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Journal articles on the topic 'Patient's classification systems'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Ghunaimi, Bader Al, Abdulnasir Hossen, and Mohammed O. Hassan. "Statistical signal characterization for congestive heart failure patient's classification." Technology and Health Care 14, no. 1 (2006): 29–45. http://dx.doi.org/10.3233/thc-2006-14104.

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Merashka, Elina. "SURGICAL RISK ASSESSMENT IN PATIENTS WITH LIVER CIRRHOSIS – A LITERATURE REVIEW." Journal of IMAB - Annual Proceeding (Scientific Papers) 31, no. 1 (2025): 5978–84. https://doi.org/10.5272/jimab.2025311.5978.

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Patients with chronic liver injury and liver cirrhosis develop disease-specific pathophysiological abnormalities. These specific findings compromise the functional activity of other organs and systems, leading to poor postoperative outcomes and increased morbidity and mortality rates in this population of patients. This requires a thorough analysis of the aforementioned pathophysiological abnormalities in order to optimize the patient's clinical condition in the preoperative period and to improve their perioperative performance status. The impaired physiological reserves in patients with liver

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Gockel, I., and TH Junginger. "The Value of Scoring Achalasia: A Comparison of Current Systems and the Impact on Treatment–The Surgeon's Viewpoint." American Surgeon 73, no. 4 (2007): 327–31. http://dx.doi.org/10.1177/000313480707300403.

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Heller myotomy in patients with achalasia promises better long-term success than pneumatic dilation, especially in younger patients, and therefore has evolved as the primary treatment option. The aim of this study was to evaluate the impact of different disease-specific severity scores on achalasia treatment. Fifty consecutive patients undergoing pneumatic dilation (n = 25) or myotomy (n = 25) were assessed pre- and postinterventionally by clinical evaluation using the Eckardt Score, the Vantrappen Classification, and the Adams's Stages, as well as by radiologic and manometric studies and by s

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Abramov, Maxim, Ekaterina Tsukanova, Alexander Tulupyev, Anastasia Korepanova, and Sergei Aleksanin. "Identification of Deterioration caused by AHF, MADS or CE by RR and QT Data Classification." Informatics and Automation 21, no. 2 (2022): 311–38. http://dx.doi.org/10.15622/ia.21.2.4.

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A sharp deterioration of the patient’s condition against the backdrop of the development of life-threatening arrhythmias with symptoms of acute heart failure (AHF), multiple organ dysfunction syndrome (MODS) or cerebral edema (CE) can lead to the death of the patient. Since the known methods of automated diagnostics currently cannot accurately and promptly determine that the patient is in a life-threatening condition leading to the fatal outcome caused by AHF, MODS or CE, there is a need to develop appropriate methods. One of the ways to identify predictors of such a state is to apply machine

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Mills, Ken I., Alexander Kohlmann, P. Mickey Williams, et al. "Microarray-based classifiers and prognosis models identify subgroups with distinct clinical outcomes and high risk of AML transformation of myelodysplastic syndrome." Blood 114, no. 5 (2009): 1063–72. http://dx.doi.org/10.1182/blood-2008-10-187203.

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The diagnosis of myelodysplastic syndrome (MDS) currently relies primarily on the morphologic assessment of the patient's bone marrow and peripheral blood cells. Moreover, prognostic scoring systems rely on observer-dependent assessments of blast percentage and dysplasia. Gene expression profiling could enhance current diagnostic and prognostic systems by providing a set of standardized, objective gene signatures. Within the Microarray Innovations in LEukemia study, a diagnostic classification model was investigated to distinguish the distinct subclasses of pediatric and adult leukemia, as wel

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Sabreen Waheed Kadhum and Mohammed Ali Tawfeeq. "Improving Performance Classification in Wireless Body Area Sensor Networks Based on Machine Learning Techniques." Journal of Engineering and Sustainable Development 29, no. 1 (2025): 112–19. https://doi.org/10.31272/jeasd.2491.

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Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown pattern

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Praveena Nuthakki. "Mobile Cloud Healthcare Systems using Fuzzy Rules based Neural Networks Classification." Journal of Information Systems Engineering and Management 10, no. 27s (2025): 285–306. https://doi.org/10.52783/jisem.v10i27s.4409.

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Computer systems have many advantages over traditional emergency systems, such as the ability to quickly respond to changing circumstances and the reliability of never going down if a server experiences technical Difficulties, This article will introduce the Mobile cloud computing for emergency health care model (MCCEH) with use of a cloud computing server, with the goal of reducing reaction time to save a patient's life by offering services connected to healthcare in emergency instances. When a health emergency arises or a car accident takes place, The MCCEH model will let look up the closest

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Praveena Nuthakki. "Mobile Cloud Healthcare Systems using Fuzzy Rules based Neural Networks Classification." Journal of Information Systems Engineering and Management 9, no. 4s (2024): 841–63. https://doi.org/10.52783/jisem.v9i4s.11557.

