Hasil untuk "Diseases of the musculoskeletal system"

Md Nurul Anwar, Michael Pan, Ashleigh V. Morrice-West et al.

Musculoskeletal injuries (MSI) in Thoroughbred racehorses are a leading cause of death and premature retirement in racehorses and are heavily influenced by training practices. Greater distances of high-speed galloping accumulated during racing campaigns are associated with MSI. Bone injury is the most common MSI, and understanding how training practices influence bone damage accumulation is critical for improving both horse welfare and racing outcomes. This study builds on an existing mathematical model of bone adaptation and damage to investigate the impact of different training programs on bone injury risk. Several training programs (three progressive, four race-fit, six rest programs and two with rest replaced by low-intensity training) were constructed to reflect representative practices undertaken by professional trainers in Victoria, Australia. Training programs varied in training volume, rest frequency and program duration. Lower volume training programs that included high-speed training, achieved sufficient bone adaptation with less accumulation of bone damage, and subsequently lower risk of bone failure. In addition, incorporating more frequent rests (at least 2 per year) and/or longer rest periods (at least 6 weeks) reduced bone damage due to the extended opportunity to remove and repair bone damage. These results provide an in-silico mathematical model of the bone response to training, demonstrating the effects of training programs on bone adaptation, damage formation and repair. The findings can guide the design of training programs that balance both bone adaptation and bone health throughout horses racing career.

Dongni Lan, Kongmei Li, Zhimao Ye et al.

Abstract Background Mogroside V (MV) is a triterpene glucoside that reportedly exhibits an array of antitumor, anti-inflammatory, hypolipidemic, and hypoglycemic properties. In prior studies, our group determined that MV was able to readily enhance osteogenic bone marrow mesenchymal stem cells (BMSCs) differentiation under high-glucose conditions through mechanisms potentially associated with miR-10b-5p and PI3K/Akt signaling activity. The precise molecular basis for these effects, however, remains to be fully elucidated. Objective This study aims to explore the potential mechanisms by which MV regulates the osteogenic differentiation of BMSCs under hyperglycemic conditions. Methods Femoral and tibial BMSCs were isolated from control and diabetic C57BL/6J mice. qRT-PCR was used to quantify miR-10b-5p levels. Putative miR-10b-5p target genes were predicted through bioinformatics assays and validated in a luciferase reporter assay system. miR-10b-5p expression was inhibited with an antagomiR-10b-5p construct, while PI3K/Akt pathway signaling was inhibited with LY294002. Western blotting was used to detect PI3K/Akt pathway and target gene protein levels, while Alizarin red staining was used to detect calcium nodule deposition by BMSCs. Results miR-10b-5p upregulation was noted in BMSCs exposed to hyperglycemic conditions. HOXD10 was identified as a cell differentiation-related miR-10b-5p target gene in bioinformatics analyses, and the targeting relationship between the two was confirmed in a luciferase reporter assay. MV treatment elicited significantly higher levels of miR-10b-5p expression, PI3K phosphorylation, and calcium deposition, while antagomiR-10b-5p or LY294002 treatment reversed these changes, and the opposite trends were observed with respect to HOXD10 protein levels. Conclusion MV favors BMSCs osteogenic differentiation under high-glucose conditions through the upregulation of miR-10b-5p and the activation of PI3K/Akt signaling.

Christos Kokkotis, Kyriakos Apostolidis, Dimitrios Menychtas et al.

Background/Objectives: Falls among the older adult population represent a significant public health concern, often leading to diminished quality of life and serious injuries that escalate healthcare costs, and they may even prove fatal. Accurate fall risk prediction is therefore crucial for implementing timely preventive measures. However, to date, there is no definitive metric to identify individuals with high risk of experiencing a fall. To address this, the present study proposes a novel approach that transforms biomechanical time-series data, derived from gait analysis, into visual representations to facilitate the application of deep learning (DL) methods for fall risk assessment. Methods: By leveraging convolutional neural networks (CNNs) and Siamese neural networks (SNNs), the proposed framework effectively addresses the challenges of limited datasets and delivers robust predictive capabilities. Results: Through the extraction of distinctive gait-related features and the generation of class-discriminative activation maps using Grad-CAM, the random forest (RF) machine learning (ML) model not only achieves commendable accuracy (83.29%) but also enhances explainability. Conclusions: Ultimately, this study underscores the potential of advanced computational tools and machine learning algorithms to improve fall risk prediction, reduce healthcare burdens, and promote greater independence and well-being among the older adults.

