Axial length, more than aging, decreases the thickness and superficial vessel density of retinal nerve fiber layer in non-glaucomatous eyes.

PURPOSE: This study seeks to build a normative database for the vessel density of the superficial retina (SVD) and evaluate how changes and trends in the retinal microvasculature may be influenced by age and axial length (AL) in non-glaucomatous eyes, as measured with optical coherence tomography angiography (OCTA). METHODS: We included 500 eyes of 290 healthy subjects visiting a county hospital. Each participant underwent comprehensive ophthalmological examinations and OCTA to measure the SVD and thickness of the macular and peripapillary areas. To analyze correlations between SVD and age or AL, multivariable linear regression models with generalized estimating equations were applied. RESULTS: Age was negatively correlated with the SVD of the superior, central, and inferior macular areas and the superior peripapillary area, with a decrease rate of 1.06%, 1.36%, 0.84%, and 0.66% per decade, respectively. However, inferior peripapillary SVD showed no significant correlation with age. AL was negatively correlated with the SVD of the inferior macular area and the superior and inferior peripapillary areas, with coefficients of -0.522%/mm, -0.733%/mm, and -0.664%/mm, respectively. AL was also negatively correlated with the thickness of the retinal nerve fiber layer and inferior ganglion cell complex (p = 0.004). CONCLUSION: Age and AL were the two main factors affecting changes in SVD. Furthermore, AL, a relative term to represent the degree of myopia, had a greater effect than age and showed a more significant effect on thickness than on SVD. This relationship has important implications because myopia is a significant issue in modern cities.

Neurobiological mechanisms of dialectical behavior therapy and Morita therapy, two psychotherapies inspired by Zen.

Psychotherapy is a learning process. Updating the prediction models of the brain may be the mechanism underlying psychotherapeutic changes. Although developed in different eras and cultures, dialectical behavior therapy (DBT) and Morita therapy are influenced by Zen principles, and both emphasize the acceptance of reality and suffering. This article reviews these two treatments, their common and distinct therapeutic factors, and their neuroscientific implications. Additionally, it proposes a framework that includes the predictive function of the mind, constructed emotions, mindfulness, therapeutic relationship, and changes enabled via reward predictions. Brain networks, including the Default Mode Network (DMN), amygdala, fear circuitry, and reward pathways, contribute to the constructive process of brain predictions. Both treatments target the assimilation of prediction errors, gradual reorganization of predictive models, and creation of a life with step-by-step constructive rewards. By elucidating the possible neurobiological mechanisms of these psychotherapeutic techniques, this article is expected to serve as the first step towards filling the cultural gap and creating more teaching methods based on these concepts.

Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study.

BACKGROUND: An accurate prediction of mortality in end-of-life care is crucial but presents challenges. Existing prognostic tools demonstrate moderate performance in predicting survival across various time frames, primarily in in-hospital settings and single-time evaluations. However, these tools may fail to capture the individualized and diverse trajectories of patients. Limited evidence exists regarding the use of artificial intelligence (AI) and wearable devices, specifically among patients with cancer at the end of life. OBJECTIVE: This study aimed to investigate the potential of using wearable devices and AI to predict death events among patients with cancer at the end of life. Our hypothesis was that continuous monitoring through smartwatches can offer valuable insights into the progression of patients at the end of life and enable the prediction of changes in their condition, which could ultimately enhance personalized care, particularly in outpatient or home care settings. METHODS: This prospective study was conducted at the National Taiwan University Hospital. Patients diagnosed with cancer and receiving end-of-life care were invited to enroll in wards, outpatient clinics, and home-based care settings. Each participant was given a smartwatch to collect physiological data, including steps taken, heart rate, sleep time, and blood oxygen saturation. Clinical assessments were conducted weekly. The participants were followed until the end of life or up to 52 weeks. With these input features, we evaluated the prediction performance of several machine learning-based classifiers and a deep neural network in 7-day death events. We used area under the receiver operating characteristic curve (AUROC), F1-score, accuracy, and specificity as evaluation metrics. A Shapley additive explanations value analysis was performed to further explore the models with good performance. RESULTS: From September 2021 to August 2022, overall, 1657 data points were collected from 40 patients with a median survival time of 34 days, with the detection of 28 death events. Among the proposed models, extreme gradient boost (XGBoost) yielded the best result, with an AUROC of 96%, F1-score of 78.5%, accuracy of 93%, and specificity of 97% on the testing set. The Shapley additive explanations value analysis identified the average heart rate as the most important feature. Other important features included steps taken, appetite, urination status, and clinical care phase. CONCLUSIONS: We demonstrated the successful prediction of patient deaths within the next 7 days using a combination of wearable devices and AI. Our findings highlight the potential of integrating AI and wearable technology into clinical end-of-life care, offering valuable insights and supporting clinical decision-making for personalized patient care. It is important to acknowledge that our study was conducted in a relatively small cohort; thus, further research is needed to validate our approach and assess its impact on clinical care. TRIAL REGISTRATION: ClinicalTrials.gov NCT05054907; https://classic.clinicaltrials.gov/ct2/show/NCT05054907.

