•Fatty liver disease (FLD) is a common clinical complication, is associated with high morbidity and mortality.•A machine learning model has been using to predict liver disease that could assist ...physicians in classifying high-risk patients and make a novel diagnosis.•The random forest model shows higher performance than other classification models.
Fatty liver disease (FLD) is a common clinical complication; it is associated with high morbidity and mortality. However, an early prediction of FLD patients provides an opportunity to make an appropriate strategy for prevention, early diagnosis and treatment. We aimed to develop a machine learning model to predict FLD that could assist physicians in classifying high-risk patients and make a novel diagnosis, prevent and manage FLD.
We included all patients who had an initial fatty liver screening at the New Taipei City Hospital between 1st and 31st December 2009. Classification models such as random forest (RF), Naïve Bayes (NB), artificial neural networks (ANN), and logistic regression (LR) were developed to predict FLD. The area under the receiver operating characteristic curve (ROC) was used to evaluate performances among the four models.
A total of 577 patients were included in this study; of those 377 patients had fatty liver. The area under the receiver operating characteristic (AUROC) of RF, NB, ANN, and LR with 10 fold-cross validation was 0.925, 0.888, 0.895, and 0.854 respectively. Additionally, The accuracy of RF, NB, ANN, and LR 87.48, 82.65, 81.85, and 76.96%.
In this study, we developed and compared the four classification models to predict fatty liver disease accurately. However, the random forest model showed higher performance than other classification models. Implementation of a random forest model in the clinical setting could help physicians to stratify fatty liver patients for primary prevention, surveillance, early treatment, and management.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•A pandemic situation may increase public awareness to take necessary precautions.•The Taiwan government encouraged the use of face masks and sanitizer, as well as social distancing as a part of ...prevention during the COVID-19 outbreak.•This response may have contributed to a decline in other infectious diseases.
Most of the communicable diseases have contact, airborne and/or droplet mode of transsmission. Following the outbreak of COVID-19, the Taiwan government implemented the use of masks and sanitizer, as well as other preventive measures like social distancing for prevention. This public response likely contributed significantly to the decline in the outbreak of other infectious diseases.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Diabetic retinopathy (DR) is one of the leading causes of blindness globally. Detecting and treating DR at an earlier stage is desirable to reduce the incidence and progression of visual loss.•A ...systematic review with a meta-analysis of relevant studies was performed to quantify the performance of DL algorithms to detect DR.•The pooled area under the receiving operating curve (AUROC) of DR was 0.97 (95%CI: 0.95–0.98), sensitivity was 0.83 (95%CI: 0.83–0.83), and specificity was 0.92 (95%CI: 0.92–0.92).•The findings of our study showed that DL-algorithms had high sensitivity and specificity for detecting referable DR from retinal fundus photographs.
Diabetic retinopathy (DR) is one of the leading causes of blindness globally. Earlier detection and timely treatment of DR are desirable to reduce the incidence and progression of vision loss. Currently, deep learning (DL) approaches have offered better performance in detecting DR from retinal fundus images. We, therefore, performed a systematic review with a meta-analysis of relevant studies to quantify the performance of DL algorithms for detecting DR.
A systematic literature search on EMBASE, PubMed, Google Scholar, Scopus was performed between January 1, 2000, and March 31, 2019. The search strategy was based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines, and DL-based study design was mandatory for articles inclusion. Two independent authors screened abstracts and titles against inclusion and exclusion criteria. Data were extracted by two authors independently using a standard form and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used for the risk of bias and applicability assessment.
Twenty-three studies were included in the systematic review; 20 studies met inclusion criteria for the meta-analysis. The pooled area under the receiving operating curve (AUROC) of DR was 0.97 (95%CI: 0.95–0.98), sensitivity was 0.83 (95%CI: 0.83–0.83), and specificity was 0.92 (95%CI: 0.92–0.92). The positive- and negative-likelihood ratio were 14.11 (95%CI: 9.91–20.07), and 0.10 (95%CI: 0.07–0.16), respectively. Moreover, the diagnostic odds ratio for DL models was 136.83 (95%CI: 79.03–236.93). All the studies provided a DR-grading scale, a human grader (e.g. trained caregivers, ophthalmologists) as a reference standard.
