Technology and Health Care - Volume 32, issue 3

Authors: Gao, Jing | Zhang, Chao | Xin, Hong

Article Type: Research Article

Abstract: BACKGROUND: Using a COOK® Cervical Ripening Balloon (CCRB) for cervical maturity has become a common clinical practice for the induction of labour (IOL). OBJECTIVE: To develop and validate a predictive instrument that could estimate the risk of a caesarean after IOL in term pregnancies with CCRB treatment. METHODS: The medical records of 415 pregnant women requiring IOL from January 2018 to October 2022 were retrospectively reviewed and randomly selected for training (290) and validation (125) sets in a 7:3 ratio. A model for predicting the risk of a caesarean was virtualised by …a nomogram using logistic regression analysis. RESULTS: After completing the multivariate analysis, parity (odds ratio [OR] = 0.226; p = 0.017), modified Bishop score at induction (OR = 0.688; p = 0.005) and the artificial rupture of membranes (OR = 0.436; p = 0.010) were identified as the predictors for implementing a caesarean delivery after IOL. The decision curve analysis showed that the model achieved a net benefit across all threshold probabilities. CONCLUSION: We successfully constructed a nomogram for caesarean delivery after IOL in pregnancies with CCRB treatment using factors including parity, modified Bishop score at induction and the artificial rupture of membrane. Show more

Keywords: Induction of labour, caesarean delivery, nomogram, Bishop score, cervical ripening balloon

DOI: 10.3233/THC-230761

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1745-1755, 2024

Authors: Hu, Huijun | Ma, Yanfang | Cheng, Aiai | Zhang, Zengqiang

Article Type: Research Article

Abstract: BACKGROUND: There have been studies on the use of cognitive behavioral nursing intervention (CBNI) but the main subjects were patients with secondary glaucoma and there are fewer studies on the care of congenital glaucoma. OBJECTIVE: To explore the clinical value of cognitive behavioral nursing model in patients with congenital glaucoma. METHODS: One hundred and fifty-three postoperative patients with congenital glaucoma treated in our hospital between January 2021 and December 2022 were prospectively selected for the study and randomly divided into a control group (n = 77) and an …observation group (n = 76). The control group was given routine nursing, and the observation group was given cognitive behavioral nursing mode on the basis of the control group. Anxiety self-assessment scale (SAS), depression self-assessment scale (SDS), Connor-Davidson toughness scale, and treatment adherence evaluation scale were used to evaluate the psychological state, mental toughness, treatment adherence, treatment effect and nursing care satisfaction in the two groups before and after 2 weeks of intervention. The efficacy of the treatment was also assessed by determining the visual acuity (VA), intraocular pressure (IOP), and mean defective (MD) value of the visual field of the two groups of patients. RESULTS: After nursing, the SDS score (46.33 ± 6.16 versus 53.21 ± 5.94) and SAS score (44.41 ± 5.6 versus 52.82 ± 6.31) in the observation group were lower than those in the control group (P < 0.05). The scores of optimism (11.55 ± 1.90 versus 8.20 ± 1.95), self-improvement (22.05 ± 3.60 versus 17.60 ± 4.30), tenacity (37.45 ± 3.10 versus 28.90 ± 4.55) and total score (71.35 ± 8.00 versus 56.85 ± 8.50) in the observation group were higher than those in the control group (P < 0.05). After care, the VA of the observation group (0.95 ± 0.22) was greater than that of the control group (0.84 ± 0.16), and the IOP (14.25 ± 0.58 versus 15.89 ± 0.67) and the MD (5.42 ± 0.46 versus 6.68 ± 0.49) of the observation group were less than that of the control group. The difference between the two groups was statistically significant (P < 0.05). The compliance (96.05% versus 85.71%) and nursing satisfaction (96.10% versus 85.71%) of the observation group were higher than those of the control group (P < 0.05). CONCLUSION: Cognitive-behavioural nursing care for glaucoma patients can improve patients’ mental toughness, improve visual acuity, reduce intraocular pressure and mean visual field defect values, and have a positive effect on enhancing patients’ treatment adherence and nursing satisfaction. Show more

Keywords: Cognitive behavioral nursing, congenital glaucoma, compliance, self-management