Use of multimodal dataset in AI for detecting glaucoma based on fundus photographs assessed with OCT: focus group study on high prevalence of myopia.

BACKGROUND: Glaucoma is one of the major causes of blindness; it is estimated that over 110 million people will be affected by glaucoma worldwide by 2040. Research on glaucoma detection using deep learning technology has been increasing, but the diagnosis of glaucoma in a large population with high incidence of myopia remains a challenge. This study aimed to provide a decision support system for the automatic detection of glaucoma using fundus images, which can be applied for general screening, especially in areas of high incidence of myopia. METHODS: A total of 1,155 fundus images were acquired from 667 individuals with a mean axial length of 25.60 ± 2.0 mm at the National Taiwan University Hospital, Hsinchu Br. These images were graded based on the findings of complete ophthalmology examinations, visual field test, and optical coherence tomography into three groups: normal (N, n = 596), pre-perimetric glaucoma (PPG, n = 66), and glaucoma (G, n = 493), and divided into a training-validation (N: 476, PPG: 55, G: 373) and test (N: 120, PPG: 11, G: 120) sets. A multimodal model with the Xception model as image feature extraction and machine learning algorithms [random forest (RF), support vector machine (SVM), dense neural network (DNN), and others] was applied. RESULTS: The Xception model classified the N, PPG, and G groups with 93.9% of the micro-average area under the receiver operating characteristic curve (AUROC) with tenfold cross-validation. Although normal and glaucoma sensitivity can reach 93.51% and 86.13% respectively, the PPG sensitivity was only 30.27%. The AUROC increased to 96.4% in the N + PPG and G groups. The multimodal model with the N + PPG and G groups showed that the AUROCs of RF, SVM, and DNN were 99.56%, 99.59%, and 99.10%, respectively; The N and PPG + G groups had less than 1% difference. The test set showed an overall 3%-5% less AUROC than the validation results. CONCLUSION: The multimodal model had good AUROC while detecting glaucoma in a population with high incidence of myopia. The model shows the potential for general automatic screening and telemedicine, especially in Asia. TRIAL REGISTRATION: The study was approved by the Institutional Review Board of the National Taiwan University Hospital, Hsinchu Branch (no. NTUHHCB 108-025-E).

The Prevalence and Impact of Fake News on COVID-19 Vaccination in Taiwan: Retrospective Study of Digital Media.

