processing algorithms. This article provides a tutorial overview of radar-based monitoring of vital signs, including an introduction to radar technologies and signal processing algorithms, guidelines for selecting radar devices for vital sign monitoring, and various specific applications and research trends in this field.
PROCEEDINGS OF THE IEEE
(2023)
Article
Computer Science, Artificial Intelligence
Haibo Liang, Haochen Han, Pengbo Ni, Yingjun Jiang
Summary: An optimized remote monitoring platform for overflow accidents was proposed, including a random forest model based on bat algorithm optimization for diagnosis and classification, resulting in a 6.67% increase in accuracy rate.
NEURAL COMPUTING & APPLICATIONS
(2021)
Review
Engineering, Electrical & Electronic
Lucy Taylor, Xiaorong Ding, David Clifton, Huiqi Lu
Summary: Worldwide, an estimated 461,000 people die from asthma attacks each year. This article aims to provide a comprehensive review of the current state-of-the-art wearable sensors and devices that use vital signs for asthma patient monitoring and management. These technologies can directly measure breathing rate and airflow sound or indirectly estimate them using algorithms based on electrocardiogram (ECG), photoplethysmogram (PPG), and chest movements. Other vital signs used in asthma patient monitoring include blood oxygen saturation, temperature, blood pressure, verbal sound, and pain responses.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Khadija Hanifi, M. Elif Karsligil
Summary: The study developed a low-cost, high-accuracy fall detection system using radar technology to observe indoor activities and detect fall accidents, aiming to reduce the risks of undiscovered falls. Experimental results showed that the system achieved a 90% recall rate for fall detection, with accuracy rates of 97.7% for respiration and 95.3% for heartbeat detection.
IEEE SENSORS JOURNAL
(2021)
Article
Forestry
Yanhui Liu, Junbao Huang, Ruihua Xiao, Shiwei Ma, Pinggen Zhou
Summary: This paper introduces a method for constructing a regional landslide early-warning model based on machine learning and validates it using Fujian Province in China as an example. The research results show that the Random Forest algorithm is the most effective model with high hit rate and accurate warnings.
Article
Optics
Tao Zhao, Xuelei Fu, Jing Zhan, Kewei Chen, Zhengying Li
Summary: This study proposes a method for monitoring vital signs during sleep using a mat embedded with a macrobending optical fiber sensor, allowing for non-contact monitoring based on vibration sensing. By exploiting whispering gallery mode and small-core fiber to enhance sensitivity, it can simultaneously detect respiration and cardiac vibrations.
Review
Chemistry, Analytical
Mahmoud Salem, Ahmed Elkaseer, Islam A. M. El-Maddah, Khaled Y. Youssef, Steffen G. Scholz, Hoda K. Mohamed
Summary: The rapid development of smart healthcare technology has transformed the relationship between healthcare providers and patients. Challenges remain in the development of multi-frequency vital IoT systems and the implementation of multi-camera systems and deep learning. The integration of reliable power management and energy harvesting systems into non-invasive data acquisition is also an important area for future research.
Review
Engineering, Multidisciplinary
Marlin Ramadhan Baidillah, Pratondo Busono, Riyanto Riyanto
Summary: This study reviews the application of machine learning algorithms (MLA) in predicting and detecting asynchronous breathing (AB) during mechanical ventilation (MV). It proposes that MLA can extract patterns from continuous vital sign monitoring data and serve as an early warning system for MV intervention, reducing the cognitive load on clinicians.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Review
Chemistry, Analytical
Vinothini Selvaraju, Nicolai Spicher, Ju Wang, Nagarajan Ganapathy, Joana M. Warnecke, Steffen Leonhardt, Ramakrishnan Swaminathan, Thomas M. Deserno
Summary: In recent years, there has been a growing interest in noncontact measurements of vital signs using cameras. However, there are still unanswered questions regarding the specific vital signs monitored, the performance and influencing factors, and the health issues addressed by camera-based techniques. This systematic review analyzed articles published between January 2018 and April 2021, focusing on continuous camera-based monitoring of five vital signs: heart rate, respiratory rate, blood pressure, body skin temperature, and oxygen saturation. The review found that heart rate and respiratory rate can be measured using different types of cameras, whereas blood pressure and oxygen saturation are mainly monitored using RGB cameras. Camera-based techniques are commonly used in intensive care, post-anaesthesia care, and sleep monitoring, and also investigate specific diseases such as heart failure. The monitored subjects range from newborn and pediatric patients to geriatric patients, athletes, and vehicle drivers. The accuracy of camera-based monitoring varies for different vital signs, with heart rate, respiratory rate, and body skin temperature being more reliably measured under static conditions compared to blood pressure and oxygen saturation.
