A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record.

PURPOSE: To develop deep learning model (Deep-KOA) that can predict the risk of knee osteoarthritis (KOA) within the next year by using the previous three years nonimage-based electronic medical record (EMR) data. PATIENTS AND METHODS: We randomly selected information of two million patients from the Taiwan National Health Insurance Research Database (NHIRD) from January 1, 1999 to December 31, 2013. During the study period, 132,594 patients were diagnosed with KOA, while 1,068,464 patients without KOA were chosen randomly as control. We constructed a feature matrix by using the three-year history of sequential diagnoses, drug prescriptions, age, and sex. Deep learning methods of convolutional neural network (CNN) and artificial neural network (ANN) were used together to develop a risk prediction model. We used the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and precision to evaluate the performance of Deep-KOA. Then, we explored the important features using stepwise feature selection. RESULTS: This study included 132,594 KOA patients, 83,111 females (62.68%), 49,483 males (37.32%), mean age 64.2 years, and 1,068,464 non-KOA patients, 545,902 females (51.09%), 522,562 males (48.91%), mean age 51.00 years. The Deep-KOA achieved an overall AUROC, sensitivity, specificity, and precision of 0.97, 0.89, 0.93, and 0.80 respectively. The discriminative analysis of Deep-KOA showed important features from several diseases such as disorders of the eye and adnexa, acute respiratory infection, other metabolic and immunity disorders, and diseases of the musculoskeletal and connective tissue. Age and sex were not found as the most discriminative features, with AUROC of 0.9593 (-0.76% loss) and 0.9644 (-0.25% loss) respectively. Whereas medications including antacid, cough suppressant, and expectorants were identified as discriminative features. CONCLUSION: Deep-KOA was developed to predict the risk of KOA within one year earlier, which may provide clues for clinical decision support systems to target patients with high risk of KOA to get precision prevention program.

Anemia and the Risk of Cognitive Impairment: An Updated Systematic Review and Meta-Analysis.

BACKGROUND: Cognitive impairment is one of the most common, burdensome, and costly disorders in the elderly worldwide. The magnitude of the association between anemia and overall cognitive impairment (OCI) has not been established. OBJECTIVE: We aimed to update and expand previous evidence of the association between anemia and the risk of OCI. METHODS: We conducted an updated systematic review and meta-analysis. We searched electronic databases, including EMBASE, PubMed, and Web of Science for published observational studies and clinical trials between 1 January 1990 and 1 June 2020. We excluded articles that were in the form of a review, letter to editors, short reports, and studies with less than 50 participants. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were followed. We estimated summary risk ratios (RRs) with random effects. RESULTS: A total of 20 studies, involving 6558 OCI patients were included. Anemia was significantly associated with an increased risk of OCI (adjusted RR (aRR) 1.39 (95% CI, 1.25-1.55; p < 0.001)). In subgroup analysis, anemia was also associated with an increased risk of all-cause dementia (adjusted RR (aRR), 1.39 (95% CI, 1.23-1.56; p < 0.001)), Alzheimer's disease [aRR, 1.59 (95% CI, 1.18-2.13; p = 0.002)], and mild cognitive impairment (aRR, 1.36 (95% CI, 1.04-1.78; p = 0.02)). CONCLUSION: This updated meta-analysis shows that patients with anemia appear to have a nearly 1.39-fold risk of developing OCI than those without anemia. The magnitude of this risk underscores the importance of improving anemia patients' health outcomes, particularly in elderly patients.

Association between Anemia and Risk of Parkinson Disease.

METHODS: We systematically searched articles on electronic databases such as PubMed, Embase, Scopus, and Google Scholar between January 1, 2000 and July 30, 2020. Articles were independently evaluated by two authors. We included observational studies (case-control and cohort) and calculated the risk ratios (RRs) for associated with anemia and PD. Heterogeneity among the studies was assessed using the Q and I 2 statistic. We utilized the random-effect model to calculate the overall RR with 95% CI. RESULTS: A total of 342 articles were identified in the initial searches, and 7 full-text articles were evaluated for eligibility. Three articles were further excluded for prespecified reasons including insufficient data and duplications, and 4 articles were included in our systematic review and meta-analysis. A random effect model meta-analysis of all 4 studies showed no increased risk of PD in patients with anemia (N = 4, RRadjusted = 1.17 (95% CI: 0.94-1.45, p = 0.15). However, heterogeneity among the studies was significant (I 2 = 92.60, p = <0.0001). The pooled relative risk of PD in female patients with anemia was higher (N = 3, RRadjusted = 1.14 (95% CI: 0.83-1.57, p = 0.40) as compared to male patients with anemia (N = 3, RRadjusted = 1.09 (95% CI: 0.83-1.42, p = 0.51). CONCLUSION: This is the first meta-analysis that shows that anemia is associated with higher risk of PD when compared with patients without anemia. However, more studies are warranted to evaluate the risk of PD among patients with anemia.