DOI: 10.3233/THC-230772

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1757-1766, 2024

Authors: Gu, Bao-Di | Wang, Yun | Ding, Rong

Article Type: Research Article

Abstract: BACKGROUND: Malnutrition is a widespread problem in critically ill patients with neurological disorders. OBJECTIVE: The purpose of this study is to investigate the effect of a multidisciplinary collaborative nutritional treatment mode based on a standardized unit for nutritional support on the outcome metrics in patients with neurological disorders who are critically ill. METHODS: We enrolled 84 participants who were hospitalized in the intensive care unit (ICU) of Yancheng No. 1 People’s Hospital for neurological disorders between June 2018 and December 2021. The participants were randomly assigned to the control group and the test …group. The control group received traditional nutritional support, while the test group was treated with a multidisciplinary collaborative nutritional treatment mode based on a standardized unit for nutritional support. We collected the general information, feeding tolerance (FT), nutritional risk score, and laboratory indicators before intervention, after intervention for one week, and after intervention for 2 weeks, and other data of the participants. RESULTS: After the intervention, the test group scored significantly lower than the control group in the incidence of gastroparesis and diarrhea, as well as the NUTRIC score, with statistically significant differences (P < 0.001). The prealbumin levels in the test group increased progressively prior to intervention, after intervention for one week, and after intervention for two weeks. Compared to the control group, the test group had higher prealbumin levels prior to intervention, after intervention for one week, and after intervention for two weeks, with statistically significant differences (P < 0.001). CONCLUSION: We developed a multidisciplinary collaborative nutritional treatment model based on a standard unit for nutritional support. This model can improve neural function, FT, and pertinent outcome indicators and is generally applicable. Show more

Keywords: Nervous system diseases, nutrition therapy, patient care team

DOI: 10.3233/THC-230791

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1767-1780, 2024

Authors: Guo, Julong | Ning, Yachan | Pan, Dikang | Wu, Sensen | Gao, Xixiang | Wang, Cong | Guo, Lianrui | Gu, Yongquan

Article Type: Research Article

Abstract: BACKGROUND: Endothelial dysfunction, the earliest stage of atherosclerosis, can be caused by smoking, but its molecular mechanism requires further investigation. OBJECTIVE: This study aimed to use bioinformatics analysis to identify potential mechanisms involved in smoking-related atherosclerotic endothelial dysfunction. METHODS: The transcriptome data used for this bioinformatics analysis were obtained from the Gene Expression Omnibus (GEO) database. The GSE137578 and GSE141136 datasets were used to identify common differentially expressed genes (co-DEGs) in endothelial cells treated with oxidized low-density lipoprotein (ox-LDL) and tobacco. The co-DEGs were annotated using Gene Ontology (GO) and Kyoto Encyclopedia of …Genes and Genomics (KEGG) databases. Additionally, a protein-protein interaction (PPI) network was constructed to visualize their interactions and screen for hub genes. GSE120521 dataset was used to verify the expression of hub genes in unstable plaques. The miRNA expression profile GSE137580 and online databases (starBase 2.0, TargetScan 8.0 and DGIdb v4.2.0) were used to predict the related non-coding RNAs and drugs. RESULTS: A total of 232 co-DEGs were identified, including 113 up-regulated genes and 119 down-regulated genes. These DEGs were primarily enriched in detrimental autophagy, cell death, transcription factors, and cytokines, and were implicated in ferroptosis, abnormal lipid metabolism, inflammation, and oxidative stress pathways. Ten hub genes were screened from the constructed PPI network, including up-regulated genes such as FOS, HMOX1, SQSTM1, PTGS2, ATF3, DDIT3, and down-regulated genes MCM4, KIF15, UHRF1, and CCL2. Importantly, HMOX1 was further up-regulated in unstable plaques (p = 0.034). Finally, a regulatory network involving lncRNA/circRNA-miRNA-hub genes and drug-hub genes was established. CONCLUSION: Atherosclerotic endothelial dysfunction is associated with smoking-induced injury. Through bioinformatics analysis, we identified potential mechanisms and provided potential therapeutic targets. Show more

Keywords: Atherosclerosis, smoke, tobacco, endothelial cells, genes

DOI: 10.3233/THC-230796

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1781-1794, 2024

Authors: Ozcelik, Neslihan | Kıvrak, Mehmet | Kotan, Abdurrahman | Selimoğlu, İnci

Article Type: Research Article

Abstract: BACKGROUND: Lung cancer is the most common type of cancer, accounting for 12.8% of cancer cases worldwide. As initially non-specific symptoms occur, it is difficult to diagnose in the early stages. OBJECTIVE: Image processing techniques developed using machine learning methods have played a crucial role in the development of decision support systems. This study aimed to classify benign and malignant lung lesions with a deep learning approach and convolutional neural networks (CNNs). METHODS: The image dataset includes 4459 Computed tomography (CT) scans (benign, 2242; malignant, 2217). The research type was retrospective; the case-control …analysis. A method based on GoogLeNet architecture, which is one of the deep learning approaches, was used to make maximum inference on images and minimize manual control. RESULTS: The dataset used to develop the CNNs model is included in the training (3567) and testing (892) datasets. The model’s highest accuracy rate in the training phase was estimated as 0.98. According to accuracy, sensitivity, specificity, positive predictive value, and negative predictive values of testing data, the highest classification performance ratio was positive predictive value with 0.984. CONCLUSION: The deep learning methods are beneficial in the diagnosis and classification of lung cancer through computed tomography images. Show more