The findings of our study showed that DL algorithms had high sensitivity and specificity for detecting referable DR from retinal fundus photographs. Applying a DL-based automated tool of assessing DR from color fundus images could provide an alternative solution to reduce misdiagnosis and improve workflow. A DL-based automated tool offers substantial benefits to reduce screening costs, accessibility to healthcare and ameliorate earlier treatments.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Digital-assisted DSMES is effective in improving glycemic control for Type 2 diabetes and knowledge•No evidence of effectiveness for HrQoL.•Choice of digital tool and engagement of patients are ...essential factors to consider when implementing interventions.•Evidence is required for newly diagnosed as well as African and South American patients.
Objective: To describe and assess digital health-led diabetes self-management education and support (DSMES) effectiveness in improving glycosylated hemoglobin, diabetes knowledge, and health-related quality of life (HrQoL) of Type 1 and 2 Diabetes in the past 10 years.
Design: Systematic Review and Meta-Analysis. The protocol was registered on PROSPERO registration number CRD42019139884.
Data Sources: PubMed, EMBASE, Cochrane library, Web of Science, and Scopus between January 2010 and August 2019.
Study Selection and Appraisal: Randomized control trials of digital health-led DSMES for Type 1 (T1DM) or 2 (T2DM) diabetes compared to usual care were included. Outcomes were change in HbA1c, diabetes knowledge, and HrQoL. Cochrane Risk of Bias 2.0 tool was used to assess bias and GRADEpro for overall quality. The analysis involved narrative synthesis, subgroup and pooled meta-analyses.
Results: From 4286 articles, 39 studies (6861 participants) were included. Mean age was 51.62 years, range (13–70). Meta-analysis revealed intervention effects on HbA1c for T2DM with difference in means (MD) from baseline -0.480% (-0.661, -0.299), I275% (6 months), -0.457% (-0.761, -0.151), I2 81% (12 months), and for T1DM -0.41% (-1.022, 0.208) I2 83% (6 months), -0.03% (-0.210, 0.142) I2 0% (12 months). Few reported HrQoL with Hedges’ g 0.183 (-0.039, 0.405), I2 0% (6 months), 0.153 (-0.060, 0.366), I2 0% (12 months) and diabetes knowledge with Hedges’ g 1.003 (0.068, 1.938), I2 87% (3 months).
Conclusion: Digital health-led DSMES are effective in improving HbA1c and diabetes knowledge, notably for T2DM. Research shows non-significant changes in HrQoL. Intervention effect on HbA1c was more impressive if delivered through mobile apps or patient portals. Further research is needed on the impact of DSMES on these outcomes, especially for newly diagnosed diabetes patients.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Background
Artificial intelligence approaches can integrate complex features and can be used to predict a patient’s risk of developing lung cancer, thereby decreasing the need for unnecessary and ...expensive diagnostic interventions.
Objective
The aim of this study was to use electronic medical records to prescreen patients who are at risk of developing lung cancer.
Methods
We randomly selected 2 million participants from the Taiwan National Health Insurance Research Database who received care between 1999 and 2013. We built a predictive lung cancer screening model with neural networks that were trained and validated using pre-2012 data, and we tested the model prospectively on post-2012 data. An age- and gender-matched subgroup that was 10 times larger than the original lung cancer group was used to assess the predictive power of the electronic medical record. Discrimination (area under the receiver operating characteristic curve AUC) and calibration analyses were performed.
Results
The analysis included 11,617 patients with lung cancer and 1,423,154 control patients. The model achieved AUCs of 0.90 for the overall population and 0.87 in patients ≥55 years of age. The AUC in the matched subgroup was 0.82. The positive predictive value was highest (14.3%) among people aged ≥55 years with a pre-existing history of lung disease.