Statin Use Is Associated with a Decreased Risk of Mortality among Patients with COVID-19.

BACKGROUND: Recent epidemiological studies remain controversial regarding the association between statin use and reducing the risk of mortality among individuals with COVID-19. OBJECTIVE: The objective of this study was to clarify the association between statin use and the risk of mortality among patients with COVID-19. METHODS: We conducted a systematic articles search of online databases (PubMed, EMBASE, Scopus, and Web of Science) between 1 February 2020 and 20 February 2021, with no restriction on language. The following search terms were used: "Statins" and "COVID-19 mortality or COVID19 mortality or SARS-CoV-2 related mortality". Two authors individually examined all articles and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for study inclusion and exclusion. The overall risk ratio (RRs) with 95% confidence interval (CI) was calculated to show the strength of the association and the heterogeneity among the studies was presented Q and I2 statistic. RESULTS: Twenty-eight studies were assessed for eligibility and 22 studies met the inclusion criteria. Statin use was associated with a significantly decreased risk of mortality among patients with COVID-19 (RR adjusted = 0.64; 95% CI: 0.57-0.72, p < 0.001). Moreover, statin use both before and after the admission was associated with lowering the risk of mortality among the COVID-19 patients (RR adjusted;before = 0.69; 95% CI: 0.56-0.84, p < 0.001 and RR adjusted;after = 0.57; 95% CI: 0.54-0.60, p < 0.001). CONCLUSION: This comprehensive study showed that statin use is associated with a decreased risk of mortality among individuals with COVID-19. A randomized control trial is needed to confirm and refute the association between them.

Deep Learning Classifier with Patient's Metadata of Dermoscopic Images in Malignant Melanoma Detection.

BACKGROUND: Incidence of skin cancer is one of the global burdens of malignancies that increase each year, with melanoma being the deadliest one. Imaging-based automated skin cancer detection still remains challenging owing to variability in the skin lesions and limited standard dataset availability. Recent research indicates the potential of deep convolutional neural networks (CNN) in predicting outcomes from simple as well as highly complicated images. However, its implementation requires high-class computational facility, that is not feasible in low resource and remote areas of health care. There is potential in combining image and patient's metadata, but the study is still lacking. OBJECTIVE: We want to develop malignant melanoma detection based on dermoscopic images and patient's metadata using an artificial intelligence (AI) model that will work on low-resource devices. METHODS: We used an open-access dermatology repository of International Skin Imaging Collaboration (ISIC) Archive dataset consist of 23,801 biopsy-proven dermoscopic images. We tested performance for binary classification malignant melanomas vs nonmalignant melanomas. From 1200 sample images, we split the data for training (72%), validation (18%), and testing (10%). We compared CNN with image data only (CNN model) vs CNN for image data combined with an artificial neural network (ANN) for patient's metadata (CNN+ANN model). RESULTS: The balanced accuracy for CNN+ANN model was higher (92.34%) than the CNN model (73.69%). Combination of the patient's metadata using ANN prevents the overfitting that occurs in the CNN model using dermoscopic images only. This small size (24 MB) of this model made it possible to run on a medium class computer without the need of cloud computing, suitable for deployment on devices with limited resources. CONCLUSION: The CNN+ANN model can increase the accuracy of classification in malignant melanoma detection even with limited data and is promising for development as a screening device in remote and low resources health care.

A probabilistic model for reducing medication errors: A sensitivity analysis using Electronic Health Records data.