Aitana Vázquez‐Fernández, Francisco F. Caballero, Humberto Yévenes‐Briones et al.

ABSTRACT Background Multimorbidity is the most common chronic condition experienced among older adults. It is unknown which amount and source of protein influences the development of frailty and mortality in patients with multimorbidity. We aimed to examine the association of plant and animal sources of protein intake with frailty and mortality among this type of patients. Methods This longitudinal study included 1868 participants aged ≥ 60 years from the Seniors‐ENRICA cohort in Spain with multimorbidity, defined as having 2 or more clinician‐diagnosed chronic diseases. Habitual diet was assessed at baseline (2008–2010) with a validated computerized diet history. Participants underwent repeated physical examinations (in 2013, 2015 and 2017) for assessment of frailty (≥ 3 criteria from the frailty phenotype: low physical activity, slow walking speed, muscle weakness, weight loss and exhaustion). All‐cause mortality was assessed up to January 2022. Analyses were conducted using Cox proportional hazard models and multistate models adjusted for sociodemographic, lifestyle and other dietary factors. Results Mean consumption of protein was 90.2 (standard deviation [SD]: 26.8) g/day, which represents 18.7% of the total energy intake and 1.23 (0.39) g per kg of body weight per day. Plant protein represented 6.16% of the energy intake, while animal protein represented 12.5%. During a median follow‐up of 12.9 (range: 11.7–13.9) years, we documented 196 incident cases of frailty and 490 deaths; of these mortality cases, 83 individuals died after a frailty diagnosis. Higher intake of total protein was associated with decreased risk of frailty (hazard ratio [HR] for tertile 3 vs. tertile 1: 0.66; 95% confidence interval [CI]: 0.45, 0.96; p trend: 0.03). In multistate models, higher fish protein intake decreased the risk in the progression from multimorbidity to frailty (HR per 1‐SD increment: 0.81 [95% CI: 0.68, 0.97]), and higher plant protein decreased the risk of progressing from multimorbidity to mortality (0.86 [0.75, 0.98]). In the progression from frailty to mortality, estimates for total, plant and animal protein showed increased risk (HR for 1 SD increment in total protein: 1.38 [1.05, 1.81]; HR for plant protein: 1.29 [1.01, 1.67]; HR for animal protein: 1.41 [1.04, 1.92]). No significant associations were found between meat protein and dairy protein in any transition. Conclusions In individuals with multimorbidity, higher protein intake, especially fish protein, was associated with lower risk of subsequent frailty, whereas plant protein intake was associated with lower risk of mortality. Higher total protein intake, however, might be detrimental in patients with multimorbidity and frailty. Trial Registration ClinicalTrials.gov identifier: NCT02804672.

Hye‐Sun Park, Sang Ho Park, Yeseul Seong et al.

Abstract Background The association between the adiponectin‐to‐leptin ratio (A/L ratio) and the risk of incident chronic kidney disease (CKD) is poorly understood. This study aimed to investigate the association between A/L ratio and the risk of incident CKD and to examine whether such a relationship varied according to sex and body composition. Methods In this prospective community‐based cohort, participants with normal kidney function were analysed (N = 5192). The association between the A/L ratio at baseline and the risk of incident CKD, defined as two or more occasions with an estimated glomerular filtration rate of <60 mL/min/m2 or proteinuria of ≥1+ on a dipstick test during the follow‐up period, was evaluated using multivariable Cox proportional hazards analyses. Subgroup analyses were conducted based on sex, body mass index (BMI) and the presence of sarcopenia. Results The participants' mean age was 57.2 ± 8.3 years, and 53.2% were women. The A/L ratio was higher in men compared with women (1.5 [0.8–3.2] and 0.5 [0.3–0.9] μg/ng, P < 0.001). During a median follow‐up of 9.8 [9.5–10.0] years, 417 incident CKD events occurred (8.7 per 1000 person‐years). Men in the highest quartile of A/L ratio had a lower risk of incident CKD (adjusted hazard ratio [aHR], 0.57; 95% confidence interval [CI], 0.33–0.99) than those in the lowest quartile. Additionally, a 1.0 increase in A/L ratio was associated with a 12% decreased risk of incident CKD in men (aHR, 0.88; 95% CI, 0.80–0.97). However, no significant association was observed in women. In subgroup analysis stratified by BMI and the presence of sarcopenia, the association between a high A/L ratio and a reduced risk of incident CKD was consistent in men with a BMI < 23.0 kg/m2 and those with sarcopenia. However, no significant association was observed between men with a BMI ≥ 23.0 kg/m2 and those without sarcopenia. Conclusions A high A/L ratio is an independent marker of a reduced risk of incident CKD in men, especially in those with a BMI < 23.0 kg/m2 and sarcopenia.