Keywords: Lung cancer, deep learning, convolutional neural network, GoogLeNet

DOI: 10.3233/THC-230810

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1795-1805, 2024

Authors: Cao, Xu | Pei, Xiaomei

Article Type: Research Article

Abstract: BACKGROUND: Diabetic kidney disease (DKD) is an important microvascular complication of diabetes mellitus (DM). OBJECTIVE: This study aimed to develop predictive nomograms to estimate the risk of DKD in patients with type 2 diabetes mellitus (T2DM). METHODS: The medical records of patients with T2DM in our hospital from March 2022 to March 2023 were retrospectively reviewed. The enrolled patients were randomly selected for training and validation sets in a 7:3 ratio. The models for predicting risk of DKD were virtualized by the nomograms using logistic regression analysis. RESULTS: Among the …enrolled 597 patients, 418 were assigned to the training set, while 179 were assigned to the validation set. Using the predictors included glycated hemoglobin A1c (HbA1c), high density lipoprotein cholesterol (HDL-C), presence of diabetic retinopathy (DR) and duration of diabetes (DD), we constructed a full model (model 1) for predicting DKD. And using the laboratory indexes of HbA1c, HDL-C, and cystatin C (Cys-C), we developed a laboratory-based model (model 2). The C-indexes were 0.897 for model 1 and 0.867 for model 2, respectively. The calibration curves demonstrated a good agreement between prediction and observation in the two models. The decision curve analysis (DCA) curves showed that the two models achieved a net benefit across all threshold probabilities. CONCLUSION: We successfully constructed two prediction models to evaluate the risk of DKD in patients with T2DM. The two models exhibited good predictive performance and could be recommended for DKD screening and early detection. Show more

Keywords: Diabetic kidney disease, type 2 diabetes mellitus, nomogram, risk factor, glycated hemoglobin A1c

DOI: 10.3233/THC-230811

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1807-1818, 2024

Authors: Wang, Luo | Li, Xin | Dong, Xue-Juan | Yu, Xiao-Ling | Zhang, Jing | Cheng, Zhi-Gang | Han, Zhi-Yu | Liu, Fang-Yi | Yu, Jie | Liang, Ping

Article Type: Research Article

Abstract: BACKGROUND: Several international practice guidelines have recommended local ablation as the first-line treatment for early-stage hepatocellular carcinoma (HCC). OBJECTIVE: This study aims to investigate the synergetic anti-tumor impact of dendritic cell-cytokine killer (DC-CIK) combined with microwave ablation (MWA) for HCC. METHODS: This retrospective study included 1,141 patients from the American Joint Committee on Cancer stage I-II HCC, who were treated with therapeutic MWA. The immunotherapy group encompassing 40 patients received additional immunotherapy with DC-CIK, whereas the control group consisting of 1,101 patients was treated with MWA alone. Propensity score matching (PSM) with ratio …of 1:3 was employed to balance selection bias. The oncological outcome and immune status were measured after combination therapy. RESULTS: The immunotherapy group patients exhibited significant longer disease-free survival (DFS, primary HCC: p = 0.036; recurrent HCC: p = 0.026). For patients with primary HCC, the recurrence frequency was reduced (p = 0.002), and recurrence interval (19 months vs. 9 months, p < 0.001) was prolonged in the immunotherapy group. Subgroup analysis revealed that patients ⩽ 60 years old, moderately-differentiated HCC, or co-infected with Hepatitis B Virus (HBV) had a significant benefit over DFS in the immunotherapy group. After combination therapy, the serum CD3+ (p = 0.049), CD8/CD28+ (p = 0.045) were elevated. CONCLUSION: Combination therapy with DC-CIK and MWA can significantly reduce the recurrence and prolong DFS, especially for patients ⩽ 60 years old or with moderately-differentiated HCC or co-infected with HBV. Show more

Keywords: Hepatocellular carcinoma, immunotherapy, ablation, dendritic cell, cytokine killer

