Heterogeneity of Alzheimer's disease identified by neuropsychological test profiling.

Alzheimer's disease (AD) is a highly heterogeneous disorder. Untangling this variability could lead to personalized treatments and improve participant recruitment for clinical trials. We investigated the cognitive subgroups by using a data-driven clustering technique in an AD cohort. People with mild-moderate probable AD from Taiwan was included. Neuropsychological test results from the Cognitive Abilities Screening Instrument were clustered using nonnegative matrix factorization. We identified two clusters in 112 patients with predominant deficits in memory (62.5%) and non-memory (37.5%) cognitive domains, respectively. The memory group performed worse in short-term memory and orientation and better in attention than the non-memory group. At baseline, patients in the memory group had worse global cognitive status and dementia severity. Linear mixed effect model did not reveal difference in disease trajectory within 3 years of follow-up between the two clusters. Our results provide insights into the cognitive heterogeneity in probable AD in an Asian population.

Applying Deep Learning Model to Predict Diagnosis Code of Medical Records.

The International Classification of Diseases (ICD) code is a diagnostic classification standard that is frequently used as a referencing system in healthcare and insurance. However, it takes time and effort to find and use the right diagnosis code based on a patient's medical records. In response, deep learning (DL) methods have been developed to assist physicians in the ICD coding process. Our findings propose a deep learning model that utilized clinical notes from medical records to predict ICD-10 codes. Our research used text-based medical data from the outpatient department (OPD) of a university hospital from January to December 2016. The dataset used clinical notes from five departments, and a total of 21,953 medical records were collected. Clinical notes consisted of a subjective component, objective component, assessment, plan (SOAP) notes, diagnosis code, and a drug list. The dataset was divided into two groups: 90% for training and 10% for test cases. We applied natural language processing (NLP) technique (word embedding, Word2Vector) to process the data. A deep learning-based convolutional neural network (CNN) model was created based on the information presented above. Three metrics (precision, recall, and F-score) were used to calculate the achievement of the deep learning CNN model. Clinically acceptable results were achieved through the deep learning model for five departments (precision: 0.53-0.96; recall: 0.85-0.99; and F-score: 0.65-0.98). With a precision of 0.95, a recall of 0.99, and an F-score of 0.98, the deep learning model performed the best in the department of cardiology. Our proposed CNN model significantly improved the prediction performance for an automated ICD-10 code prediction system based on prior clinical information. This CNN model could reduce the laborious task of manual coding and could assist physicians in making a better diagnosis.

Deep Learning for the Diagnosis of Esophageal Cancer in Endoscopic Images: A Systematic Review and Meta-Analysis.

Esophageal cancer, one of the most common cancers with a poor prognosis, is the sixth leading cause of cancer-related mortality worldwide. Early and accurate diagnosis of esophageal cancer, thus, plays a vital role in choosing the appropriate treatment plan for patients and increasing their survival rate. However, an accurate diagnosis of esophageal cancer requires substantial expertise and experience. Nowadays, the deep learning (DL) model for the diagnosis of esophageal cancer has shown promising performance. Therefore, we conducted an updated meta-analysis to determine the diagnostic accuracy of the DL model for the diagnosis of esophageal cancer. A search of PubMed, EMBASE, Scopus, and Web of Science, between 1 January 2012 and 1 August 2022, was conducted to identify potential studies evaluating the diagnostic performance of the DL model for esophageal cancer using endoscopic images. The study was performed in accordance with PRISMA guidelines. Two reviewers independently assessed potential studies for inclusion and extracted data from retrieved studies. Methodological quality was assessed by using the QUADAS-2 guidelines. The pooled accuracy, sensitivity, specificity, positive and negative predictive value, and the area under the receiver operating curve (AUROC) were calculated using a random effect model. A total of 28 potential studies involving a total of 703,006 images were included. The pooled accuracy, sensitivity, specificity, and positive and negative predictive value of DL for the diagnosis of esophageal cancer were 92.90%, 93.80%, 91.73%, 93.62%, and 91.97%, respectively. The pooled AUROC of DL for the diagnosis of esophageal cancer was 0.96. Furthermore, there was no publication bias among the studies. The findings of our study show that the DL model has great potential to accurately and quickly diagnose esophageal cancer. However, most studies developed their model using endoscopic data from the Asian population. Therefore, we recommend further validation through studies of other populations as well.

Development and Validation of Machine Learning Models to Classify Artery Stenosis for Automated Generating Ultrasound Report.