Article
Anesthesiology
Jesper Molgaard, Soren Straarup Rasmussen, Jonas Eiberg, Helge Bjarup Dissing Sorensen, Christian Sylvest Meyhoff, Eske Kvanner Aasvang
Summary: Deviating physiology is common before and after vascular surgery. Severe desaturation had a longer duration on the first postoperative day compared to preoperatively, while moderate desaturations were reflected in postoperative desaturations. Cumulative duration outside thresholds is, in some cases, exacerbated after surgery.
ACTA ANAESTHESIOLOGICA SCANDINAVICA
(2023)
Article
Medicine, General & Internal
Asger K. Molgaard, Kasper S. Gasbjerg, Christian S. Meyhoff, Troels H. Lunn, Janus C. Jakobsen, Ismail Gogemur, Ole Mathiesen, Daniel Hagl-Pedersen
Summary: This study compared the effects of dexamethasone and placebo on the concentration of cardiac troponin I and T after total knee arthroplasty. The results showed that dexamethasone had no effect on the postoperative troponin levels on the first morning after surgery.
AMERICAN JOURNAL OF MEDICINE
(2023)
Article
Clinical Neurology
Andreas Brink-Kjaer, Niraj Gupta, Eric Marin, Jennifer Zitser, Oliver Sum-Ping, Anahid Hekmat, Flavia Bueno, Ana Cahuas, James Langston, Poul Jennum, Helge B. D. Sorensen, Emmanuel Mignot, Emmanuel During
Summary: The study aimed to develop a high-frequency actigraphy and questionnaire-based machine learning classifier for detecting isolated rapid-eye-movement sleep behavior disorder (iRBD). The results showed that both the actigraphy and questionnaire classifiers achieved high accuracy and precision, and the combination of actigraphy and questionnaire had the best predictive performance.
MOVEMENT DISORDERS
(2023)
Article
Engineering, Biomedical
Mads Olsen, Jamie M. Zeitzer, Risa N. Richardson, Polina Davidenko, Poul J. Jennum, Helge B. D. Sorensen, Emmanuel Mignot
Summary: Wrist-worn consumer sleep technologies with accelerometers and photoplethysmography have potential to be sleep monitoring systems. A deep neural network with a strong temporal core was developed to predict sleep stages using ACC and PPG signals. The network achieved high accuracy and kappa values on both internal and external datasets.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2023)
Article
Clinical Neurology
Umaer Hanif, Eva Kirkegaard Kiaer, Robson Capasso, Stanley Y. Liu, Emmanuel J. M. Mignot, Helge B. D. Sorensen, Poul Jennum
Summary: This study proposes a deep learning approach for automatic scoring of obstructive sleep apnea using drug-induced sleep endoscopy (DISE) videos. With an average F1 score of 70% across 281 DISE videos, the automated scoring shows high validity and feasibility in assessing the degree of upper airway collapse.
Article
Anesthesiology
Allan Koster, Christian Sylvest Meyhoff, Lars Peter Kloster Andersen
Summary: During the COVID-19 pandemic in Denmark, COVID-19-positive patients in the ICU experienced self-alienation, a sense of captivity, surrealism, extreme loneliness, and intercorporeal deprivation. This study provides valuable insights into the experiences of isolated patients through in-depth interviews and observations.