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Computer systems have many advantages over traditional emergency systems, such as the ability to quickly respond to changing circumstances and the reliability of never going down if a server experiences technical Difficulties, This article will introduce the Mobile cloud computing for emergency health care model (MCCEH) with use of a cloud computing server, with the goal of reducing reaction time to save a patient's life by offering services connected to healthcare in emergency instances. When a health emergency arises or a car accident takes place, The MCCEH model will let look up the closest

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Bhaswanth, N., D. Venkata Sekhar, and M. Purushotham Reddy. "Psoriasis Skin Disease Classification based on Clinical Images." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11s (2023): 257–65. http://dx.doi.org/10.17762/ijritcc.v11i11s.8098.

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Psoriasis is an autoimmune skin disorder that causes skin plaques to develop into red and scaly patches. It affects millions of people globally. Dermatologists currently employ visual and haptic methods to determine a medical issue's severity. Intelligent medical imaging-based diagnosis systems are now a possibility because of the relatively recent development of deep learning technologies for medical image processing. These systems can help a human expert make better decisions about a patient's health. Convolutional neural networks, or CNNs, on the other hand, have achieved imaging performanc

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Manisha P. Patil and Dr. Uruj Jaleel. "Multimodal Disease Classification and Severity analysis Approaches using Machine and Deep Learning." Journal of Advances in Science and Technology 21, no. 1 (2024): 147–52. http://dx.doi.org/10.29070/24mw3w90.

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Chest X-rays are a common diagnostic tool for pulmonary and cardiac conditions in hospitals because they provide a clear picture of the patient's thorax. With the use of image-to-text radiology report production, medical imaging results may be automatically described in radiology reports. There are a lot of different pieces of patient data that radiologists may access, but most current systems only use the picture data. the objective of developing AI systems with a focus on humans, with the ability to learn radiologists' search habits via their eye movements, with the hope of enhancing DL syst

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Hamid, Dalia Ebrahim, Hanan M. Amer, Hossam El-Din Salah Moustafa, and Hanaa Salem Marie. "Empowering health data protection: machine learning-enabled diabetes classification in a secure cloud-based IoT framework." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 2 (2024): 1110. http://dx.doi.org/10.11591/ijeecs.v34.i2.pp1110-1121.

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Smart medical devices and the internet of things (IoT) have enhanced healthcare systems by allowing remote monitoring of patient's health. Because of the unexpected increase in the number of diabetes patients, it is critical to regularly evaluate patients' health conditions before any significant illness occurs. As a result of transmitting a large volume of sensitive medical data, dealing with IoT data security issues remains a difficult challenge. This paper presents a secure remote diabetes monitoring (SR-DM) model that uses hybrid encryption, combining the advanced encryption standard and e

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Hamid, Dalia Ebrahim, Hanan M. Amer, Hossam El-Din Salah Moustafa, and Hanaa Salem Marie. "Empowering health data protection: machine learning-enabled diabetes classification in a secure cloud-based IoT framework." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 2 (2024): 1110–21. https://doi.org/10.11591/ijeecs.v34.i2.pp1110-1121.

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Smart medical devices and the internet of things (IoT) have enhanced healthcare systems by allowing remote monitoring of patient's health. Because of the unexpected increase in the number of diabetes patients, it is critical to regularly evaluate patients' health conditions before any significant illness occurs. As a result of transmitting a large volume of sensitive medical data, dealing with IoT data security issues remains a difficult challenge. This paper presents a secure remote diabetes monitoring (SR-DM) model that uses hybrid encryption, combining the advanced encryption standard and e

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Khan, Muhammad Hassan, and Marcin Grzegorzek. "Vojta-Therapy." International Journal of Software Innovation 5, no. 3 (2017): 18–32. http://dx.doi.org/10.4018/ijsi.2017070102.

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This paper proposed a novel computer vision-based framework to recognize the accurate movements of a patient during the Vojta-therapy. Vojta-therapy is a useful technique for the physical and mental impairments in humans. During the therapy, a specific stimulation is given to the patients to cause the patient's body to perform certain reflexive pattern movements. The repetition of this stimulation ultimately makes available the previously blocked connections between the spinal cord and brain, and after a few sessions, patients can perform these movements without any external stimulation. In th

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Fabisiak, Luiza, and Karina Szczypor-Piasecka. "Diagnostic Analysis of Patients Qualified for Hip Replacement Using Multi-Criteria Methods." International Journal of Healthcare Information Systems and Informatics 15, no. 4 (2020): 56–69. http://dx.doi.org/10.4018/ijhisi.2020100104.