Sydney C. Liles, Bradley Bley, Daniel K. White

Objective: Current treatment for knee Osteoarthritis (OA) includes exercise and intra-articular injections with corticosteroid (CS), hyaluronic acid (HA), etc., which address OA-related pain and functional limitation. While these interventions can be given together, little is known about the efficacy of a multi-modal approach. The purpose of this scoping review is to examine studies that compare combining exercise and intra-articular knee injections to exercise alone for the management of knee OA. Methods: A search was performed using PubMed, CINAHL, and Clinicaltrials.gov with MeSH terms “knee osteoarthritis” AND “exercise” AND “injections”. Abstracts were screened to meet inclusion criteria of both intervention groups including exercise and one group receiving an injection for treatment of knee OA. Full text articles were screened to meet inclusion criteria and rated using the Pedro Scale. Results: 11 studies that met inclusion criteria. The included studies utilized CS, hyaluronic acid (HA), and Bone Marrow Concentrate (BMC), botulinum toxin A, or a combination of dextrose and lidocaine injections. Most studies included supervised exercise interventions with all studies including strengthening of the quadriceps. CS and exercise compared to exercise alone showed similar improvements in pain. The HA injection studies yielded mixed results with two studies finding HA and exercise was not superior than exercise alone while two other studies found that HA and exercise were superior. Conclusion: There was a paucity of literature investigating multimodal approaches. Most of the included studies did not find superior effects of adding a knee injection to exercise compared to exercise alone for knee OA.

Victoria Hedley, Rebecca Leary, Anando Sen et al.

Over the past 50 years, the advancements in medical and health research have radically changed the epidemiology of health conditions in neonates, children, and adolescents; and clinical research has on the whole, moved forward. However, large sections of the pediatric community remain vulnerable and underserved, by clinical research. One reason for this is the fact that most pediatric diseases are also rare diseases (i.e., they fit the EU definition of a rare condition, by affecting no more than 5 in 10,000 individuals), and indeed the majority of conditions under this umbrella heading are in fact much rarer, affecting fewer than 1 in 100,000. Rare pediatric diseases incur particular challenges, both in terms of actually conducting clinical trials but also planning trials (and indeed, stimulating the preclinical research and knowledge generation necessary to embark on clinical trials in the first place). The pediatric regulation and orphan regulation (covering rare diseases) were introduced to address the complexities in research and development of medicines specifically for children and for people living with a rare disease, respectively. The regulations have been reasonably effective, particularly in areas where adult and pediatric diseases overlap, driving the development of more pediatric medicines; however, challenges still remain, often exacerbated by the rarity of the diseases. These include issues around trial planning, the need for more innovative methodologies in smaller populations, significant delays in trial start up and recruitment, recruitment issues (due to small populations and the nature of the conditions), lack of endpoints, and scarce data. This chapter will discuss some of the major challenges in delivering trials in pediatric rare diseases while also assessing current and future solutions to address these.

Md. Fahad Hossain

This dataset represents almost all the harmful diseases for rice in Bangladesh. This dataset consists of 1106 image of five harmful diseases called Brown Spot, Leaf Scaled, Rice Blast, Rice Turngo, Steath Blight in two different background variation named field background picture and white background picture. Two different background variation helps the dataset to perform more accurately so that the user can use this data for field use as well as white background for decision making. The data is collected from rice field of Dhaka Division. This dataset can use for rice leaf diseases classification, diseases detection using Computer Vision and Pattern Recognition for different rice leaf disease.