ACTA ANAESTHESIOLOGICA SCANDINAVICA
(2023)
Article
Anesthesiology
Katja Kjaer Gronbaek, Soren Moller Rasmussen, Natasha Hemicke Langer, Mette Vincentz, Anne-Britt Oxboll, Marlene Sogaard, Hussein Nasser Awada, Tomas O. Jensen, Magnus Thorsten Jensen, Helge B. D. Sorensen, Eske Kvanner Aasvang, Christian Sylvest Meyhoff
Summary: Continuous monitoring of vital signs in COVID-19 patients can detect deviations earlier and provides more accurate information compared to intermittent measurements. Patients admitted to ICU or having fatal outcomes tend to have longer durations of vital sign deviations.
ACTA ANAESTHESIOLOGICA SCANDINAVICA
(2023)
Article
Chemistry, Analytical
Luna Fabricius Ekenberg, Dan Eik Hofsten, Soren M. Rasmussen, Jesper Molgaard, Philip Hasbak, Helge B. D. Sorensen, Christian S. Meyhoff, Eske K. Aasvang
Summary: This study assessed the accuracy of wearable wireless electrocardiographic (ECG) monitoring in detecting myocardial ischemia, and found that there was a difference in ST-segment deviation between single-lead and 12-lead ECG. Both methods had low sensitivity for detecting reversible myocardial ischemia.
Article
Anesthesiology
Andreas Ohrt Johansen, Jesper Molgaard, Soren Straarup Rasmussen, Ying Gu, Katja Kjaer Gronbaek, Helge B. D. Sorensen, Eske Kvanner Aasvang, Christian Sylvest Meyhoff
Summary: Monitoring of high-risk patients in hospital wards is crucial in identifying and preventing clinical deterioration. Sympathetic nervous system activity measured by Electrodermal activity (EDA) may be related to complications, but its clinical use remains untested. This study found significant associations between specific deviations of EDA and subsequent serious adverse events (SAE), suggesting that EDA patterns could be indicators of upcoming clinical deterioration in high-risk patients.
JOURNAL OF CLINICAL MONITORING AND COMPUTING
(2023)
Article
Anesthesiology
Emilie Sigvardt, Katja Kjaer Gronbaek, Mia Lind Jepsen, Marlene Sogaard, Louise Haahr, Ana Inacio, Eske Kvanner Aasvang, Christian Sylvest Meyhoff
Summary: Continuous monitoring of vital signs significantly reduces workload compared to manual assessment.
ACTA ANAESTHESIOLOGICA SCANDINAVICA
(2023)
Article
Anesthesiology
Tayyba N. Aslam, Thomas L. Klitgaard, Christian A. O. Ahlstedt, Finn H. Andersen, Michelle S. Chew, Marie O. Collet, Maria Cronhjort, Stine Estrup, Ole K. Fossum, Shirin K. Frisvold, Hans-Joerg Gillmann, Anders Granholm, Trine M. Gundem, Kristin Hauss, Jacob Hollenberg, Maria E. Huanca Condori, Johanna Hastbacka, Bror A. Johnstad, Eric Keus, Maj-Brit N. Kjaer, Pal Klepstad, Mette Krag, Reidar Kvale, Manu L. N. G. Malbrain, Christian S. Meyhoff, Matt Morgan, Anders Moller, Carmen A. Pfortmueller, Lone M. Poulsen, Andrew C. Robertson, Joerg C. Schefold, Olav L. Schjorring, Martin Siegemund, Martin I. Sigurdsson, Fredrik Sjoevall, Kristian Strand, Thomas Stueber, Wojciech Szczeklik, Rebecka R. Wahlin, Helge L. Wangberg, Karl-Andre Wian, Sine Wichmann, Kristin Hofso, Morten H. Moller, Anders Perner, Bodil S. Rasmussen, Jon H. Laake, SVALBARD Investigators
Summary: This study reveals the uncertainty among clinicians when choosing ventilator modes for patients with acute hypoxaemic respiratory failure. The survey indicates that there is clinical equipoise for the preferred ventilator mode in moderate AHRF patients.