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Patients with advanced hip osteoarthritis are likely to suffer from biomechanical disorders. As many criteria inform how such patients are being qualified for alloplasty procedures, this article proposes a multi-criteria decisional framework in qualifying patients for treatment while undergoing diagnostic analysis for hip replacement surgery. In order to assess the patient's health condition, the competence of physicians and physiotherapists must first be checked. After creating the expert preference model and decision tree, the AHP method was applied followed by the Electre Tri method in the

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Andrade-Arenas, Laberiano, Inoc Rubio-Paucar, Domingo Hernández Celis, and Cesar Yactayo-Arias. "Expert systems in mental health: innovative approach for personalized treatment." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 1 (2024): 414. http://dx.doi.org/10.11591/ijeecs.v36.i1.pp414-427.

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Custom classification of mental illnesses has emerged as a challenge for mental health specialists, often minimized by patients' lack of awareness of symptoms and the importance of early intervention. Therefore, the purpose of this research is to provide a comprehensive understanding of personalized treatment, encompassing both pharmacological and non-pharmacological options, specifically tailored to mental disorders, considering factors such as the patient's age and gender, among other relevant characteristics. In this context, the Buchanan methodology has been chosen as the framework for str

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Laberiano, Andrade-Arenas Inoc Rubio-Paucar Domingo Hernández Celis Cesar Yactayo-Arias. "Expert systems in mental health: innovative approach for personalized treatment." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 1 (2024): 414–27. https://doi.org/10.11591/ijeecs.v36.i1.pp414-427.

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Custom classification of mental illnesses has emerged as a challenge for mental health specialists, often minimized by patients' lack of awareness of symptoms and the importance of early intervention. Therefore, the purpose of this research is to provide a comprehensive understanding of personalized treatment, encompassing both pharmacological and non-pharmacological options, specifically tailored to mental disorders, considering factors such as the patient's age and gender, among other relevant characteristics. In this context, the Buchanan methodology has been chosen as the framework for str

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S. P. Rathinaeaswari and V. Santhi. "A New Efficient and Privacy-Preserving Hybrid Classification model for Patient-Centric Clinical Decision Support System." Journal of Advanced Research in Applied Sciences and Engineering Technology 33, no. 1 (2023): 299–316. http://dx.doi.org/10.37934/araset.33.1.299316.

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A lot of focus has recently been placed on clinical decision support systems, that use advanced data mining methods to assist clinicians in making wise decisions. Along with increasing diagnosis accuracy, clinical decision support systems (CDSS) have the added benefit of speeding up diagnosis. Data security is crucial in this system. In this research, we offer EPPCD (Efficient and Privacy preserving Patient-centric Clinical Decision) support system to assist physicians in predicting illness risks of patients in a privacy-preserving manner. This system is proposed to solve the privacy difficult

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Patel, Alpesh A., Andrew Dailey, Darrel S. Brodke, et al. "Thoracolumbar spine trauma classification: the Thoracolumbar Injury Classification and Severity Score system and case examples." Journal of Neurosurgery: Spine 10, no. 3 (2009): 201–6. http://dx.doi.org/10.3171/2008.12.spine08388.

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Object The aim of this study was to review the Thoracolumbar Injury Classification and Severity Score (TLICS) and to demonstrate its application through a series of spine trauma cases. Methods The Spine Trauma Study Group collaborated to create and report the TLICS system. The TLICS system is reviewed and applied to 3 cases of thoracolumbar spine trauma. Results The TLICS system identifies 3 major injury characteristics to describe thoracolumbar spine injuries: injury morphology, posterior ligamentous complex integrity, and neurological status. In addition, minor injury characteristics such as

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Barros, Márcia Izabeli Guimarães, Iully Tiyoko Rebelatto Suguiura, Alessandra Linzmeyer, and Alberito Rodrigo de Carvalho. "ASSOCIATION BETWEEN TWO CLASSIFICATION MODELS OF CHRONIC PAINFUL LOW BACK DISORDERS, "BIOMEDICAL" AND "BIOPSYCHOSOCIAL"." Varia Scientia - Ciências da Saúde 9, no. 1 (2023): 57–63. http://dx.doi.org/10.48075/vscs.v9i1.31655.

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Introduction: Chronic low back pain (CLBP) represents a complex condition. Although, by the biomedical model, biological signals and symptoms use to be the guide to both the diagnostic and prognostic, those does not correspond to the CLBP biopsychosocial etiology by ignoring the patient's beliefs and convictions about their pain. Objective: To examine the association between CLBP classification based on the biomedical model and the biopsychosocial model. Materials and methods: A retrospective cross sectional observational study. The study analyzed physiotherapeutic screening records obtained f

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Muzafar Ali Bhand, Sanaullaha Pathan, Hameedullah Khan, et al. "Modified Nakaguchi Classification: Is it a New Way to Measure Outcome of Chronic Subrural Hematoma through GCOSE?" Pakistan Journal Of Neurological Surgery 28, no. 4 (2024): 468–75. https://doi.org/10.36552/pjns.v28i4.1052.

