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Exploiting biochemical data to improve osteosarcoma diagnosis with deep learning
AbstractEarly and accurate diagnosis of osteosarcomas (OS) is of great clinical significance, and machine learning (ML) based methods are increasingly adopted. However, current ML-based methods for osteosarcoma diagnosis consider only X-ray images, usually fail to generalize to new cases, and lack explainability. In this paper, we seek to explore the capability of deep learning models in diagnosing primary OS, with higher accuracy, explainability, and generality. Concretely, we analyze the added value of integrating the biochemical data, i.e., alkaline phosphatase (ALP) and lactate dehydrogenase (LDH), and design a model t...
Source: Health Information Science and Systems - April 18, 2024 Category: Information Technology Source Type: research

A review of machine learning-based methods for predicting drug –target interactions
In conclusion, we address current challenges and outline potential future directions in this research field. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - April 12, 2024 Category: Information Technology Source Type: research

Characterization of biliary and duodenal microbiota in patients with primary and recurrent choledocholithiasis
ConclusionsThe duodenal microbiota was in an imbalance in CDL. The duodenal microbiota was probably the main source of the biliary microbiota and was closely related to CDL formation and recurrence.Enterococcus,Fusobacterium,Escherichia andKlebsiella might contribute to CDL recurrence.Clinical trialsThe study was registered at the Chinese Clinical Trial Registry (https://www.chictr.org.cn/index.html,  ChiCTR2000033940). (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - April 4, 2024 Category: Information Technology Source Type: research

Image-based second opinion for blood typing
AbstractThis paper considers a new method for providing a recommendation (second opinion) for a laboratory assistant in manual blood typing based on serological plates. The manual method consists of two steps: preparation and analysis. During preparation step the laboratory assistant needs to fill each well of a plate with a blood sample and a reagent mixture according to methodological guidelines. In the second step it is necessary to visually determine the result of the reactions, named agglutination. Despite the popularity of this method, it is slow and highly influenced by human factor, which cause blood typing errors....
Source: Health Information Science and Systems - April 2, 2024 Category: Information Technology Source Type: research

A drug prescription recommendation system based on novel DIAKID ontology and extensive semantic rules
AbstractAccording to the World Health Organization (WHO) data from 2000 to 2019, the number of people living with Diabetes Mellitus and Chronic Kidney Disease (CKD) is increasing rapidly. It is observed that Diabetes Mellitus increased by 70% and ranked in the top 10 among all causes of death, while the rate of those who died from CKD increased by 63% and rose from the 13th place to the 10th place. In this work, we combined the drug dose prediction model, drug-drug interaction warnings and drugs that potassium raising (K-raising) warnings to create a novel and effective ontology-based assistive prescription recommendation ...
Source: Health Information Science and Systems - March 23, 2024 Category: Information Technology Source Type: research

Alterations of DNA methylation profile in peripheral blood of children with simple obesity
ConclusionsAbnormal DNA methylation is closely related to childhood simple obesity. The altered methylation of CpG-cg05831083 and CpG-cg14926485 could potentially serve as biomarkers for childhood simple obesity. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - March 18, 2024 Category: Information Technology Source Type: research

Dynamically stabilized recurrent neural network optimized with Artificial Gorilla Troops espoused Alzheimer ’s disorder detection using EEG signals
AbstractAlzheimer ’s disease is an incurable neurological disorder that damages cognitive abilities, but early identification reduces the symptoms significantly. The absence of competent healthcare professionals has made automatic identification of Alzheimer’s disease more crucial since it lessens the amount of w ork for staff members and improves diagnostic outcomes. The major aim of this work is “to develop a computer diagnostic scheme that makes it possible to identify AD using the Electroencephalogram (EEG) signal”. Therefore, Dynamically Stabilized Recurrent Neural Network Optimized with Artificial Gorilla Tro...
Source: Health Information Science and Systems - March 15, 2024 Category: Information Technology Source Type: research

Gpmb-yolo: a lightweight model for efficient blood cell detection in medical imaging
This study serves as a valuable reference for the efficient detection of blood cells in medical images. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - March 11, 2024 Category: Information Technology Source Type: research