ACTA ANAESTHESIOLOGICA SCANDINAVICA
(2023)
Article
Anesthesiology
Sofie S. Pedersen, Martin Kryspin Sorensen, Markus Harboe Olsen, Zara R. Stisen, Anton Lund, Kirsten Moller, Jane Skjoth-Rasmussen, Finn B. Moltke, Christian S. Meyhoff
Summary: By comparing the ScO2 values measured in different locations, the study found no significant difference in ScO2 values measured on the forehead skin and on the dura mater, while the ScO2 values on the skull were lower than those on the skin, indicating an influence from scalp tissue on the signal.
ACTA ANAESTHESIOLOGICA SCANDINAVICA
(2023)
Review
Medicine, General & Internal
Stijn W. de Jonge, Rick H. Hulskes, Maedeh Zokaei Nikoo, Robert P. Weenink, Christian S. Meyhoff, Kate Leslie, Paul Myles, Andrew Forbes, Robert Greif, Ozan Akca, Andrea Kurz, Daniel Sessler, Janet Martin, Marcel G. W. Dijkgraaf, Kane Pryor, F. Javier Belda, Carlos Ferrando, Gabriel M. Gurman, Christina M. Scifres, David S. McKenna, Matthew T. Chan, Pascal Thibon, Jannicke Mellin-Olsen, Benedetta Allegranzi, Marja Boermeester, Markus W. Hollmann
Summary: The use of high fraction of inspired oxygen (FiO(2)) intraoperatively for the prevention of surgical site infection (SSI) remains controversial. Published data lacks participant level details to test hypotheses. The purpose of this meta-analysis is to evaluate the benefits and harms of high FiO(2) and its effect modifiers.
Article
Biology
Seyyed Bahram Borgheai, Alyssa Hillary Zisk, John McLinden, James Mcintyre, Reza Sadjadi, Yalda Shahriari
Summary: This study proposed a novel personalized scheme using fNIRS and EEG as the main tools to predict and compensate for the variability in BCI systems, especially for individuals with severe motor deficits. By establishing predictive models, it was found that there were significant associations between the predicted performances and the actual performances.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hongliang Guo, Hanbo Liu, Ahong Zhu, Mingyang Li, Helong Yu, Yun Zhu, Xiaoxiao Chen, Yujia Xu, Lianxing Gao, Qiongying Zhang, Yangping Shentu
Summary: In this paper, a BDSMA-based image segmentation method is proposed, which improves the limitations of the original algorithm by combining SMA with DE and introducing a cooperative mixing model. The experimental results demonstrate the superiority of this method in terms of convergence speed and precision compared to other methods, and its successful application to brain tumor medical images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jingfei Hu, Linwei Qiu, Hua Wang, Jicong Zhang
Summary: This study proposes a novel semi-supervised point consistency network (SPC-Net) for retinal artery/vein (A/V) classification, addressing the challenges of specific tubular structures and limited well-labeled data in CNN-based approaches. The SPC-Net combines an AVC module and an MPC module, and introduces point set representations and consistency regularization to improve the accuracy of A/V classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Summary: This study introduces a novel hybrid model called ConTraNet, which combines the strengths of CNN and Transformer neural networks, and achieves significant improvement in classification performance with limited training data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Juan Antonio Valera-Calero, Dario Lopez-Zanoni, Sandra Sanchez-Jorge, Cesar Fernandez-de-las-Penas, Marcos Jose Navarro-Santana, Sofia Olivia Calvo-Moreno, Gustavo Plaza-Manzano
Summary: This study developed an easy-to-use application for assessing the diagnostic accuracy of digital pain drawings (PDs) compared to the classic paper-and-pencil method. The results demonstrated that digital PDs have higher reliability and accuracy compared to paper-and-pencil PDs, and there were no significant differences in assessing pain extent between the two methods. The PAIN EXTENT app showed good convergent validity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Biao Qu, Jialue Zhang, Taishan Kang, Jianzhong Lin, Meijin Lin, Huajun She, Qingxia Wu, Meiyun Wang, Gaofeng Zheng
Summary: This study proposes a deep unrolled neural network, pFISTA-DR, for radial MRI image reconstruction, which successfully preserves image details using a preprocessing module, learnable convolution filters, and adaptive threshold.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Alireza Rafiei, Milad Ghiasi Rad, Andrea Sikora, Rishikesan Kamaleswaran