BACKGROUND: Vaccination is an important intervention to prevent the incidence and spread of serious diseases. Many factors including information obtained from the internet influence individuals' decisions to vaccinate. Misinformation is a critical issue and can be hard to detect, although it can change people's minds, opinions, and decisions. The impact of misinformation on public health and vaccination hesitancy is well documented, but little research has been conducted on the relationship between the size of the population reached by misinformation and the vaccination decisions made by that population. A number of fact-checking services are available on the web, including the Islander news analysis system, a free web service that provides individuals with real-time judgment on web news. In this study, we used such services to estimate the amount of fake news available and used Google Trends levels to model the spread of fake news. We quantified this relationship using official public data on COVID-19 vaccination in Taiwan. OBJECTIVE: In this study, we aimed to quantify the impact of the magnitude of the propagation of fake news on vaccination decisions. METHODS: We collected public data about COVID-19 infections and vaccination from Taiwan's official website and estimated the popularity of searches using Google Trends. We indirectly collected news from 26 digital media sources, using the news database of the Islander system. This system crawls the internet in real time, analyzes the news, and stores it. The incitement and suspicion scores of the Islander system were used to objectively judge news, and a fake news percentage variable was produced. We used multivariable linear regression, chi-square tests, and the Johnson-Neyman procedure to analyze this relationship, using weekly data. RESULTS: A total of 791,183 news items were obtained over 43 weeks in 2021. There was a significant increase in the proportion of fake news in 11 of the 26 media sources during the public vaccination stage. The regression model revealed a positive adjusted coefficient (ß=0.98, P=.002) of vaccine availability on the following week's vaccination doses, and a negative adjusted coefficient (ß=-3.21, P=.04) of the interaction term on the fake news percentage with the Google Trends level. The Johnson-Neiman plot of the adjusted effect for the interaction term showed that the Google Trends level had a significant negative adjustment effect on vaccination doses for the following week when the proportion of fake news exceeded 39.3%. CONCLUSIONS: There was a significant relationship between the amount of fake news to which the population was exposed and the number of vaccination doses administered. Reducing the amount of fake news and increasing public immunity to misinformation will be critical to maintain public health in the internet age.

A pressure ulcers assessment system for diagnosis and decision making using convolutional neural networks.

BACKGROUND/PURPOSE: Pressure ulcers are a common problem in hospital care and long-term care. Pressure ulcers are caused by prolonged compression of soft tissues, which can cause local tissue damage and even lead to serious infections. This study uses a deep learning algorithm to construct a system that diagnoses pressure ulcers and assists in making treatment decisions, thus providing additional reference for first-line caregivers. METHODS: We performed a retrospective research of medical records to find photos of patients with pressure ulcers at National Taiwan University Hospital from 2016 to 2020. We used photos from 2016 to 2019 for training and after removing the photos which were vague, underexposed, or overexposed, 327 photos were obtained. The photos were then labeled as "erythema" or "non-erythema" for the first classification task and "extensive necrosis", "moderate necrosis" or "limited necrosis" for the second, by consensus of three recruited physicians. An Inception-ResNet-v2 model, a kind of Convolutional Neural Network (CNN), was applied for training these two classification tasks to construct an assessment system. Finally, we tested the model with the photos of pressure ulcers taken from 2019 to 2020 to verify its accuracy. RESULTS: For the task of classification of erythema and non-erythema wounds, our CNN model achieved an accuracy of about 98.5%. For the task of classification of necrotic tissue, our model achieved accuracy of about 97%. CONCLUSION: Our CNN model, which was based on Inception-ResNet-v2, achieved high accuracy when classifying different types of pressure ulcers, making it applicable in clinical circumstances.

Clinical characteristics of influenza with or without Streptococcus pneumoniae co-infection in children.

BACKGROUND/PURPOSE: Influenza is frequently complicated with bacterial co-infection. This study aimed to disclose the significance of Streptococcus pneumoniae co-infection in children with influenza. METHODS: We retrospectively reviewed medical records of pediatric patients hospitalized for influenza with or without pneumococcal co-infection at the National Taiwan University Hospital from 2007 to 2019. Clinical characteristics and outcomes were compared between patients with and without S. pneumoniae co-infection. RESULTS: There were 558 children hospitalized for influenza: 494 had influenza alone whereas 64 had S. pneumoniae co-infection. Patients with S. pneumoniae co-infection had older ages, lower SpO2, higher C-Reactive Protein (CRP), lower serum sodium, lower platelet counts, more chest radiograph findings of patch and consolidation on admission, longer hospitalization, more intensive care, longer intensive care unit (ICU) stay, more mechanical ventilation, more inotropes/vasopressors use, more surgical interventions including video-assisted thoracoscopic surgery (VATS) and extracorporeal membrane oxygenation (ECMO), and higher case-fatality rate. CONCLUSION: Compared to influenza alone, patients with S. pneumoniae co-infection had more morbidities and mortalities. Pneumococcal co-infection is considered when influenza patients have lower SpO2, lower platelet counts, higher CRP, lower serum sodium, and more radiographic patches and consolidations on admission.