DOI: 10.3233/THC-230871

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1819-1834, 2024

Authors: Bai, Yuan | He, Fang | Yu, Ying | Li, Jia

Article Type: Research Article

Abstract: BACKGROUND: Patients with coronavirus disease 2019 (COVID-19) have been shown to die mainly due to disease-induced acute respiratory distress syndrome (ARDS). Prone position ventilation (PPV) is an important ventilation strategy in the management of patients with ARDS. OBJECTIVE: To investigate the application of PPV in ventilation strategies for patients with COVID-19. METHODS: Three hundred patients with COVID-19 admitted to the Intensive Care Unit (ICU) of Shanxi Bethune Hospital from January 2020 to June 2021 were retrospectively collected. Based on body position and conscious state, all patients were divided into three groups: intubation prone …position group (n = 110), awake prone position group (n = 90) and supine position group (n = 100); The acute physiology and chronic health evaluation II (APACHE-II) scores, blood gas indicators, complications and other relevant clinical indicators were compared among the three groups. One-way ANOVA was used to compare means between multiple groups for quantitative information that conformed to a normal distribution. Repeated measures ANOVA was used for repeated measures data. Component comparisons were made using the Kruskal-Wallis H rank sum test for non-normally distributed quantitative data. RESULTS: One-way repeated-measures ANOVA main effect analysis showed different effects of different treatments on PaO2 in patients with COVID-19 (F treatment = 256.231, P < 0.05), with the order of awake prone position group > intubation prone position group > supine position group. The effects of the three different treatments on P/F in patients with COVID-19 (F treatment = 311.661, P < 0.05), with the order of awake prone position group > supine position group > intubation prone position group; Moreover, the three treatments had different effects on APACHE II scores in patients with COVID-19 (F treatment = 201.342, P < 0.05), with the order of intubation prone position group > supine position group > awake prone position group. CONCLUSION: Intubation prone position and awake prone position can improve lung function to some extent in patients with COVID-19, and should be applied as early as possible in patients with COVID-19-induced ARDS. Show more

Keywords: Prone position ventilation, supine position, coronavirus disease 2019, lung function, efficacy

DOI: 10.3233/THC-230874

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1835-1846, 2024

Authors: Zhang, Hua | Wang, Ning | Zhang, Xiao-Yan | Liu, Cong | Zhang, Jing

Article Type: Research Article

Abstract: BACKGROUND: The use of telemonitoring electrocardiogram (ECG) improves the detection rate of various arrhythmias as it offers an extended recording time and does not impose activity restrictions. However, many community hospitals lack the resources to conduct this test due to a lack of cardiac function specialists and antiquated screening equipment. OBJECTIVE: To establish a digital ECG telemonitoring system and remote consultation service between tertiary hospitals and community medical service stations to improve diagnosis and treatment effectiveness. METHODS: We used the PI ECG telemonitoring data acquisition system, with a personal digital assistant (PDA) or …computer serving as the platform for ECG acquisition, storage, display, printing, and transmission. We introduced this system to standardize the storage and transmission of ECG telemonitoring data, and ensure accurate transmission, reproducibility, and preservation of data. RESULTS: The implementation of the PI ECG telemonitoring data acquisition system enabled the sharing of remote ambulatory electrocardiography data between Beijing Shijitan Hospital and its affiliated community hospitals. This has resulted in reduced waiting times for patients to receive reports (from 0.3 to 1 hour, to 0.1 or less), shortened consultation times (from 2 hours to 0.1 hour), and improved patient satisfaction with consultations (> 20% of the patients reported they were highly satisfied). The system successfully combined the goals of intelligence, real-time synchronization, and efficiency, revolutionizing ECG telemonitoring diagnosis in the network era. CONCLUSION: Sharing digital telematics information between tertiary and community hospitals enables a tiered approach to diagnosis and treatment and allows for more efficient, accurate, and convenient remote diagnostics. Show more

Keywords: Community-based medical and health services, digital telemonitoring electrocardiography for diagnosis and treatment, information sharing, tiered diagnosis and treatment

DOI: 10.3233/THC-230914

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1847-1857, 2024

Authors: Badnjević, Almir | Pokvić, Lejla Gurbeta | Smajlhodžić-Deljo, Merima | Spahić, Lemana | Bego, Tamer | Meseldžić, Neven | Prnjavorac, Lejla | Prnjavorac, Besim | Bedak, Omer