Hasin Rehana, Muhammad Ibrahim, Md. Haider Ali

Agriculture plays an important role in the food and economy of Bangladesh. The rapid growth of population over the years also has increased the demand for food production. One of the major reasons behind low crop production is numerous bacteria, virus and fungal plant diseases. Early detection of plant diseases and proper usage of pesticides and fertilizers are vital for preventing the diseases and boost the yield. Most of the farmers use generalized pesticides and fertilizers in the entire fields without specifically knowing the condition of the plants. Thus the production cost oftentimes increases, and, not only that, sometimes this becomes detrimental to the yield. Deep Learning models are found to be very effective to automatically detect plant diseases from images of plants, thereby reducing the need for human specialists. This paper aims at building a lightweight deep learning model for predicting leaf disease in tomato plants. By modifying the region-based convolutional neural network, we design an efficient and effective model that demonstrates satisfactory empirical performance on a benchmark dataset. Our proposed model can easily be deployed in a larger system where drones take images of leaves and these images will be fed into our model to know the health condition.

Steven A. Frank

Danger requires a strong rapid response. Speedy triggers are prone to false signals. False alarms can be costly, requiring strong negative regulators to oppose the initial triggers. Strongly opposed forces can easily be perturbed, leading to imbalance and disease. For example, immunity and fear response balance strong rapid triggers against widespread slow negative regulators. Diseases of immunity and behavior arise from imbalance. A different opposition of forces occurs in mammalian growth, which balances strong paternally expressed accelerators against maternally expressed suppressors. Diseases of overgrowth or undergrowth arise from imbalance. Other examples of opposing forces and disease include control of dopamine expression and male versus female favored traits.

Ganping Li, Yoshito Otake, Mazen Soufi et al.

Purpose: Manual annotations for training deep learning (DL) models in auto-segmentation are time-intensive. This study introduces a hybrid representation-enhanced sampling strategy that integrates both density and diversity criteria within an uncertainty-based Bayesian active learning (BAL) framework to reduce annotation efforts by selecting the most informative training samples. Methods: The experiments are performed on two lower extremity (LE) datasets of MRI and CT images, focusing on the segmentation of the femur, pelvis, sacrum, quadriceps femoris, hamstrings, adductors, sartorius, and iliopsoas, utilizing a U-net-based BAL framework. Our method selects uncertain samples with high density and diversity for manual revision, optimizing for maximal similarity to unlabeled instances and minimal similarity to existing training data. We assess the accuracy and efficiency using Dice and a proposed metric called reduced annotation cost (RAC), respectively. We further evaluate the impact of various acquisition rules on BAL performance and design an ablation study for effectiveness estimation. Results: In MRI and CT datasets, our method was superior or comparable to existing ones, achieving a 0.8\% Dice and 1.0\% RAC increase in CT (statistically significant), and a 0.8\% Dice and 1.1\% RAC increase in MRI (not statistically significant) in volume-wise acquisition. Our ablation study indicates that combining density and diversity criteria enhances the efficiency of BAL in musculoskeletal segmentation compared to using either criterion alone. Conclusion: Our sampling method is proven efficient in reducing annotation costs in image segmentation tasks. The combination of the proposed method and our BAL framework provides a semi-automatic way for efficient annotation of medical image datasets.

Kristjan Krips, Nikita Snetkov, Jelizaveta Vakarjuk et al.

Assessing and comparing the security level of different voting systems is non-trivial as the technical means provided for and societal assumptions made about various systems differ significantly. However, trust assumptions concerning the involved parties are present for all voting systems and can be used as a basis for comparison. This paper discusses eight concrete voting systems with different properties, 12 types of parties involved, and seven general security goals set for voting. The emerging trust relations are assessed for their criticality, and the result is used for comparison of the considered systems.

Sumaya Mustofa, Md Mehedi Hasan Munna, Yousuf Rayhan Emon et al.

Leaf disease is a common fatal disease for plants. Early diagnosis and detection is necessary in order to improve the prognosis of leaf diseases affecting plant. For predicting leaf disease, several automated systems have already been developed using different plant pathology imaging modalities. This paper provides a systematic review of the literature on leaf disease-based models for the diagnosis of various plant leaf diseases via deep learning. The advantages and limitations of different deep learning models including Vision Transformer (ViT), Deep convolutional neural network (DCNN), Convolutional neural network (CNN), Residual Skip Network-based Super-Resolution for Leaf Disease Detection (RSNSR-LDD), Disease Detection Network (DDN), and YOLO (You only look once) are described in this review. The review also shows that the studies related to leaf disease detection applied different deep learning models to a number of publicly available datasets. For comparing the performance of the models, different metrics such as accuracy, precision, recall, etc. were used in the existing studies.