Clinical characteristics of recurrent pneumonia in children with or without underlying diseases.

BACKGROUND: Recurrent pneumonia is uncommon in children and few studies investigate the clinical impact of underlying diseases on this issue. This study aimed to explore the difference in clinical manifestations, pathogens, and prognosis of recurrent pneumonia in children with or without underlying diseases. METHODS: We conducted a retrospective study of pediatric recurrent pneumonia from 2007 to 2019 in National Taiwan University Hospital. Patients under the age of 18 who had two or more episodes of pneumonia in a year were included, and the minimum interval of two pneumonia episodes was more than one month. Aspiration pneumonia was excluded. Demographic and clinical characteristics of patients were collected and compared. RESULTS: Among 8508 children with pneumonia, 802 (9.4%) of them had recurrent pneumonia. Among these 802 patients, 655 (81.7%) had underlying diseases including neurological disorders (N = 252, 38.5%), allergy (N = 211, 32.2%), and cardiovascular diseases (N = 193, 29.5%). Children without underlying diseases had more viral bronchopneumonia (p < 0.001). Children with underlying diseases were more likely to acquire Staphylococcus aureus (p = 0.001), and gram-negative bacteriae, more pneumonia episodes (3 vs 2, p < 0.001), a longer hospital stay (median: 7 vs. 4 days, p < 0.001), a higher ICU rate (28.8% vs 3.59%, p < 0.001), and a higher case-fatality rate (5.19% vs 0%, p < 0.001) than those without underlying diseases. CONCLUSION: Children with underlying diseases, prone to have recurrent pneumonia and more susceptible to resistant microorganisms, had more severe diseases and poorer clinical outcomes. Therefore, more attention may be paid on clinical severity and the therapeutic plan.

RSV pneumonia with or without bacterial co-infection among healthy children.

BACKGROUND: Respiratory syncytial virus (RSV) is a common cause of childhood pneumonia, but there is limited understanding of whether bacterial co-infections affect clinical severity. METHODS: We conducted a retrospective cohort study at National Taiwan University Hospital from 2010 to 2019 to compare clinical characteristics and outcomes between RSV with and without bacterial co-infection in children without underlying diseases, including length of hospital stay, intensive care unit (ICU) admission, ventilator use, and death. RESULTS: Among 620 inpatients with RSV pneumonia, the median age was 1.33 months (interquartile range, 0.67-2 years); 239 (38.6%) under 1 year old; 366 (59.0%) males; 201 (32.4%) co-infected with bacteria. The three most common bacteria are Streptococcus pneumoniae, Staphylococcus aureus and Haemophilus influenzae. The annually seasonal analysis showed that spring and autumn were peak seasons, and September was the peak month. Compared with single RSV infection, children with bacterial co-infection were younger (p = 0.021), had longer hospital stay (p < 0.001), needed more ICU care (p = 0.02), had higher levels of C-reactive protein (p = 0.009) and more frequent hyponatremia (p = 0.013). Overall, younger age, bacterial co-infection (especially S. aureus), thrombocytosis, and lower hemoglobin level were associated with the risk of requiring ICU care. CONCLUSION: RSV related bacterial co-infections were not uncommon and assoicated with ICU admission, especially for young children, and more attention should be given. For empirical antibacterial treatment, high-dose amoxicillin-clavulanic acid or ampicillin-sulbactam was recommended for non-severe cases; vancomycin and third-generation cephalosporins were suggested for critically ill patients requiring ICU care.

The intracranial tumor segmentation challenge: Contour tumors on brain MRI for radiosurgery.

We report the set-up of the Intracranial Tumor Segmentation (ICTS) dataset. This dataset was retrieved from clinical work of radiosurgery, contoured by qualified neurosurgeons and radiation oncologists. It contains contrast-enhanced T1-weighted images of 1500 patients, together with the labels of tumors to be treated. The ICTS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.