Duplex ultrasonography (DUS) is a safe, non-invasive, and affordable primary screening tool to identify the vascular risk factors of stroke. The overall process of DUS examination involves a series of complex processes, such as identifying blood vessels, capturing the images of blood vessels, measuring the velocity of blood flow, and then physicians, according to the above information, determining the severity of artery stenosis for generating final ultrasound reports. Generation of transcranial doppler (TCD) and extracranial carotid doppler (ECCD) ultrasound reports involves a lot of manual review processes, which is time-consuming and makes it easy to make errors. Accurate classification of the severity of artery stenosis can provide an early opportunity for decision-making regarding the treatment of artery stenosis. Therefore, machine learning models were developed and validated for classifying artery stenosis severity based on hemodynamic features. This study collected data from all available cases and controlled at one academic teaching hospital in Taiwan between 1 June 2020, and 30 June 2020, from a university teaching hospital and reviewed all patients' medical records. Supervised machine learning models were developed to classify the severity of artery stenosis. The receiver operating characteristic curve, accuracy, sensitivity, specificity, and positive and negative predictive value were used for model performance evaluation. The performance of the random forest model was better compared to the logistic regression model. For ECCD reports, the accuracy of the random forest model to predict stenosis in various sites was between 0.85 and 1. For TCD reports, the overall accuracy of the random forest model to predict stenosis in various sites was between 0.67 and 0.86. The findings of our study suggest that a machine learning-based model accurately classifies artery stenosis, which indicates that the model has enormous potential to facilitate screening for artery stenosis.

Deep-ADCA: Development and Validation of Deep Learning Model for Automated Diagnosis Code Assignment Using Clinical Notes in Electronic Medical Records.

Currently, the International Classification of Diseases (ICD) codes are being used to improve clinical, financial, and administrative performance. Inaccurate ICD coding can lower the quality of care, and delay or prevent reimbursement. However, selecting the appropriate ICD code from a patient's clinical history is time-consuming and requires expert knowledge. The rapid spread of electronic medical records (EMRs) has generated a large amount of clinical data and provides an opportunity to predict ICD codes using deep learning models. The main objective of this study was to use a deep learning-based natural language processing (NLP) model to accurately predict ICD-10 codes, which could help providers to make better clinical decisions and improve their level of service. We retrospectively collected clinical notes from five outpatient departments (OPD) from one university teaching hospital between January 2016 and December 2016. We applied NLP techniques, including global vectors, word to vectors, and embedding techniques to process the data. The dataset was split into two independent training and testing datasets consisting of 90% and 10% of the entire dataset, respectively. A convolutional neural network (CNN) model was developed, and the performance was measured using the precision, recall, and F-score. A total of 21,953 medical records were collected from 5016 patients. The performance of the CNN model for the five different departments was clinically satisfactory (Precision: 0.50~0.69 and recall: 0.78~0.91). However, the CNN model achieved the best performance for the cardiology department, with a precision of 69%, a recall of 89% and an F-score of 78%. The CNN model for predicting ICD-10 codes provides an opportunity to improve the quality of care. Implementing this model in real-world clinical settings could reduce the manual coding workload, enhance the efficiency of clinical coding, and support physicians in making better clinical decisions.

Association between Statin Use and Risk of Parkinson's Disease: Evidence from 18 Observational Studies Comprising 3.7 Million Individuals.

The potential impact of statins on the risk of Parkinson's disease (PD) is still controversial; therefore, we conducted a comprehensive meta-analysis of observational studies to examine the effect of statin use on the risk of PD. We searched electronic databases, such as PubMed, EMBASE, Scopus, and Web of Science, for articles published between 1 January 2000 and 15 March 2022. Cohort studies which examined the association between statins and PD risk in the general population were also included. Two authors assessed the data and extracted all potential information for analysis. Random effects meta-analyses were performed to measure the risk ratio (RR) and 95% confidence intervals (CIs). Eighteen cohort studies including 3.7 million individuals with 31,153 PD participants were identified. In statin users, compared with non-users, the RR for PD was 0.79 (95% CI: 0.68-0.91). In a subgroup analysis of PD, this association was observed with medium and high quality, and the studies were adjusted for age, gender, and smoking status. When the data were stratified according to the duration of exposure, long-duration statin use was associated with a decreased risk of PD (RR = 0.49; 95% CI: 0.26-0.92). There was no significant decrease in the risk of PD in short-term statin users (RR = 0.94; 95% CI: 0.67-1.31). Moreover, no significant difference in the reduction in the risk of PD was observed between men (RR = 0.80; 95% CI: 0.75-0.86) and women (RR = 0.80; 95% CI: 0.75-0.86). Although our findings confirm a reduction in the PD risk associated with statin treatment and suggest that statins play a clinically favorable role, these findings should be interpreted with caution. Future randomized control trials with an ad hoc design are needed to confirm the potential utility of statins in reducing the risk of PD.

Chemopreventive Effects of Concomitant or Individual Use of Statins, Aspirin, Metformin, and Angiotensin Drugs: A Study Using Claims Data of 23 Million Individuals.