Antoni Caserta, Prue Morgan, Marnee J. McKay et al.

Wei Ji, Yueyang Lu, Zhuoyi Ma et al.

Background: Ankylosing spondylitis (AS) is featured by chronic inflammation of the sacroiliac joints and spine as well as pathological new bone formation. Osteoclastogenesis is a critical part in the development of bone formation. Circular RNAs (circRNAs) are recent research hotspot in the RNA field while rarely reported in osteoclastogenesis. Methods: AS mesenchymal stem cells (ASMSCs) and healthy donor mesenchymal stem cells (HDMSCs) were co-cultured with peripheral blood mononuclear cells (PBMCs). RT-qPCR was applied to detect the expression level of circ-0110634 in different exosomes. TRAP staining and TRAP activity detection were performed to identify the effect of circ-0110634 overexpression on osteoclastogenesis. Bioinformatics analysis and mechanism investigation were conducted to explore the downstream molecular mechanism of circ-0110634. Results: The effect of ASMSCs on PBMCs osteoclastogenesis is weaker than that of HDMSCs. Circ-0110634 had higher expression in ASMSCs exosomes than HDMSCs exosomes. Circ-0110634 overexpression suppressed the osteoclastogenesis. Circ-0110634 bound to both TNF receptor associated factor 2 (TRAF2) and tumor necrosis factor receptor II (TNFRII). Circ-0110634 also accelerated the dimerization of TRAF2 to induce TRAF2 ubiquitination and degradation. Circ-0110634 repressed the interplay between TRAF2 and TNFRII to inactivate the nuclear factor-κB (NF-κB) and mitogen-activated protein kinases (MAPK) pathways. Triptolide promoted the osteoclastogenesis of ASMSCs exosomes-treated PBMCs via decreasing the exosomal transference of circ-0110634 in a dose-dependent manner. Consistently, triptolide treatment stimulated osteoclastogenesis to alleviate the arthritis of DBA/1 mice through suppressing circ-0110634. Conclusion: Our study confirmed that triptolide targets circ-0110634 to ease the burden of AS patients. The Translational potential of this article: This study suggests triptolide targets circ-0110634 to regulate osteoclastogenesis, which provides a novel potential target in triptolide treatment for AS patients.

Yongmao Wu, Yan Shao, Denghui Xie et al.

Aims: To verify whether secretory leucocyte protease inhibitor (SLPI) can promote early tendon-to-bone healing after anterior cruciate ligament (ACL) reconstruction. Methods: In vitro: the mobility of the rat bone mesenchymal stem cells (BMSCs) treated with SLPI was evaluated by scratch assay. Then the expression levels of osteogenic differentiation-related genes were analyzed by real-time quantitative PCR (qPCR) to determine the osteogenic effect of SLPI on BMSCs. In vivo: a rat model of ACL reconstruction was used to verify the effect of SLPI on tendon-to-bone healing. All the animals of the SLPI group and the negative control (NC) group were euthanized for histological evaluation, micro-CT scanning, and biomechanical testing. Results: SLPI improved the migration ability of BMSCs and upregulated the expression of genes related to osteogenic differentiation of BMSCs in vitro. In vivo, the SLPI group had higher histological scores at the tendon-bone interface by histological evaluation. Micro-CT showed more new bone formation and bone ingrowth around the grafted tendon in the SLPI group. Evaluation of the healing strength of the tendon-bone connection showed that the SLPI group had a higher maximum failure force and stiffness. Conclusion: SLPI can effectively promote early tendon-to-bone healing after ACL reconstruction via enhancing the migration and osteogenic differentiation of BMSCs. Cite this article: Bone Joint Res 2022;11(7):503–512.