Disease Concept-Embedding Based on the Self-Supervised Method for Medical Information Extraction from Electronic Health Records and Disease Retrieval: Algorithm Development and Validation Study.

BACKGROUND: The electronic health record (EHR) contains a wealth of medical information. An organized EHR can greatly help doctors treat patients. In some cases, only limited patient information is collected to help doctors make treatment decisions. Because EHRs can serve as a reference for this limited information, doctors' treatment capabilities can be enhanced. Natural language processing and deep learning methods can help organize and translate EHR information into medical knowledge and experience. OBJECTIVE: In this study, we aimed to create a model to extract concept embeddings from EHRs for disease pattern retrieval and further classification tasks. METHODS: We collected 1,040,989 emergency department visits from the National Taiwan University Hospital Integrated Medical Database and 305,897 samples from the National Hospital and Ambulatory Medical Care Survey Emergency Department data. After data cleansing and preprocessing, the data sets were divided into training, validation, and test sets. We proposed a Transformer-based model to embed EHRs and used Bidirectional Encoder Representations from Transformers (BERT) to extract features from free text and concatenate features with structural data as input to our proposed model. Then, Deep InfoMax (DIM) and Simple Contrastive Learning of Visual Representations (SimCLR) were used for the unsupervised embedding of the disease concept. The pretrained disease concept-embedding model, named EDisease, was further finetuned to adapt to the critical care outcome prediction task. We evaluated the performance of embedding using t-distributed stochastic neighbor embedding (t-SNE) to perform dimension reduction for visualization. The performance of the finetuned predictive model was evaluated against published models using the area under the receiver operating characteristic (AUROC). RESULTS: The performance of our model on the outcome prediction had the highest AUROC of 0.876. In the ablation study, the use of a smaller data set or fewer unsupervised methods for pretraining deteriorated the prediction performance. The AUROCs were 0.857, 0.870, and 0.868 for the model without pretraining, the model pretrained by only SimCLR, and the model pretrained by only DIM, respectively. On the smaller finetuning set, the AUROC was 0.815 for the proposed model. CONCLUSIONS: Through contrastive learning methods, disease concepts can be embedded meaningfully. Moreover, these methods can be used for disease retrieval tasks to enhance clinical practice capabilities. The disease concept model is also suitable as a pretrained model for subsequent prediction tasks.

Artificial Intelligence-Assisted Early Detection of Retinitis Pigmentosa - the Most Common Inherited Retinal Degeneration.

The purpose of this study was to detect the presence of retinitis pigmentosa (RP) based on color fundus photographs using a deep learning model. A total of 1670 color fundus photographs from the Taiwan inherited retinal degeneration project and National Taiwan University Hospital were acquired and preprocessed. The fundus photographs were labeled RP or normal and divided into training and validation datasets (n = 1284) and a test dataset (n = 386). Three transfer learning models based on pre-trained Inception V3, Inception Resnet V2, and Xception deep learning architectures, respectively, were developed to classify the presence of RP on fundus images. The model sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve were compared. The results from the best transfer learning model were compared with the reading results of two general ophthalmologists, one retinal specialist, and one specialist in retina and inherited retinal degenerations. A total of 935 RP and 324 normal images were used to train the models. The test dataset consisted of 193 RP and 193 normal images. Among the three transfer learning models evaluated, the Xception model had the best performance, achieving an AUROC of 96.74%. Gradient-weighted class activation mapping indicated that the contrast between the periphery and the macula on fundus photographs was an important feature in detecting RP. False-positive results were mostly obtained in cases of high myopia with highly tessellated retina, and false-negative results were mostly obtained in cases of unclear media, such as cataract, that led to a decrease in the contrast between the peripheral retina and the macula. Our model demonstrated the highest accuracy of 96.00%, which was comparable with the average results of 81.50%, of the other four ophthalmologists. Moreover, the accuracy was obtained at the same level of sensitivity (95.71%), as compared to an inherited retinal disease specialist. RP is an important disease, but its early and precise diagnosis is challenging. We developed and evaluated a transfer-learning-based model to detect RP from color fundus photographs. The results of this study validate the utility of deep learning in automating the identification of RP from fundus photographs.