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Objective: The ? value of the Modified Nakaguchi classification is 0.78, the highest from other available classification systems for CSDH. The main objective of this study was to evaluate the outcome of CSDH concerning the type of CSDH according to the modified Nakaguchi classification. Material and Methods: It is a single cohort study conducted at Liaquat University Hospital Hyderabad and Jamshoro in the Neurosurgical Department. The patient's known case of chronic subdural hematoma was included in the study after evaluation of inclusion and exclusion criteria and variables like type of CSDH

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Alsabah, Mohammed Sameer, Nibras Mahmood Ahmed Aljarah, and Sever Viorel Paşca. "Intelligent Algorithmic Approaches to ECG Signal Classification in Heart Disease Detection." Electrical Engineering Technical Journal 2, no. 1 (2025): 25–32. https://doi.org/10.51173/eetj.v2i1.19.

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Electrocardiography (ECG) is one of the most important non-invasive tools for detecting electrical cardiac signals. The Heart signals consider a thorough Investigation of the heart & allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) can employ electrodes with measurement of the electrical movement of the heart. Extracting ECG signs will be non-invasive control veer off opens the entryway on the world of inventive up and about preparing What's more perceptions dissection systems in the analysis a heart malady. With the help of today’s extensive database for E

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Chen, Xiang, Ming Cao, Hua Wei, Zhongan Shang, and Linghao Zhang. "Patient Emotion Recognition in Human Computer Interaction System Based on Machine Learning Method and Interactive Design Theory." Journal of Medical Imaging and Health Informatics 11, no. 2 (2021): 307–12. http://dx.doi.org/10.1166/jmihi.2021.3293.

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There are more and more human computer interaction systems (HCIS) in the medical field. Improving the service quality of HCIS and making them more intelligent is an inevitable trend in the future. Emotion recognition is of great significance for patients using HCIS. Some excellent HCIS not only satisfies the needs of patients, but also judges the emotional state of patients based on the results of emotional recognition, thereby providing more intimate medical services. Therefore, emotion recognition is crucial for HCIS. To effectively optimize the correct rate of emotion recognition, a novel e

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Kandaya, Shaarmila, Abdul Rahim Abdullah, Norhashimah Mohd Saad, Ahmad Sobri Muda, and Muhammad Izzat Ahmad Sabri. "Segmentation and classification techniques used to detect early stroke diagnosis using brain magnetic resonance imaging: a review." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 648. http://dx.doi.org/10.11591/ijai.v13.i1.pp648-657.

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<p>Stroke is a leading cause of disability and death worldwide. Early diagnosis and treatment are crucial in reducing the risk of stroke-related complications. Brain magnetic resonance imaging (MRI) is a common diagnostic tool used for stroke evaluation. However, manual interpretation of MRI images can be time-consuming and subjective. Machine learning (ML) algorithms have shown promise in automating and improving stroke diagnosis accuracy. This article focuses on classification and segmentation techniques used to detect early stroke diagnosis using brain magnetic imaging. The diagnosis,

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Sahu, Barnali, Siddharth Saurabh, and Tripti Swarnakar. "Covid - 19 Patient's CT Images classification: StackAlexNet-19 A Deep Learning Approach." International Journal of Computing and Digital Systems 12, no. 1 (2022): 1454–64. http://dx.doi.org/10.12785/ijcds/1201117.

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Mishra, Ashwani, and Sanjeev Gangwar. "Lung Cancer Detection and Classification using Machine Learning Algorithms." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 6s (2023): 277–82. http://dx.doi.org/10.17762/ijritcc.v11i6s.6920.

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Lung cancer is a clump of cells in the lung that are multiplying uncontrollably and improperly. Lung cancer is the deadliest disease, and its cure should be the primary focus of all scientific research. Although it cannot be prevented, we can lessen the danger. Thus, a patient's chance of life depends on the early identification of lung cancer. Several machine learning methods, such as Support Vector Machine, Logistic Regression, Artificial Neural Networks, and Naive Bayes, have been used for the investigation and prognosis of lung cancer. In this paper, Lung cancer prediction is finished by g

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Shaikh, Imran, and Kadam V.K. "Automatic Computer Propped Diagnosis Framework of Liver Cancer Detection with Simulation using CNN LSTM." International Journal of Engineering Research in Electrical and Electronics Engineering 9, no. 1 (2022): 1–7. http://dx.doi.org/10.36647/ijereee/09.01.a001.