Kirstin Roster, Colm Connaughton, Francisco A. Rodrigues

Recent infectious disease outbreaks, such as the COVID-19 pandemic and the Zika epidemic in Brazil, have demonstrated both the importance and difficulty of accurately forecasting novel infectious diseases. When new diseases first emerge, we have little knowledge of the transmission process, the level and duration of immunity to reinfection, or other parameters required to build realistic epidemiological models. Time series forecasts and machine learning, while less reliant on assumptions about the disease, require large amounts of data that are also not available in early stages of an outbreak. In this study, we examine how knowledge of related diseases can help make predictions of new diseases in data-scarce environments using transfer learning. We implement both an empirical and a theoretical approach. Using empirical data from Brazil, we compare how well different machine learning models transfer knowledge between two different disease pairs: (i) dengue and Zika, and (ii) influenza and COVID-19. In the theoretical analysis, we generate data using different transmission and recovery rates with an SIR compartmental model, and then compare the effectiveness of different transfer learning methods. We find that transfer learning offers the potential to improve predictions, even beyond a model based on data from the target disease, though the appropriate source disease must be chosen carefully. While imperfect, these models offer an additional input for decision makers during pandemic response.

Gareth T. Jones, Gary J. Macfarlane, Karen Forrest Keenan et al.

Abstract Background Psoriatic arthritis (PsA) presents a unique clinical challenge. Affecting joints, skin, nails, and other organs, it is associated with various comorbidities and has a significant impact on quality of life, social participation and working life. While biologic and other targeted synthetic disease modifying anti-rheumatic drugs (bDMARDs and tsDMARDs) have revolutionised therapy, questions remain about the long-term safety of these agents, and their effectiveness and cost-effectiveness in the real-world clinical setting. Methods/design The British Society for Rheumatology Psoriatic Arthritis Register (BSR-PsA) is a prospective registry of patients with PsA, recruited from across Great Britain, who are (a) commencing a bDMARD/tsDMARD; or (b) naïve to all bDMARDs/tsDMARDs. Ethical approval was given by the NHS West of Scotland Research Ethics Committee 3 (reference: 18/WS/0126). Clinical data are extracted from participants’ medical records, including symptom onset and diagnosis, joint, skin and nail symptoms, dactylitis and enthesitis. Physical measurements (height, weight and 66/68 joint counts) and a detailed drug history are taken. Participants are also asked to complete questionnaires comprising instruments relating to general health and quality of life, axial disease, sleep and fatigue, impact of disease, functional status, mental health, other symptoms, and occupational status. The study duration is 5 years in the first instance, and all participants are followed up annually until the end of the study. Participants commencing a bDMARD/tsDMARD are also followed up three and six months after the start of therapy. Disease activity, including C-reactive protein, is assessed at each visit; and participants from some centres are invited to donate blood and urine samples for the creation of a biobank. Discussion Complementing data from randomised trials, results from this study will contribute to the evidence base underpinning the clinical management of psoriatic arthritis. Various analyses will determine the effectiveness and safety of bDMARDs/tsDMARDs in the real-world, will examine the clinical and biological predictors of treatment response, and will provide real-world data on the cost-effectiveness of these therapies, as well as providing informative data important to patients such as quality of life and occupational outcomes. Trial registration The full study protocol is registered on the Open Science Framework ( https://osf.io/jzs8n ).

Min Sue Park, Hyeontae Jo, Haeun Lee et al.

A prompt severity assessment model of patients confirmed for having infectious diseases could enable efficient diagnosis while alleviating burden on the medical system. This study aims to develop a SARS-CoV-2 severity assessment model and establish a medical system that allows patients to check the severity of their cases and informs them to visit the appropriate clinic center based on past treatment data of other patients with similar severity levels. This paper provides the development processes of a severity assessment model using machine learning techniques and its application on SARS-CoV-2 patients. The proposed model is trained on a nationwide dataset provided by a Korean government agency and only requires patients' basic personal data, allowing them to judge the severity of their own cases. After modeling, the boosting-based decision tree model was selected as the classifier while mortality rate was interpreted as the probability score. The dataset was collected from all Korean citizens who were confirmed with COVID-19 between February, 2020 and July, 2021. The experiments achieved high model performance with an approximate precision of $0{\cdot}923$ and AUROC score of $0{\cdot}950$ [$95$% Tolerance Interval $0{\cdot}940$-$0{\cdot}958$, $95$% Confidence Interval $0{\cdot}949$-$0{\cdot}950$]. Moreover, our experiments identified the most important variables affecting the severity in the model via sensitivity analysis. The prompt severity assessment model for managing infectious people has been attained through using a nationwide dataset. It has demonstrated its superior performance by surpassing that of conventional risk assessments. With the model's high performance and easily accessible features, the triage algorithm is expected to be particularly useful when patients monitor their health status by themselves through smartphone applications.