Web-Based Dashboard for the Interactive Visualization and Analysis of National Risk-Standardized Mortality Rates of Sepsis in the US.

Sepsis mortality is heavily influenced by the quality of care in hospitals. Comparing risk-standardized mortality rate (RSMR) of sepsis patients in different states in the United States has potentially important clinical and policy implications. In the current study, we aimed to compare national sepsis RSMR using an interactive web-based dashboard. We analyzed sepsis mortality using the National Inpatient Sample Database of the US. The RSMR was calculated by the hierarchical logistic regression model. We wrote the interactive web-based dashboard using the Shiny framework, an R package that integrates R-based statistics computation and graphics generation. Visual summarizations (e.g., heat map, and time series chart), and interactive tools (e.g., year selection, automatic year play, map zoom, copy or print data, ranking data by name or value, and data search) were implemented to enhance user experience. The web-based dashboard (https://sepsismap.shinyapps.io/index2/) is cross-platform and publicly available to anyone with interest in sepsis outcomes, health inequality, and administration of state/federal healthcare. After extrapolation to the national level, approximately 35 million hospitalizations were analyzed for sepsis mortality each year. Eight years of sepsis mortality data were summarized into four easy to understand dimensions: Sepsis Identification Criteria; Sepsis Mortality Predictors; RSMR Map; RSMR Trend. Substantial variation in RSMR was observed for different states in the US. This web-based dashboard allows anyone to visualize the substantial variation in RSMR across the whole US. Our work has the potential to support healthcare transparency, information diffusion, health decision-making, and the formulation of new public policies.

Metformin use mitigates the adverse prognostic effect of diabetes mellitus in chronic obstructive pulmonary disease.

BACKGROUND AND OBJECTIVE: Among patients with chronic obstructive pulmonary disease (COPD), diabetes mellitus (DM) is a common comorbidity and is probably associated with increased systemic inflammation and worse prognosis. Metformin, with its pleiotropic anti-inflammatory and antioxidant actions, may offer theoretical benefits in COPD patients with DM. Thus, this study aimed to investigate the effects of DM and metformin use on mortality in the clinical trajectory of COPD. METHODS: This was a retrospective cohort study comprising patients with spirometry-confirmed COPD and an age of ≥40 years from 2008 to 2014. The primary outcome of interest was all-cause mortality. We evaluated the effects of DM on mortality through the clinical course of COPD and we also assessed the impact of metformin use on survival of the COPD population. RESULTS: Among 4231 COPD patients, 556 (13%) had DM, and these patients had 1.62 times higher hazards of 2-year mortality than those without DM (95% confidence interval [CI], 1.15-2.28) after adjusting for age, gender, COPD stage, comorbidities and prior COPD hospitalization. Over a 2-year period, metformin users had a significantly lower risk of death (hazard ratio, 0.46; 95% CI, 0.23-0.92) compared with non-metformin users in patients with coexistent COPD and DM. Moreover, metformin users had similar survival to COPD patients without DM. CONCLUSIONS: This study shows that DM is associated with an increased risk of death in COPD patients and metformin use seems to mitigate the hazard. Our findings suggest a potential role of metformin in the management of DM in COPD.

Critical Trio Exome Benefits In-Time Decision-Making for Pediatric Patients With Severe Illnesses.

OBJECTIVES: Critical illnesses caused by undiagnosed genetic conditions are challenging in PICUs. Whole-exome sequencing is a powerful diagnostic tool but usually costly and often fail to arrive at a final diagnosis in a short period. We assessed the feasibility of our whole-exome sequencing as a tool to improve the efficacy of rare diseases diagnosis for pediatric patients with severe illness. DESIGN: Observational analysis. METHOD: We employed a fast but standard whole-exome sequencing platform together with text mining-assisted variant prioritization in PICU setting over a 1-year period. SETTING: A tertiary referral Children's Hospital in Taiwan. PATIENTS: Critically ill PICU patients suspected of having a genetic disease and newborns who were suspected of having a serious genetic disease after newborn screening were enrolled. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Around 50,000 to 100,000 variants were obtained for each of the 40 patients in 5 days after blood sampling. Eleven patients were immediately found be affected by previously reported mutations after searching mutation databases. Another seven patients had a diagnosis among the top five in a list ranked by text mining. As a whole, 21 patients (52.5%) obtained a diagnosis in 6.2 ± 1.1 working days (range, 4.3-9 d). Most of the diagnoses were first recognized in Taiwan. Specific medications were recommended for 10 patients (10/21, 47.6%), transplantation was advised for five, and hospice care was suggested for two patients. Overall, clinical management was altered in time for 81.0% of patients who had a molecular diagnosis. CONCLUSIONS: The current whole-exome sequencing algorithm, balanced in cost and speed, uncovers genetic conditions in infants and children in PICU, which helps their managements in time and promotes better utilization of PICU resources.