Article Type: Research Article

Abstract: BACKGROUND: With the end of the coronavirus disease 2019 (COVID-19) pandemic, it becomes intriguing to observe the impact of innovative digital technologies on the diagnosis and management of diseases, in order to improve clinical outcomes for patients. OBJECTIVE: The research aims to enhance diagnostics, prediction, and personalized treatment for patients across three classes of clinical severity (mild, moderate, and severe). What sets this study apart is its innovative approach, wherein classification extends beyond mere disease presence, encompassing the classification of disease severity. This novel perspective lays the foundation for a crucial decision support system during patient …triage. METHODS: An artificial neural network, as a deep learning technique, enabled the development of a complex model based on the analysis of data collected during the process of diagnosing and treating 1000 patients at the Tešanj General Hospital, Bosnia and Herzegovina. RESULTS: The final model achieved a classification accuracy of 82.4% on the validation data set, which testifies to the successful application of the artificial neural network in the classification of clinical outcomes and therapy in patients infected with viral infections. CONCLUSION: The results obtained show that expert systems are valuable tools for decision support in healthcare in communities with limited resources and increased demands. The research has the potential to improve patient care for future epidemics and pandemics. Show more

Keywords: COVID-19, artificial intelligence, medical techniques, classification, digitization, prediction

DOI: 10.3233/THC-230917

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1859-1870, 2024

Authors: Zou, Li | Dong, Wei | Ai, Ying | Li, Yantao | Cheng, Yun | Feng, Yun

Article Type: Research Article

Abstract: BACKGROUND: Recurrent spontaneous abortion affects approximately 1–2% of reproductive-age women, with roughly half of RSA cases classified as unexplained recurrent spontaneous abortion (URSA). Genetic polymorphisms in eNOS gene have been shown to have significant implications across various disease processes. Nevertheless, the potential impact of eNOS gene polymorphisms on the susceptibility to URSA in Yunnan population has yet to be explored or documented. OBJECTIVE: This study aims to investigate the potential association between specific variations in the eNOS gene (VNTR 4b/a, - 786T > C, and + 894G > …T) and the risk of URSA in Yunnan population. METHODS: A total of 243 URSA patients and 241 healthy females are involved in this study. We conducted amplification of the eNOS gene fragment and performed sanger sequencing to detect the specific eNOS gene polymorphisms, including VNTR 4b/a, - 786T > C, and + 894G > T. Using a multivariate logistic regression model, we evaluate the potential association between eNOS gene polymorphisms (VNTR 4b/a, - 786T > C, and + 894G > T) and the risk of URSA. Furthermore, serum NO levels were measured in URSA patients. RESULTS: The presence of VNTR 4a, - 786C, and + 894T alleles was found to be associated with an increased risk of URSA. Additionally, our study revealed a significant association between the G-C-4b haplotype of the investigated eNOS gene polymorphisms and a predisposition to URSA. Notably, these eNOS polymorphisms were shown to reduce serum NO levels in URSA patients. CONCLUSION: This study provides evidence supporting the association between eNOS gene polymorphisms, VNTR 4b/a, - 786T > C, and + 894G > T, and the occurrence of URSA in Yunnan Province, China. Show more

Keywords: eNOS, URSA, VNTR 4b/a, -786T > C, +894G > T

DOI: 10.3233/THC-230934

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1871-1879, 2024

Authors: Zhou, Quan | Chen, Xurui

Article Type: Research Article

Abstract: BACKGROUND: The need for personalised care in the long-term management of patient health is paramount due to the variability in individual features and responses to specific medication. With the availability of large quantities of electronic patient records, big data analysis presents a valuable opportunity to gain insights into disease presentation and patient impact. OBJECTIVE: This study aims to utilise data science in the medical field to extract unknown information from databases, validate previously obtained data, and enhance personalised patient care. METHODS: An analytics suite is developed for monitoring patient health and treating cholesterol, …thyroid, and diabetes disorders. This suite employs exploratory, predictive, and visual analytics to categorise patient data into multiple tiers and forecast related complication risk and treatment response. RESULTS: The study found that the analytics suite could successfully identify correlations between various biological indicators of patients and disorders. The suite also showcased potential in predicting health risks and responses to treatments. CONCLUSION: The analytics employed in this study suggest advanced methods of data analysis, which could serve as potential decision-making tools for healthcare providers. These methods might lead to improved treatment outcomes, contributing significantly to personalised patient care. Show more

Keywords: Data analytics, neural networks, decision support system, patient monitoring, survival probability and visualization

DOI: 10.3233/THC-230980

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1881-1896, 2024

Authors: Chang, Jihui | Zhang, Shuang | Qiu, XuZhong | Huang, HaiJun | Zhang, Yun | Li, Kai