OBJECTIVES: Medication-related clinical decision support systems have already been considered as a sophisticated method to improve healthcare quality, however, its importance has not been fully recognized. This paper's aim was to validate an existing probabilistic model that can automatically identify medication errors by performing a sensitivity analysis from electronic medical record data. METHODS: We first built a knowledge base that consisted of 2.22 million disease-medication (DM) and 0.78 million medication-medication (MM) associations using Taiwan Health and Welfare data science claims data between January 1st, 2009 and December 31st, 2011. Further, we collected 0.6 million outpatient visit prescriptions from six departments across five different medical centers/hospitals. Afterward, we employed the data to our AESOP model and validated it using a sensitivity analysis of 11 various thresholds (α = [0.5; 1.5]) that were used to identify positive DM and MM associations. We randomly selected 2400 randomly prescriptions and compared them to the gold standard of 18 physicians' manual review for appropriateness. RESULTS: One hundred twenty-one results of 2400 prescriptions with various thresholds were tested by the AESOP model. Validation against the gold standard showed a high accuracy (over 80%), sensitivity (80-96%), and positive predictive value (over 85%). The negative predictive values ranged from 45 to 75% across three departments, cardiology, neurology, and ophthalmology. CONCLUSION: We performed a sensitivity analysis and validated the AESOP model in different hospitals. Thus, picking the optimal threshold of the model depended on balancing false negatives with false positives and depending on the specialty and the purpose of the system.

A personalized medication management platform (PMMP) to improve medication adherence: A randomized control trial.

OBJECTIVES: Medication non-adherence caused by forgetting and delays has serious health implications and causes substantial expenses to patients, healthcare providers, and insurance companies. We assessed the effectiveness of a personalized medication management platform (PMMP) for improving medication adherence, self-management medication, and reducing long-term medication costs. METHODS: We developed a mobile PMMP to reduce delayed and missed medications. A randomized control trial was conducted of three medical centers in Taiwan. A total 1198 participants who aged over 20 years, received outpatient prescription drugs for a maximum period of 14 days. 763 patients were randomly assigned to intervention group as receiving daily SMS reminders for their medications and 434 patients in control group did not. The primary outcome was change in delaying and forgetting medication between before and after intervention (after 7 days). RESULTS: Medication delays were reduced from 85% to 18% (67% improvement) after SMSs for the intervention group and from 80% to 43% (37% improvement) for the control group. Patients forgot medications were significantly reduced from 46% to 5% (41% improvement) for the experimental group after SMSs and from 44% to 17% (27% improvement) for the control group. The SMSs were considered helpful by 83% of patients and 74% of them thought SMSs help in controlling diseases. 92% of patients would recommend this system to their family and friends. CONCLUSIONS: A timely and personalized medication reminder through SMS can improve medication adherence in a nationalized healthcare system with overall savings in medication costs and significant improvements in health and disease management. TRIAL REGISTRATION: ClinicalTrials.gov: NCT02197689.

Cancer-disease associations: A visualization and animation through medical big data.

OBJECTIVE: Cancer is the primary disease responsible for death and disability worldwide. Currently, prevention and early detection represents the best hope for cure. Knowing the expected diseases that occur with a particular cancer in advance could lead to physicians being able to better tailor their treatment for cancer. The aim of this study was to build an animated visualization tool called as Cancer Associations Map Animation (CAMA), to chart the association of cancers with other disease over time. METHODS: The study population was collected from the Taiwan National Health Insurance Database during the period January 2000 to December 2002, 782 million outpatient visits were used to compute the associations of nine major cancers with other diseases. A motion chart was used to quantify and visualize the associations between diseases and cancers. RESULTS: The CAMA motion chart that was built successfully facilitated the observation of cancer-disease associations across ages and genders. The CAMA system can be accessed online at http://203.71.86.98/web/runq16.html. CONCLUSION: The CAMA animation system is an animated medical data visualization tool which provides a dynamic, time-lapse, animated view of cancer-disease associations across different age groups and gender. Derived from a large, nationwide healthcare dataset, this exploratory data analysis tool can detect cancer comorbidities earlier than is possible by manual inspection. Taking into account the trajectory of cancer-specific comorbidity development may facilitate clinicians and healthcare researchers to more efficiently explore early stage hypotheses, develop new cancer treatment approaches, and identify potential effect modifiers or new risk factors associated with specific cancers.