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Initial prediction of any kind of cancer is always advantageous for on-time medical treatment to save the patient's life. The Computer-Aided Diagnosis (CAD) tools using signal processing & image processing methods gained significant attention for immediate & accurate diagnosis using patient’s raw medical data like Magnetic Resonance Imaging (MRI), Chromatography (CT), etc. The liver cancer early detection & analysis of its grading is an important research problem. In this research, we proposed the two models semi-automatic & automatic frameworks for liver disease classification

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Tunkiwala, Ali, Udatta Kher, and Nupur H. Vaidya. "“ABCD” Implant Classification: A Comprehensive Philosophy for Treatment Planning in Completely Edentulous Arches." Journal of Oral Implantology 46, no. 2 (2020): 93–99. http://dx.doi.org/10.1563/aaid-joi-d-19-00147.

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A thorough and precise treatment plan that considers various factors such as age, availability of bone, interarch space for prosthesis design, smile line, lip support, patient desires, and economics is a necessity before implant surgery. Many previous classification systems for treatment planning in edentulous situations tend to focus on only a certain parameter such as esthetics, or available bone volume, or are specifically designed for the maxilla or mandible. The authors have proposed a simplified and universal ABCD classification that uses the 4 vital parameters of age, bone volume, cosme

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Songara, Ankit, Pankaj Dhiman, Vipul Sharma, and Karan Kumar. "A Supervised Learning-Based Framework for Predicting COVID-19 in Patients." International Journal of Distributed Systems and Technologies 14, no. 1 (2023): 1–12. http://dx.doi.org/10.4018/ijdst.317412.

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The integration of ML and loT can provide insightful details for critical decision making, automated responses, etc. Predicting future trends and detecting anomalies are some of the areas where loT and ML are being used at a rapid rate. Machine learning can help decode the hidden patterns in IoT data. It may complement or replace manual processes in critical areas with automated systems that use statistically derived behavior. In healthcare, wearable sensors used for tracking patient activity have been continuously producing a staggering amount of data. This paper proposes an IoT-based scalabl

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Sulaeman, Gilang, Yohani Setiya Rafika Nur Nur, Adanti Wido Paramadini, Dasril Aldo, and M. Yoka Fathoni. "Optimization Of Extreme Learning Machine Models Using Metaheuristic Approaches For Diabetes Classification." Jurnal Teknik Informatika (Jutif) 6, no. 3 (2025): 1503–16. https://doi.org/10.52436/1.jutif.2025.6.3.4690.

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Proper classification of diabetes is a significant challenge in contemporary healthcare, especially related to early detection and clinical decision support systems. This study aims to optimize the Extreme Learning Machine (ELM) model with a metaheuristic approach to improve performance in diabetes classification. The data used was an open dataset containing the patient's medical attributes, such as age, gender, smoking status, body mass index, blood glucose level, and HbA1c. The initial process includes data cleansing, one-hot coding for categorical features, MinMax normalization, and unbalan

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Prathima, Y., and Manish Saxena Dr. "Construction of model for the classification of chronic disease and particularly heart." International Journal of Advance Research in Multidisciplinary 1, no. 1 (2023): 496–502. https://doi.org/10.5281/zenodo.12820822.

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Medical services provide gigantic information on every day ground having diverse structures like printed, images, numbers pool and so forth. However, there is absence of devices accessible in healthcare to process this data. Data mining frame works are utilized to extricate information from this data which can be utilized by media proficient individual to figure future procedures. Heart illness is the primary driver of death in the masses. Early recognizing and hazard expectations are essential for patient's medicines and specialists’ analysis. Data mining has found success in highly vis

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Toth, Federico. "Integration vs separation in the provision of health care: 24 OECD countries compared." Health Economics, Policy and Law 15, no. 2 (2018): 160–72. http://dx.doi.org/10.1017/s1744133118000476.

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AbstractThis article proposes a classification of the different national health care systems based on the way the network of health care providers is organised. To this end, we present two rivalling models: on the one hand, the integrated model and, on the other, the separated model. These two models are defined based on five dimensions: (1) integration of insurer and provider; (2) integration of primary and secondary care; (3) presence of gatekeeping mechanisms; (4) patient's freedom of choice; and (5) solo or group practice of general practitioners. Each of these dimensions is applied to the

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Retnaningsih, Retnaningsih, Hairu Zaman, Aris Catur Bintoro, and Sofro Muchlis Achsan Udji. "THE RELATIONSHIP TETANUS SEVERITY WITH OUTCOME CLINIC." Jurnal Kedokteran Diponegoro (Diponegoro Medical Journal) 12, no. 3 (2023): 112–18. http://dx.doi.org/10.14710/dmj.v12i3.37672.