Article Type: Research Article

Abstract: BACKGROUND: Humeral fracture is a common long bone fracture in orthopedic clinical diagnosis and treatment. OBJECTIVE: To investigate the local temperature increase owing to changes in the specific absorption ratio (SAR) of the human body caused by humeral bone nails during magnetic resonance imaging (MRI). METHODS: A refined geometric model of the upper body was constructed via data segmentation and post-processing using the digital human image dataset. Finally, the geometric model was imported into COMSOL, a 3-T magnetic resonance coil was built, and the operating frequency (128 MHz) was set to analyze the …SAR of the bone-nail pair and temperature changes. RESULTS: The analysis of the changes after bone-nail implantation under different tissue conditions revealed that the SAR and temperature after implantation and fixation were three times higher than those before, and the areas with abrupt changes in SAR and temperature were primarily concentrated in the bone-nail area. CONCLUSION: In MRI, metal implants can cause local elevation of the SAR near the implant in the human body, resulting in a temperature increase around the implant. Consequently, long-term scanning can damage the human body. Show more

Keywords: Bone nails, magnetic resonance imaging, temperature rise effect, specific absorption ratio, digital human

DOI: 10.3233/THC-230995

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1897-1907, 2024

Authors: Loganathan, Prakash Kannan | Dorkins, Charles | Marlow, George | Zahra, James | Tulsianey, Mudit A. | Rowles, Toby J. | Chadwick, Paula

Article Type: Research Article

Abstract: BACKGROUND: Newborn hypothermia at birth remains as global challenge across all settings. The prevention of delivery room hypothermia at birth could potentially reduce neonatal morbidity and mortality. OBJECTIVE: To compare the heat conservation efficacy of Neohelp and Neowrap and evaluate the heat production efficacy of trans-warmer infant mattress (TWM) in a laboratory setting. METHODS: A beaker of water was heated at 60 ∘ C was covered by Neohelp or two layers of Neowrap and left to cool in an open room for 90 minutes and calculated the decay constant. Using …infra-red camera, we measured the maximum temperature and time taken to reach the temperature in the TWM. RESULTS: Neowrap took 863 seconds for the temperature to drop from 37∘ C to 35∘ C, compared with 941 seconds with Neohelp. When activated TWM reached a maximum temperature of 39.3 ± 0.1∘ C. It took 30 seconds when the activator was placed in the centre, compared with 88 seconds when it was at the corner. CONCLUSION: Compared to Neowrap, Neohelp had better heat conservation properties. Activating the metal disk from the TWM center would deliver quicker heat. Show more

Keywords: Hypothermia, neohelp, neowrap, trans-warmer mattress

DOI: 10.3233/THC-231001

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1909-1914, 2024

Authors: Wang, Xiangyun | Chen, Yuanjing | Ai, Hongjun | Li, Panpan | Zhu, Chengjie | Yuan, Jiaying

Article Type: Research Article

Abstract: BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory complication among the elderly, and its severity can escalate to respiratory failure as the disease progresses. OBJECTIVE: To evaluate the application value of non-invasive ventilation in the clinical treatment of patients with COPD and lung cancer. This study assesses its therapeutic effects and its impact on patients’ quality of life (QoL) as measured by the Functional Assessment of Cancer Therapy-Lung (FACT-L) scale. METHODS: A retrospective analysis was conducted on clinical data from 102 patients with COPD and lung cancer. Patients were divided into …two groups: the control group (n = 48), who received conventional treatment, and the observation group (n = 54), who received non-invasive positive pressure ventilation (NIPPV) in addition to conventional treatment. Relevant indicators of curative effect, including blood gas indices, incidence of dyspnoea, improvements in mental health and appetite, and FACT-L QoL scores, were analysed at 2 weeks, 1 month, and 6 months post-treatment. RESULTS: At 2 weeks post-treatment, the observation group who had used NIPPV showed significant improvements in blood gas indices, dyspnoea, mental state and self-care ability compared with the control group (p < 0.05). At 1 month, these benefits persisted and included improved maintenance of body weight (p < 0.05). By 6 months, the observation group had a lower incidence of pulmonary encephalopathy (p < 0.05), and QoL, as measured by the FACT-L scale, improved significantly in the observation group but declined in the control group (p < 0.05). CONCLUSION: NIPPV demonstrates significant efficacy in treating COPD patients with lung cancer, particularly in enhancing curative effects and improving patients’ QoL. Show more

Keywords: Non-invasive ventilation, lung cancer, respiratory failure, quality of life