Despite previous studies on statins, aspirin, metformin, and angiotensin-converting-enzyme inhibitors (ACEIs)/angiotensin II receptor blockers (ARBs), little has been studied about all their possible combinations for chemoprevention against cancers. This study aimed to comprehensively analyze the composite chemopreventive effects of all the combinations. In this case-control study, health records were retrieved from claims databases of Taiwan's Health and Welfare Data Science Center. Eligible cases were matched at a 1:4 ratio with controls for age and sex. Both cases and controls were categorized into 16 exposure groups based on medication use. A total of 601,733 cancer cases were identified. Cancer risks (denoted by adjusted odds ratio; 99% confidence interval) were found to be significantly decreased: overall risk of all cancers in statin-alone (0.864; 0.843, 0.886), aspirin-alone (0.949; 0.939, 0.958), and ACEIs/ARBs (0.982; 0.978, 0.985) users; prostate (0.924; 0.889, 0.962) and female breast (0.967; 0.936, 1.000) cancers in metformin-alone users; gastrointestinal, lung, and liver cancers in aspirin and/or ACEIs/ARBs users; and liver cancer (0.433; 0.398, 0.471) in statin users. In conclusion, the results found no synergistic effect of multiple use of these agents on cancer prevention. Use of two (statins and aspirin, statins and metformin, statins and ACEIs/ARBs, and aspirin and ACEIS/ARBs) showed chemopreventive effects in some combinations, while the use of four, in general, did not.

Logical Observation Identifiers Names and Codes (LOINC®) Applied to Microbiology: A National Laboratory Mapping Experience in Taiwan.

BACKGROUND AND OBJECTIVE: Logical Observation Identifiers Names and Codes (LOINC) is a universal standard for identifying laboratory tests and clinical observations. It facilitates a smooth information exchange between hospitals, locally and internationally. Although it offers immense benefits for patient care, LOINC coding is complex, resource-intensive, and requires substantial domain expertise. Our objective was to provide training and evaluate the performance of LOINC mapping of 20 pathogens from 53 hospitals participating in the National Notifiable Disease Surveillance System (NNDSS). METHODS: Complete mapping codes for 20 pathogens (nine bacteria and 11 viruses) were requested from all participating hospitals to review between January 2014 and December 2016. Participating hospitals mapped those pathogens to LOINC terminology, utilizing the Regenstrief LOINC mapping assistant (RELMA) and reported to the NNDSS, beginning in January 2014. The mapping problems were identified by expert panels that classified frequently asked questionnaires (FAQs) into seven LOINC categories. Finally, proper and meaningful suggestions were provided based on the error pattern in the FAQs. A general meeting was organized if the error pattern proved to be difficult to resolve. If the experts did not conclude the local issue's error pattern, a request was sent to the LOINC committee for resolution. RESULTS: A total of 53 hospitals participated in our study. Of these, 26 (49.05%) used homegrown and 27 (50.95%) used outsourced LOINC mapping. Hospitals who participated in 2015 had a greater improvement in LOINC mapping than those of 2016 (26.5% vs. 3.9%). Most FAQs were related to notification principles (47%), LOINC system (42%), and LOINC property (26%) in 2014, 2015, and 2016, respectively. CONCLUSIONS: The findings of our study show that multiple stage approaches improved LOINC mapping by up to 26.5%.

Encouraging On-Time Completion of Leprosy Patients Treatment: Implementing E-Leprosy Framework to Primary Health Care in Indonesia.

Low adherence to leprosy treatment is the main challenge in Indonesia. This is a quasi-experimental observational study in a real setting of a leprosy control program in Indonesia. The study is aimed at evaluating an e-leprosy framework in increasing the rate of on-time attendance at primary health care and on-time completion of treatment of leprosy patients. This study has implemented an e-leprosy framework for primary health care at Pekalongan District. The intervention was conducted for 19 months to observe a 1-episode long-term treatment of leprosy patients. The study collected data of 391 registered patients from June 2012 to March 2016. Based on the inclusion and exclusion criteria, this study selected 188 patients. The SMS (short message service) reminders proved to be effective in increasing on-time completion and on-time attendance rates by 21% and 14.6%, respectively. There is a trend for late collections of the drugs at the 3rd, 8th, and 11th multidrug therapy drug collections.

An empirical study of Taiwan's food security index.

OBJECTIVE: The FAO has developed an approach for estimating the prevalence of undernourishment. Based on the FAO method Taiwan has a prevalence of undernourishment of 3.98%, which is higher than that of some developing countries. As this is not a true reflection of the status of undernourishment in our nation, the purpose of the present study was to modify the FAO methodology for Taiwan. DESIGN: Two factors were considered in the modified version. As the minimum dietary energy requirement was the main factor contributing to the inflated prevalence in Taiwan, we adjusted for a lighter physical activity level, based on the average BMI of the Taiwanese population, and calculated a new minimum dietary energy requirement. We then fitted a second-order polynomial regression model for prediction of per capita dietary energy supply. RESULTS: The adjusted minimum dietary energy requirement was reduced to 7648 kJ/d or 7765 kJ/d compared with the original value of 8054 kJ/d. This resulted in a decrease of the prevalence of undernourishment in Taiwan to 2.5% or 3.0%, which is much closer to that of other countries with the same level of economic development. The second-order polynomial regression model efficiently reduced the variation in dietary energy consumption and resulted in an undernourishment prevalence of less than 2.5%. CONCLUSIONS: This new adapted method is more appropriate for Taiwan. It is recommended that each country evaluates the appropriateness of the FAO approach for its population.