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Introduction : Tetanus is an acute toxemia caused by a neurotoxin produced by C. tetani characterized by periodic and severe muscle stiffness and spasms. Prognostic assessment is the most important thing to see the risk of mortality. Score systems are often used by clinicians such as Phillips score (1967), Ablett classification (1967), Dakar score (1975).20 Methods: This study was an analytic study with a cross-sectional design in a population of tetanus patients treated from January 2019 to December 2021. The inclusion criteria in this study were patients who had complete medical records. Inf

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MD Ali, Mohd Adli, Edre Mohammad Aidid, and HAFIDZUL ABDULLAH. "RESPECTING PATIENT PRIVACY WITH FEDERATED ARTIFICIAL INTELLIGENCE." Journal of Information Systems and Digital Technologies 3, no. 2 (2021): 84–93. http://dx.doi.org/10.31436/jisdt.v3i2.220.

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Multiple research has shown that deep artificial neural networks (ANN) can assist physicians in diagnosing a patient with greater accuracy and sensitivity. ANN applications are not limited to just the classification of diseases but also include image segmentation, tumor localization, and mortality rate predictions. Nonetheless, the great march of success by ANN is only possible by the availability of an open medical dataset. Only with such open datasets can developers build, train and test ANN models to obtain higher accuracy. However, there is yet an open medical dataset in Malaysia that can

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Cvetko, Tim, and Tinkara Robek. "Deep Learning Analysis for Estimating Sleep Syndrome Detection Utilizing the Twin Convolutional Model FTC2." BOHR International Journal of Future Robotics and Artificial Intelligence 1, no. 1 (2021): 11–17. http://dx.doi.org/10.54646/bijfrai.002.

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Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient's neurophysiological signals collected at sleep labs. This is a difficult, tedious and a time-consuming task. The limitations of manual sleep stage scor- ing have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diag- nosis and treatment of related sleep disorders. In this paper, we propose a novel method and a practical ap

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Souza, Melwin D. "Intelligent Health Surveillance: Cognitive IoT Enhanced by Rapid Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 2393–99. http://dx.doi.org/10.22214/ijraset.2024.63492.

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Abstract: The quality of life for diabetic patients is significantly improved through continuous monitoring. Integrating various technologies such as the Internet of Things (IoT), embedded software, communication technologies, artificial intelligence, and smart devices helps reduce the financial burden on the healthcare system. Advances in communication technologies have facilitated personalized and remote healthcare. To meet the growing demand for advanced e-health applications, developing intelligent healthcare systems and increasing the number of applications connected to the network is ess

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Mrs. Deepali K. Jadhav, Et al. "Brain Tumor Classification, Segmentation, and Detection using Deep Learning - A Review." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 585–95. http://dx.doi.org/10.17762/ijritcc.v11i9.8848.

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V.Vapnik in 1965 proposed Vector methods. Kimeldorf presented a technique for creating kernel space based on support vectors in 1971. Support Vector Machine (SVM) techniques were initially presented in the 1990s by V. Vapnik in the field of statistical learning. Since then, pattern recognition, natural language processing, image processing and other areas have seen extensive use of SVM. By converting non-linear sample space into linear space via a kernel approach, the algorithm's complexity is reduced. Image classification is a well-known issue in image processing. Predicting the input image c

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Mustapha, Ishamuddin, Nohman Khan, Muhammad Imran Qureshi, Ahmad Ali Harasis, and Nguyen Thuy Van. "Impact of Industry 4.0 on Healthcare: A Systematic Literature Review (SLR) from the Last Decade." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 18 (2021): 116. http://dx.doi.org/10.3991/ijim.v15i18.25531.

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<p class="0abstract">This study aims to analyze the impact of industry 4.0 on the healthcare system. Scopus database is used to extract the data, and 297 articles are initially found in the database. We used the PRISMA statement 2015 to include and exclude data; the final 48 articles were included for the review. The author's keyword and VOS viewer co-occurrence of the terms are classifying the same result. The records are presented in three major categories, and outcomes are discussed accordingly in the classification of literature. Healthcare systems, cloud computing digital technologi

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Brunzini, Agnese, Marta Rossi, Marco Mandolini, Federica Cappelletti, and Michele Germani. "DEVELOPMENT OF A CLASSIFIER AND A SIMULATOR TO SUPPORT THE DESIGN OF AN ANTI-DECUBITUS ACTIVE MATTRESS." Proceedings of the Design Society 3 (June 19, 2023): 1445–54. http://dx.doi.org/10.1017/pds.2023.145.

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AbstractApproximately 10% of hospitalized patients develops decubitus ulcers that quickly degenerates into chronic illness that reduces the quality of life and requires expensive clinical management. The use of an anti-decubitus active mattress, that automatically redistributes the pressure loads, reduces the occurrence of new lesions and promotes the healing of the pre-existing ones.The aim of this work is to design and develop two tools to support the design of an anti-decubitus active mattress. Almost all the systems found in literature are based on the classification of pressure maps throu

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Grauvogel, Juergen, Jan Kaminsky, and Steffen K. Rosahl. "The Impact of Tinnitus and Vertigo on Patient-Perceived Quality of Life After Cerebellopontine Angle Surgery." Neurosurgery 67, no. 3 (2010): 601–10. http://dx.doi.org/10.1227/01.neu.0000374725.19259.ea.