DOI: 10.3233/THC-231063

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1915-1923, 2024

Authors: Netto, Ann Nita | Abraham, Lizy | Philip, Saji

Article Type: Research Article

Abstract: BACKGROUND: Cardiac diseases are highly detrimental illnesses, responsible for approximately 32% of global mortality [1]. Early diagnosis and prompt treatment can reduce deaths caused by cardiac diseases. In paediatric patients, it is challenging for paediatricians to identify functional murmurs and pathological murmurs from heart sounds. OBJECTIVE: The study intends to develop a novel blended ensemble model using hybrid deep learning models and softmax regression to classify adult, and paediatric heart sounds into five distinct classes, distinguishing itself as a groundbreaking work in this domain. Furthermore, the research aims to create a comprehensive 5-class paediatric phonocardiogram (PCG) …dataset. The dataset includes two critical pathological classes, namely atrial septal defects and ventricular septal defects, along with functional murmurs, pathological and normal heart sounds. METHODS: The work proposes a blended ensemble model (HbNet-Heartbeat Network) comprising two hybrid models, CNN-BiLSTM and CNN-LSTM, as base models and Softmax regression as meta-learner. HbNet leverages the strengths of base models and improves the overall PCG classification accuracy. Mel Frequency Cepstral Coefficients (MFCC) capture the crucial audio signal characteristics relevant to the classification. The amalgamation of these two deep learning structures enhances the precision and reliability of PCG classification, leading to improved diagnostic results. RESULTS: The HbNet model exhibited excellent results with an average accuracy of 99.72% and sensitivity of 99.3% on an adult dataset, surpassing all the existing state-of-the-art works. The researchers have validated the reliability of the HbNet model by testing it on a real-time paediatric dataset. The paediatric model’s accuracy is 86.5%. HbNet detected functional murmur with 100% precision. CONCLUSION: The results indicate that the HbNet model exhibits a high level of efficacy in the early detection of cardiac disorders. Results also imply that HbNet has the potential to serve as a valuable tool for the development of decision-support systems that aid medical practitioners in confirming their diagnoses. This method makes it easier for medical professionals to diagnose and initiate prompt treatment while performing preliminary auscultation and reduces unnecessary echocardiograms. Show more

Keywords: Blended ensemble, mel frequency cepstral coefficient, meta-learner, phonocardiogram, softmax regression

DOI: 10.3233/THC-231290

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1925-1945, 2024

Authors: Liu, Lili

Article Type: Research Article

Abstract: BACKGROUND: Osteoporosis is a medical disorder that causes bone tissue to deteriorate and lose density, increasing the risk of fractures. Applying Neural Networks (NN) to analyze medical imaging data and detect the presence or severity of osteoporosis in patients is known as osteoporosis classification using Deep Learning (DL) algorithms. DL algorithms can extract relevant information from bone images and discover intricate patterns that could indicate osteoporosis. OBJECTIVE: DCNN biases must be initialized carefully, much like their weights. Biases that are initialized incorrectly might affect the network’s learning dynamics and hinder the model’s ability to converge to …an ideal solution. In this research, Deep Convolutional Neural Networks (DCNNs) are used, which have several benefits over conventional ML techniques for image processing. METHOD: One of the key benefits of DCNNs is the ability to automatically Feature Extraction (FE) from raw data. Feature learning is a time-consuming procedure in conventional ML algorithms. During the training phase of DCNNs, the network learns to recognize relevant characteristics straight from the data. The Squirrel Search Algorithm (SSA) makes use of a combination of Local Search (LS) and Random Search (RS) techniques that are inspired by the foraging habits of squirrels. RESULTS: The method made it possible to efficiently explore the search space to find prospective values while using promising areas to refine and improve the solutions. Effectively recognizing optimum or nearly optimal solutions depends on balancing exploration and exploitation. The weight in the DCNN is optimized with the help of SSA, which enhances the performance of the classification. CONCLUSION: The comparative analysis with state-of-the-art techniques shows that the proposed SSA-based DCNN is highly accurate, with 96.57% accuracy. Show more

Keywords: Osteoporosis, classification, accuracy, optimization, bias, neural network, and squirrel search