Chronic wound assessment and infection detection method.

BACKGROUND: Numerous patients suffer from chronic wounds and wound infections nowadays. Until now, the care for wounds after surgery still remain a tedious and challenging work for the medical personnel and patients. As a result, with the help of the hand-held mobile devices, there is high demand for the development of a series of algorithms and related methods for wound infection early detection and wound self monitoring. METHODS: This research proposed an automated way to perform (1) wound image segmentation and (2) wound infection assessment after surgical operations. The first part describes an edge-based self-adaptive threshold detection image segmentation method to exclude nonwounded areas from the original images. The second part describes a wound infection assessment method based on machine learning approach. In this method, the extraction of feature points from the suture area and an optimal clustering method based on unimodal Rosin threshold algorithm that divides feature points into clusters are introduced. These clusters are then merged into several regions of interest (ROIs), each of which is regarded as a suture site. Notably, a support vector machine (SVM) can automatically interpret infections on these detected suture site. RESULTS: For (1) wound image segmentation, boundary-based evaluation were applied on 100 images with gold standard set up by three physicians. Overall, it achieves 76.44% true positive rate and 89.04% accuracy value. For (2) wound infection assessment, the results from a retrospective study using confirmed wound pictures from three physicians for the following four symptoms are presented: (1) Swelling, (2) Granulation, (3) Infection, and (4) Tissue Necrosis. Through cross-validation of 134 wound images, for anomaly detection, our classifiers achieved 87.31% accuracy value; for symptom assessment, our classifiers achieved 83.58% accuracy value. CONCLUSIONS: This augmentation mechanism has been demonstrated reliable enough to reduce the need for face-to-face diagnoses. To facilitate the use of this method and analytical framework, an automatic wound interpretation app and an accompanying website were developed. TRIAL REGISTRATION: 201505164RIND , 201803108RSB .

A novel depth estimation algorithm of chest compression for feedback of high-quality cardiopulmonary resuscitation based on a smartwatch.

INTRODUCTION: High-quality cardiopulmonary resuscitation (CPR) is a key factor affecting cardiac arrest survival. Accurate monitoring and real-time feedback are emphasized to improve CPR quality. The purpose of this study was to develop and validate a novel depth estimation algorithm based on a smartwatch equipped with a built-in accelerometer for feedback instructions during CPR. METHODS: For data collection and model building, researchers wore an Android Wear smartwatch and performed chest compression-only CPR on a Resusci Anne QCPR training manikin. We developed an algorithm based on the assumptions that (1) maximal acceleration measured by the smartwatch accelerometer and the chest compression depth (CCD) are positively correlated and (2) the magnitude of acceleration at a specific time point and interval is correlated with its neighboring points. We defined a statistic value M as a function of time and the magnitude of maximal acceleration. We labeled and processed collected data and determined the relationship between M value, compression rate and CCD. We built a model accordingly, and developed a smartwatch app capable of detecting CCD. For validation, researchers wore a smartwatch with the preinstalled app and performed chest compression-only CPR on the manikin at target sessions. We compared the CCD results given by the smartwatch and the reference using the Wilcoxon Signed Rank Test (WSRT), and used Bland-Altman (BA) analysis to assess the agreement between the two methods. RESULTS: We analyzed a total of 3978 compressions that covered the target rate of 80-140/min and CCD of 4-7 cm. WSRT showed that there was no significant difference between the two methods (P = 0.084). By BA analysis the mean of differences was 0.003 and the bias between the two methods was not significant (95% CI: -0.079 to 0.085). CONCLUSION: Our study indicates that the algorithm developed for estimating CCD based on a smartwatch with a built-in accelerometer is promising. Further studies will be conducted to evaluate its application for CPR training and clinical practice.