Artificial intelligence-based framework to identify the abnormalities in the COVID-19 disease and other common respiratory diseases from digital stethoscope data using deep CNN
AbstractThe utilization of lung sounds to diagnose lung diseases using respiratory sound features has significantly increased in the past few years. The Digital Stethoscope data has been examined extensively by medical researchers and technical scientists to diagnose the symptoms of respiratory diseases. Artificial intelligence-based approaches are applied in the real universe to distinguish respiratory disease signs from human pulmonary auscultation sounds. The Deep CNN model is implemented with combined multi-feature channels (Modified MFCC, Log Mel, and Soft Mel) to obtain the sound parameters from lung-based Digital St...
Source: Health Information Science and Systems - March 9, 2024 Category: Information Technology Source Type: research

Optimised deep k-nearest neighbour ’s based diabetic retinopathy diagnosis(ODeep-NN) using retinal images
AbstractDiabetes mellitus has been regarded as one of the prime health issues in present days, which can often lead to diabetic retinopathy, a complication of the disease that affects the eyes, causing loss of vision. For precisely detecting the condition ’s existence, clinicians are required to recognise the presence of lesions in colour fundus images, making it an arduous and time-consuming task. To deal with this problem, a lot of work has been undertaken to develop deep learning-based computer-aided diagnosis systems that assist clinicians in m aking accurate diagnoses of the diseases in medical images. Contrariwise,...
Source: Health Information Science and Systems - March 9, 2024 Category: Information Technology Source Type: research

Analyzing and identifying predictable time range for stress prediction based on chaos theory and deep learning
ConclusionIntegrating deep learning and chaos theory for stress prediction is effective, and can improve the prediction accuracy over 2% and 8% more than those of the deep learning and the Chaos method respectively. Implications and further possible improvements are also discussed at the end of the paper. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - March 6, 2024 Category: Information Technology Source Type: research

Autism spectrum disorder detection with kNN imputer and machine learning classifiers via questionnaire mode of screening
AbstractAutism spectrum disorder (ASD) is a neurodevelopmental disorder. ASD cannot be fully cured, but early-stage diagnosis followed by therapies and rehabilitation helps an autistic person to live a quality life. Clinical diagnosis of ASD symptoms via questionnaire and screening tests such as Autism Spectrum Quotient-10 (AQ-10) and Quantitative Check-list for Autism in Toddlers (Q-chat) are expensive, inaccessible, and time-consuming processes. Machine learning (ML) techniques are beneficial to predict ASD easily at the initial stage of diagnosis. The main aim of this work is to classify ASD and typical developed (TD) c...
Source: Health Information Science and Systems - March 6, 2024 Category: Information Technology Source Type: research

Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing
ConclusionOur research demonstrated the potential of using AI, particularly DL models, in enhancing the accuracy of ASD detection and diagnosis. This innovative approach signifies the critical role AI can play in advancing early diagnostic techniques, enabling better patient outcomes and underlining the importance of early identification of ASD, especially in children. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - March 6, 2024 Category: Information Technology Source Type: research

Identification of cancer driver genes based on hierarchical weak consensus model
AbstractCancer is a complex gene mutation disease that derives from the accumulation of mutations during somatic cell evolution. With the advent of high-throughput technology, a large amount of omics data has been generated, and how to find cancer-related driver genes from a large number of omics data is a challenge. In the early stage, the researchers developed many frequency-based driver genes identification methods, but they could not identify driver genes with low mutation rates well. Afterwards, researchers developed network-based methods by fusing multi-omics data, but they rarely considered the connection among feat...
Source: Health Information Science and Systems - March 6, 2024 Category: Information Technology Source Type: research

Efficient management of pulmonary embolism diagnosis using a two-step interconnected machine learning model based on electronic health records data
AbstractPulmonary Embolism (PE) is a life-threatening clinical disease with no specific clinical symptoms and Computed Tomography Angiography (CTA) is used for diagnosis. Clinical decision support scoring systems like Wells and rGeneva based on PE risk factors have been developed to estimate the pre-test probability but are underused, leading to continuous overuse of CTA imaging. This diagnostic study aimed to propose a novel approach for efficient management of PE diagnosis using a two-step interconnected machine learning framework directly by analyzing patients' Electronic Health Records data. First, we performed feature...
Source: Health Information Science and Systems - March 6, 2024 Category: Information Technology Source Type: research