DOI: 10.3233/THC-231517

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1947-1965, 2024

Authors: Sun, Yi | Ren, Tao | Ji, Xueli

Article Type: Research Article

Abstract: BACKGROUND: Currently, cerebral infarction (CI) is mainly treated by emergency craniotomy or conservative treatment. However, some studies have questioned the functional recovery of patients after hyperbaric oxygen therapy (HBOT)-specialized care. OBJECTIVE: This paper mainly explores the influence of HBOT-specialized care on limb motor function (LMF) and mental state of CI patients with hemiplegia. METHODS: The medical records of 113 CI patients with hemiplegia treated in our hospital from March 2020 to March 2022 were collected. Of these, 53 received routine care nursing (conventional group) and 60 cases were given HBOT-specialized care (research group). …Patient general data, scores of Fugl-Meyer Assessment (FMA), National Institutes of Health Stroke Scale (NIHSS), Self-rating Anxiety/Depression Scale (SAS/SDS) and Barthel Index (BI), and nursing efficiency were comparatively analyzed. RESULTS: The two groups showed comparability in general data. FMA and BI scores were increased in the research group after rehabilitation treatment, higher than the baseline and those of the conventional group, while NIHSS, SAS, and SDS scores were reduced, lower compared with baseline and those of the conventional group. In addition, significantly higher nursing efficiency was determined in the research group. CONCLUSION: HBOT-specialized care has beneficial effects on LMF, mental state, negative emotions and self-care ability of CI patients with hemiplegia and can enhance nursing efficacy, which deserves clinical popularization. Show more

Keywords: Hyperbaric oxygen therapy-specialized care, hyperbaric oxygen, cerebral infarction, motor function, mental state

DOI: 10.3233/THC-231643

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1967-1976, 2024

Authors: Wu, Mingzhen | Luan, Jixin | Zhang, Di | Fan, Hua | Qiao, Lishan | Zhang, Chuanchen

Article Type: Research Article

Abstract: BACKGROUND: Histopathological evaluation is currently the gold standard for grading gliomas; however, this technique is invasive. OBJECTIVE: This study aimed to develop and validate a diagnostic prediction model for glioma by employing multiple machine learning algorithms to identify risk factors associated with high-grade glioma, facilitating the prediction of glioma grading. METHODS: Data from 1114 eligible glioma patients were obtained from The Cancer Genome Atlas (TCGA) database, which was divided into a training set (n = 781) and a test set (n = 333). …Fifty machine learning algorithms were employed, and the optimal algorithm was selected to construct a prediction model. The performance of the machine learning prediction model was compared to the clinical prediction model in terms of discrimination, calibration, and clinical validity to assess the performance of the prediction model. RESULTS: The area under the curve (AUC) values of the machine learning prediction models (training set: 0.870 vs. 0.740, test set: 0.863 vs. 0.718) were significantly improved from the clinical prediction models. Furthermore, significant improvement in discrimination was observed for the Integrated Discrimination Improvement (IDI) (training set: 0.230, test set: 0.270) and Net Reclassification Index (NRI) (training set: 0.170, test set: 0.170) from the clinical prognostic model. Both models showed a high goodness of fit and an increased net benefit. CONCLUSION: A strong prediction accuracy model can be developed using machine learning algorithms to screen for high-grade glioma risk predictors, which can serve as a non-invasive prediction tool for preoperative diagnostic grading of glioma. Show more

Keywords: Glioma, machine learning, prediction model, grading, risk predictors

DOI: 10.3233/THC-231645

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1977-1990, 2024

Authors: Muniasamy, Anandhavalli | Begum, Arshiya | Sabahath, Asfia | yaqub, Humara | Karunakaran, Gauthaman

Article Type: Research Article

Abstract: BACKGROUND: Coronary heart disease (CHD) is one of the deadliest diseases and a risk prediction model for cardiovascular conditions is needed. Due to the huge number of features that lead to heart problems, it is often difficult for an expert to evaluate these huge features into account. So, there is a need of appropriate feature selection for the given CHD dataset. For early CHD detection, deep learning modes (DL) show promising results in the existing studies. OBJECTIVE: This study aimed to develop a deep convolution neural network (CNN) model for classification with a selected number of …efficient features using the LASSO (least absolute shrinkage and selection operator) technique. Also, aims to compare the model with similar studies and analyze the performance of the proposed model using accuracy measures. METHODS: The CHD dataset of NHANES (National Health and Nutritional Examination Survey) was examined with 49 features using LASSO technique. This research work is an attempt to apply an improved CNN model for the classification of the CHD dataset with huge features CNN model with feature extractor consists of a fully connected layer with two convolution 1D layers, and classifier part consists of two fully connected layers with SoftMax function was trained on this dataset. Metrics like accuracy recall, specificity, and ROC were used for the evaluation of the proposed model. RESULTS: The feature selection was performed by applying the LASSO model. The proposed CNN model achieved 99.36% accuracy, while previous studies model achieved over 80 to 92% accuracy. CONCLUSION: The application of the proposed CNN with the LASSO model for the classification of CHD can speed up the diagnosis of CHD and appears to be effective in predicting cardiovascular disease based on risk features. Show more

Keywords: Coronary heart disease, deep learning, machine learning, LASSO, convolutional neural network

DOI: 10.3233/THC-231807

Citation: Technology and Health Care, vol. 32, no. 3, pp. 1991-2007, 2024