Conclusions
Our model achieved excellent performance in predicting lung cancer within 1 year and has potential to be deployed for digital patient screening. Convolution neural networks facilitate the effective use of EMRs to identify individuals at high risk for developing lung cancer.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The current Objective Structured Clinical Examination (OSCE) is complex, costly, and difficult to provide high-quality assessments. This pilot study employed a focus group and debugging stage to test ...the Crowdsource Authoring Assessment Tool (CAAT) for the creation and sharing of assessment tools used in editing and customizing, to match specific users' needs, and to provide higher-quality checklists. Competency assessment international experts (n = 50) were asked to 1) participate in and experience the CAAT system when editing their own checklist, 2) edit a urinary catheterization checklist using CAAT, and 3) complete a Technology Acceptance Model (TAM) questionnaire consisting of 14 items to evaluate its four domains. The study occurred between October 2018 and May 2019. The median time for developing a new checklist using the CAAT was 65.76 minutes whereas the traditional method required 167.90 minutes. The CAAT system enabled quicker checklist creation and editing regardless of the experience and native language of participants. Participants also expressed the CAAT enhanced checklist development with 96% of them willing to recommend this tool to others. The use of a crowdsource authoring tool as revealed by this study has efficiently reduced the time to almost a third it would take when using the traditional method. In addition, it allows collaborations to partake on a simple platform which also promotes contributions in checklist creation, editing, and rating.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Statins have been shown to be a beneficial treatment as chemotherapy and target therapy for lung cancer. This study aimed to investigate the effectiveness of statins in combination with epidermal ...growth factor receptor‐tyrosine kinase inhibitor therapy for the resistance and mortality of lung cancer patients. A population‐based cohort study was conducted using the Taiwan Cancer Registry database. From January 1, 2007, to December 31, 2012, in total 792 non‐statins and 41 statins users who had undergone EGFR‐TKIs treatment were included in this study. All patients were monitored until the event of death or when changed to another therapy. Kaplan‐Meier estimators and Cox proportional hazards regression models were used to calculate overall survival. We found that the mortality was significantly lower in patients in the statins group compared with patients in the non‐statins group (4‐y cumulative mortality, 77.3%; 95% confidence interval (CI), 36.6%‐81.4% vs. 85.5%; 95% CI, 78.5%‐98%; P = .004). Statin use was associated with a reduced risk of death in patients the group who had tumor sizes <3 cm (hazard ratio HR, 0.51, 95% CI, 0.29‐0.89) and for patients in the group who had CCI scores <3 (HR, 0.6; 95% CI, 0.41‐0.88; P = .009). In our study, statins were found to be associated with prolonged survival time in patients with lung cancer who were treated with EGFR‐TKIs and played a synergistic anticancer role.
In this article, we describe the use of statins on the beneficial mortality of lung cancer patients with EGFR‐TKIs therapy. We found that statins were associated with prolonged survival time in patients with lung cancer who were taking EGFR‐TKIs. These could be playing a synergic anticancer role during the TKI treatment period, as well as improving quality of life worldwide and medical practice overall.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
•The use of artificial intelligence in diabetic retinopathy has become a popular research focus in the past decade.•Conducted a bibliometric approach to identify and analyse the academic literature ...on artificial intelligence in diabetic retinopathy.•This study summarizes the recent advances in artificial intelligence technology on diabetic retinopathy research and sheds light on the emerging trends, sources, and leading institutions.
The use of artificial intelligence in diabetic retinopathy has become a popular research focus in the past decade. However, no scientometric report has provided a systematic overview of this scientific area.
We utilized a bibliometric approach to identify and analyse the academic literature on artificial intelligence in diabetic retinopathy and explore emerging research trends, key authors, co-authorship networks, institutions, countries, and journals. We further captured the diabetic retinopathy conditions and technology commonly used within this area.
Web of Science was used to collect relevant articles on artificial intelligence use in diabetic retinopathy published between January 1, 2012, and December 31, 2022 . All the retrieved titles were screened for eligibility, with one criterion that they must be in English. All the bibliographic information was extracted and used to perform a descriptive analysis. Bibliometrix (R tool) and VOSviewer (Leiden University) were used to construct and visualize the annual numbers of publications, journals, authors, countries, institutions, collaboration networks, keywords, and references.