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Abstract BACKGROUND Quality of life (QOL) has come into focus after treatment for cerebellopontine angle (CPA) lesions. OBJECTIVE This study compared subjective (tinnitus, vertigo) and objective (hearing loss, facial palsy) results of CPA surgery with patient-perceived impairment of QOL. METHODS A retrospective analysis of a consecutive series of 48 patients operated on for either a vestibular schwannoma or a meningioma in the CPA was performed. Patient's subjective impairment of QOL by tinnitus, vertigo, hearing loss, and facial nerve palsy was assessed by a visual analog scale (VAS). Objecti

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Gordienko, Daniel V., and Artem O. Kravchenko. "Electrocardiography signal processing method for effective assessment of a patient's heart rate using a convolutional neural network." Digital Diagnostics 5, no. 1S (2024): 109–11. http://dx.doi.org/10.17816/dd627084.

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BACKGROUND: The initial step in annotating an electrocardiogram is the evaluation of the patient's heart rhythm. In the presented study, a method has been developed to process the electrocardiographic signal and estimate the heart rhythm. The method is based on the application of a trained convolutional neural network, which will provide the physician with preliminary information about possible atrial fibrillation or the presence of other rhythm disturbances as soon as possible after receiving the electrocardiogram. Furthermore, such methodologies can be incorporated into telemedicine systems,

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Zaitin, Yerlan, Madina Mansurova, Murat Kunelbayev, Gulnur Tyulepberdinova, Talshyn Sarsembayeva, and Adai Shomanov. "Development of a patient health monitoring system based on the internet of things with a module for predicting vital signs." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 1 (2024): 518. http://dx.doi.org/10.11591/ijeecs.v33.i1.pp518-529.

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<span>Recent issues related to human health in the world have shown the importance of telemedicine considering necessities to perform the remote monitoring of patients. In this study, using a patient smart monitoring system (PSMS), we collected 5,000 samples of heart rate and blood saturation vital signs from 4 volunteers and tried to find better correlation algorithms to develop a module to predict what these vital signs will be in the next 60 seconds. The following regression algorithms recurrent neural network (long short-term memory) (RNN(LSTM)), autorregresive integrated moving aver

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Abbood, Alaa Ahmed, Qahtan Makki Shallal, and Mohammed Abdulraheem Fadhel. "Automated brain tumor classification using various deep learning models: a comparative study." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (2021): 252. http://dx.doi.org/10.11591/ijeecs.v22.i1.pp252-259.

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The brain tumor, the most common and aggressive disease, leads to a very shorter lifespan. Thus, planning treatments is a crucial step in improving a patient's quality of life. In general, several image techniques such as CT, MRI, and ultrasound have been used for assessing tumors in the prostate, breast, lung, brain, etc. Primarily, MRI images are applied to detect tumors in the brain during this work. The enormous amount of data produced by the MRI scan thwarts tumor vs. non-tumor manual classification at a particular time. Unfortunately, with a small number of images, it has certain limitat

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Geetha, G., and K. Mohana Prasad. "An Hybrid Ensemble Machine Learning Approach to Predict Type 2 Diabetes Mellitus." Webology 18, Special Issue 02 (2021): 311–31. http://dx.doi.org/10.14704/web/v18si02/web18074.

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Diabetic Mellitus is one of the chronic diseases that affect many people around the globe. The severity of the disease and risk can be greatly reduced if it is predicted in the early stage. The main objective of the proposed model (T2DDP) is to predict type 2 diabetes mellitus and alert the patients well in advance to reduce the risk factor and severity associated with diabetes diseases. We have used supervised classification algorithms such as Naïve Bayes and ensemble algorithms like bagging with random forest and Adaboost for decision tree. The ensemble algorithm is mainly used to improve th

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Fabian, Ido Didi, Vishal Shah, Noa Kapelushnik, et al. "Examinations under anaesthesia as a measure of disease burden in unilateral retinoblastoma: the London experience." British Journal of Ophthalmology 104, no. 1 (2019): 17–22. http://dx.doi.org/10.1136/bjophthalmol-2018-313556.

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BackgroundEarly diagnosis strategies and advances in retinoblastoma (Rb) management have resulted in nearly 100% survival. More attention should, therefore, be given to quality of life considerations. We aimed to quantify the number of examinations under anaesthesia (EUAs) in a cohort of patients with Rb, as a measure of disease burden.MethodsA retrospective analysis of patients with unilateral Rb that presented to the London Rb service from 2006 to 2013, were treated and had long-term follow-up. Correlations of clinical variables to number of EUAs were investigated.ResultsA total of 107 patie

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Anusha Ampavathi. "A Novel Approach of Cloud Computing Network for Authentication and Security Enhancement of IoT Enabled Cancer Forecasting System." Journal of Electrical Systems 20, no. 7s (2024): 518–34. http://dx.doi.org/10.52783/jes.3357.