Summary: This study aimed to improve machine learning model prediction of fluid overload by integrating synthetic data, which could be translated to other clinical outcomes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jinlian Ma, Dexing Kong, Fa Wu, Lingyun Bao, Jing Yuan, Yusheng Liu
Summary: In this study, a new method based on MDenseNet is proposed for automatic segmentation of nodular lesions from ultrasound images. Experimental results demonstrate that the proposed method can accurately extract multiple nodules from thyroid and breast ultrasound images, with good accuracy and reproducibility, and it shows great potential in other clinical segmentation tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jiabao Sheng, SaiKit Lam, Jiang Zhang, Yuanpeng Zhang, Jing Cai
Summary: Omics fusion is an important preprocessing approach in medical image processing that assists in various studies. This study aims to develop a fusion methodology for predicting distant metastasis in nasopharyngeal carcinoma by mitigating the disparities in omics data and utilizing a label-softening technique and a multi-kernel-based neural network.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qianqian Wang, Mingyu Zhang, Aohan Li, Xiaojun Yao, Yingqing Chen
Summary: The development of PARP-1 inhibitors is crucial for the treatment of various cancers. This study investigates the structural regulation of PARP-1 by different allosteric inhibitors, revealing the basis of allosteric inhibition and providing guidance for the discovery of more innovative PARP-1 inhibitors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qing Xu, Wenting Duan
Summary: In this paper, a dual attention supervised module, named DualAttNet, is proposed for multi-label lesion detection in chest radiographs. By efficiently fusing global and local lesion classification information, the module is able to recognize targets with different sizes. Experimental results show that DualAttNet outperforms baselines in terms of mAP and AP50 with different detection architectures.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Kaja Gutowska, Piotr Formanowicz
Summary: The primary aim of this research is to propose algorithms for identifying significant reactions and subprocesses within biological system models constructed using classical Petri nets. These solutions enable two analysis methods: importance analysis for identifying critical individual reactions to the model's functionality and occurrence analysis for finding essential subprocesses. The utility of these methods has been demonstrated through analyses of an example model related to the DNA damage response mechanism. It should be noted that these proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods extend classical Petri net-based analyses, enhancing our comprehension of the investigated biological phenomena and aiding in the identification of potential molecular targets for drugs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hansle Gwon, Imjin Ahn, Yunha Kim, Hee Jun Kang, Hyeram Seo, Heejung Choi, Ha Na Cho, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Kye Hwa Lee, Tae Joon Jun, Young-Hak Kim
Summary: Electronic medical records have potential in advancing healthcare technologies, but privacy issues hinder their full utilization. Deep learning-based generative models can mitigate this problem by creating synthetic data similar to real patient data. However, the risk of data leakage due to malicious attacks poses a challenge to traditional generative models. To address this, we propose a method that employs local differential privacy (LDP) to protect the model from attacks and preserve the privacy of training data, while generating medical data with reasonable performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Siwei Tao, Zonghan Tian, Ling Bai, Yueshu Xu, Cuifang Kuang, Xu Liu
Summary: This study proposes a transfer learning-based method to address the phase retrieval problem in grating-based X-ray phase contrast imaging. By generating a training dataset and using deep learning techniques, this method improves image quality and can be applied to X-ray 2D and 3D imaging.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