Identifying Unmet Treatment Needs for Patients With Osteoporotic Fracture: Feasibility Study for an Electronic Clinical Surveillance System.

BACKGROUND: Traditional clinical surveillance relied on the results from clinical trials and observational studies of administrative databases. However, these studies not only required many valuable resources but also faced a very long time lag. OBJECTIVE: This study aimed to illustrate a practical application of the National Taiwan University Hospital Clinical Surveillance System (NCSS) in the identification of patients with an osteoporotic fracture and to provide a high reusability infrastructure for longitudinal clinical data. METHODS: The NCSS integrates electronic medical records in the National Taiwan University Hospital (NTUH) with a data warehouse and is equipped with a user-friendly interface. The NCSS was developed using professional insight from multidisciplinary experts, including clinical practitioners, epidemiologists, and biomedical engineers. The practical example identifying the unmet treatment needs for patients encountering major osteoporotic fractures described herein was mainly achieved by adopting the computerized workflow in the NCSS. RESULTS: We developed the infrastructure of the NCSS, including an integrated data warehouse and an automatic surveillance workflow. By applying the NCSS, we efficiently identified 2193 patients who were newly diagnosed with a hip or vertebral fracture between 2010 and 2014 at NTUH. By adopting the filter function, we identified 1808 (1808/2193, 82.44%) patients who continued their follow-up at NTUH, and 464 (464/2193, 21.16%) patients who were prescribed anti-osteoporosis medications, within 3 and 12 months post the index date of their fracture, respectively. CONCLUSIONS: The NCSS systems can integrate the workflow of cohort identification to accelerate the survey process of clinically relevant problems and provide decision support in the daily practice of clinical physicians, thereby making the benefit of evidence-based medicine a reality.

Confidentiality Protection of Digital Health Records in Cloud Computing.

Electronic medical records containing confidential information were uploaded to the cloud. The cloud allows medical crews to access and manage the data and integration of medical records easily. This data system provides relevant information to medical personnel and facilitates and improve electronic medical record management and data transmission. A structure of cloud-based and patient-centered personal health record (PHR) is proposed in this study. This technique helps patients to manage their health information, such as appointment date with doctor, health reports, and a completed understanding of their own health conditions. It will create patients a positive attitudes to maintain the health. The patients make decision on their own for those whom has access to their records over a specific span of time specified by the patients. Storing data in the cloud environment can reduce costs and enhance the share of information, but the potential threat of information security should be taken into consideration. This study is proposing the cloud-based secure transmission mechanism is suitable for multiple users (like nurse aides, patients, and family members).

Development of a decision support engine to assist patients with hospital selection.

Hospital selection is a complicated decision-making process. Although patients have expressed greater desire to participate in decision-makings of their healthcare, it can be problematic for them to accumulate large amount of information and using it for making an optimal choice in hospital selection. The aim of this research is to develop a decision engine for hospital selection (DEHS) to support patients while accessing healthcare resources. DEHS applied the analytic hierarchy process and the geographic information system to aggregate different decision factors and spatial information. The results were evaluated through investigating the consistency of the preferences that users inputted, the degree that the results match patient choices, the satisfactions of users, and the helpfulness of the results. Data were collected for 3 months. One hundred and four users visited DEHS and 85.5 % of them used DEHS more than once. Recommendations of the institutes (36 %) was ranked as the primary decision factor that most users concerned. Sixty-seven percent of the sessions searched for hospitals and 33 % for clinics. Eighty-eight percent of the results matched the choices of patients. Eighty-three percent of the users agreed that the suggested results were satisfactory, and 70 % agreed that the information were helpful. The DEHS provides the patients with simple measurements and individualized list of suggested medical institutes, and allows them to make decisions based on credible information and consults the experiences of others at the same time. The suggested results were considered satisfactory and helpful.