In total, 931 articles that met the criteria were collected. The number of annual publications showed an increasing trend over the last ten years. Investigative Ophthalmology & Visual Science (58/931), IEEE Access (54/931), and Computers in Biology and Medicine (23/931) were the most journals with most publications. China (211/931), India (143/931, USA (133/931), and South Korea (44/931) were the most productive countries of origin. The National University of Singapore (40/931), Singapore Eye Research Institute (35/931), and Johns Hopkins University (34/931) were the most productive institutions. Ting D. (34/931), Wong T. (28/931), and Tan G. (17/931) were the most productive researchers.
This study summarizes the recent advances in artificial intelligence technology on diabetic retinopathy research and sheds light on the emerging trends, sources, leading institutions, and hot topics through bibliometric analysis and network visualization. Although this field has already shown great potential in health care, our findings will provide valuable clues relevant to future research directions and clinical practice.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Malignant melanoma is a complex malignancy with significant morbidity and mortality. The incidence continues to rise, and despite advances in treatment, the prognosis is poor. Thus, it is necessary ...to develop novel strategies to treat this aggressive cancer. Synthetic cannabinoids have been implicated in inhibiting cancer cell proliferation, reducing tumor growth, and reducing metastasis. We developed a unique study focusing on the effects of treatment with a cannabinoid derivative on malignant melanoma tumors in a murine model.
Murine B16F10 melanoma tumors were established subcutaneously in C57BL/6 mice. Mice were then treated with intraperitoneal injections of vehicle twice per week (control—group 1, n = 6), Cisplatin 5 mg/kg/wk (group 2; n = 6), and Cannabidiol (CBD) 5 mg/kg twice per week (group 3; n = 6). Tumors were measured and volume calculated as (4π/3) × (width/2)2 × (length/2). Tumor size and survival curves were measured. Results were compared using a one-way ANOVA with multiple comparison test.
A significant decrease in tumor size was detected in mice treated with CBD when compared with the control group (P = 0.01). The survival curve of melanoma tumors treated with CBD increased when compared with the control group and was statistically significant (P = 0.04). The growth curve and survival curve of melanoma tumors treated with Cisplatin were significantly decreased and increased, respectively, when compared with the control and CBD-treated groups. Mice treated with Cisplatin demonstrated the longest survival time, but the quality of life and movement of CBD-treated mice were observed to be better.
We demonstrate a potential beneficial therapeutic effect of cannabinoids, which could influence the course of melanoma in a murine model. Increased survival and less tumorgenicity are novel findings that should guide research to better understand the mechanisms by which cannabinoids could be utilized as adjunctive treatment of cancer, specifically melanoma. Further studies are necessary to evaluate this potentially new and novel treatment of malignant melanoma.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Many important events occur at birth. The fetus is suddenly removed from a protected intra-uterine environment that is aquatic, warm, and nearly sterile, to the dry, cold external world laden with ...microbes. To survive, the neonate must interact with many organisms, making use of some, while vigorously defending against the others like a nation conducting trade with friendly countries and guarding against hostile ones from invading it, waging wars if necessary. Although, the neonatal immune system is plastic, however, it is highly tolerant which is due to both the fetal development during gestation as well as significant sudden changes in fetal environment and enormous exposure to the new antigens and intestinal bacteria and their products. This "quiescent mode" of innate immune system is part of a highly regulated process to fulfill all requirements of multi-layered process of early life, implemented effectively through the cells of innate immune system. While, most of the neonatal innate immune cells (e.g., neutrophils and monocytes) present contained activity and lower frequencies compared to their adult counterparts, innate lymphoid cells (ILCs), a distinct cellular component of innate immunity, show higher level of activity and presence during period of infancy compared to later stages of life and adulthood, which may suggest a role for ILCs in variable susceptibility to certain conditions during life time. In this review, while we focus on the characteristics and status of ILCs in neonatal immune system, we also draw an analogy from a national defense perspective because of the great similarities between that and the immune system by providing the known biological counterparts of all five core operational elements, the five Ds of defense, detection, discrimination, deployment, destruction, and de-escalation, with special focus on innate immunity, maternal support, and influence during the neonatal and infancy periods.
PDF