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Recently, a variety of approaches have been employed to address a broad spectrum of real-world issues; these methodology cover a variety of areas, including healthcare systems. Previous researchers concentrated on health-care monitoring software. It had several shortcomings, such as poor health-care data storage, time, expense, and processing complexity. This paper proposes a unique IoT-enabled and secured clinical monitoring paradigm to address these issues. Initially, implant several sensors to gather information on vital indicators like body temperature fluctuation. Phone numbers, marital s

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Tatarkanov, Aslan A., Аlexander А. Umyskov, Ruslan Kh Tekeev, and Vladimir Zh Kuklin. "Model Development of Universal Hardware and Software Module for Medical Information System." International Journal of Emerging Technology and Advanced Engineering 12, no. 10 (2022): 136–46. http://dx.doi.org/10.46338/ijetae1022_15.

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Medical information systems (MIS) are the object of the research presented. They allow information processing throughout the patient's interaction with medical services - from contacting such institutions to monitoring the patient after rehabilitation. However, for various reasons, during the information process exchange within the framework of MIS, distortions of collected or transmitted data are possible, which leads to wrong medical decisions. Currently, new approaches are being implemented using Software-Defined Networks, but our work proposes an alternative solution designed to work conse

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Sachs, Karen, Garry P. Nolan, Wendy J. Fantl, Erin F. Simonds, Sean C. Bendall, and Kara L. Davis. "Network-Based Discovery of Prognostic Markers in Pediatric AML by Multi-Dimensional Single Cell Mass Cytometry." Blood 120, no. 21 (2012): 1411. http://dx.doi.org/10.1182/blood.v120.21.1411.1411.

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Abstract Abstract 1411 Background: Currently available indicators of risk stratification for acute myeloid leukemia (AML) approximate a patient's true prognosis (Rubnitz, J.E., et al., Lancet Oncol, 2010). While signaling networks are central to biological systems they do not lend themselves to easy characterization. As such, network characteristics are not incorporated into risk stratification. There exists a need for computational techniques that enable elucidation of a patient's global signaling state. Flow cytometry is an essential tool for classifying AML surface markers by surface marker

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ADAMS, ANN, CHRISTOPHER D. BUCKINGHAM, SARA ARBER, JOHN B. MCKINLAY, LISA MARCEAU, and CAROL LINK. "The influence of patient's age on clinical decision-making about coronary heart disease in the USA and the UK." Ageing and Society 26, no. 2 (2006): 303–21. http://dx.doi.org/10.1017/s0144686x05004265.

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This paper examines UK and US primary care doctors' decision-making about older (aged 75 years) and midlife (aged 55 years) patients presenting with coronary heart disease (CHD). Using an analytic approach based on conceptualising clinical decision-making as a classification process, it explores the ways in which doctors' cognitive processes contribute to ageism in health-care at three key decision points during consultations. In each country, 56 randomly selected doctors were shown videotaped vignettes of actors portraying patients with CHD. The patients' ages (55 or 75 years), gender, ethnic

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Tim, Cvetko, and Robek Tinkara. "Deep Learning Analysis for Estimating Sleep Syndrome Detection Utilizing the Twin Convolutional Model FTC2." BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning 1, no. 1 (2021): 14–19. http://dx.doi.org/10.54646/bijiam.003.

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Manual sleep stage scoring is frequently performed by sleep specialists by visually evaluating the patient's neurophysiological signals acquired in sleep laboratories. This is a difficult, time-consuming, laborious process. Because of the limits of human sleep stage scoring, there is a greater need for creating Automatic Sleep Stage Classification (ASSC) systems. Sleep stage categorization is the process of distinguishing the distinct stages of sleep is an important step in assisting physicians in the diagnosis treatment of associated sleep disorders. In this research, we offer a unique method

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Dadras, Soheil S. "Molecular Diagnostics in Melanoma: Current Status and Perspectives." Archives of Pathology & Laboratory Medicine 135, no. 7 (2011): 860–69. http://dx.doi.org/10.5858/2009-0623-rar1.1.

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Abstract Context.—In the current “molecular” era, the advent of technology, such as array-based platforms, systems biology, and genome-wide approaches, has made it possible to examine human cancers, including melanoma, for genetic mutations, deletions, amplification, differentially regulated genes, and epigenetic changes. Advancement in current technologies is such that one can now examine ribonucleic acid (RNA), deoxyribonucleic acid (DNA), and protein directly from the patient's own tumor. Objective.—To apply these new technologies in advancing molecular diagnostics in melanoma has historica

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