We conclude with a discussion of General Data Protection Regulation (GDPR) and the practical, philosophical, and policy challenges associated with communicating food safety and nutrition information digitally.Human phonemics responds to an urgent need in the medical research community; namely, reproducibility. We sought to assess longitudinal electronic health records (EHRs) using machine learning (ML) methods to computationally derive probable Alzheimer's Disease (AD) and related dementia subphenotypes. A retrospective analysis of EHR data from a cohort of 7587 patients seen at a large, multi-specialty urban academic medical center in New York was conducted. Subphenotypes were derived using hierarchical clustering from 792 probable AD patients (cases) who had received at least one diagnosis of AD using their clinical data. The other 6795 patients, labeled as controls, were matched on age and gender with the cases and randomly selected in the ratio of 91. Prediction models with multiple ML algorithms were trained on this cohort using 5-fold cross-validation. XGBoost was used to rank the variable importance. Four subphenotypes were computationally derived. Subphenotype A (n = 273; 28.2%) had more patients with cardiovascular diseases; subphenotype B (n = 221; 27.9%) had more patients with mental health illnesswith cardiovascular diseases and mental health illnesses. ML algorithms based on patient demographics, diagnosis, and treatment demonstrated promising results in predicting the risk of developing AD at different time points across an individual's lifespan. Four subphenotypes were computationally derived that correlated with cardiovascular diseases and mental health illnesses. ML algorithms based on patient demographics, diagnosis, and treatment demonstrated promising results in predicting the risk of developing AD at different time points across an individual's lifespan. To develop and evaluate the classification accuracy of a computable phenotype for pediatric Crohn's disease using electronic health record data from PEDSnet, a large, multi-institutional research network and Learning Health System. Using clinician and informatician input, algorithms were developed using combinations of diagnostic and medication data drawn from the PEDSnet clinical dataset which is comprised of 5.6 million children from eight U.S. academic children's health systems. Six test algorithms (four cases, two non-cases) that combined use of specific medications for Crohn's disease plus the presence of Crohn's diagnosis were initially tested against the entire PEDSnet dataset. From these, three were selected for performance assessment using manual chart review (primary case algorithm, n = 360, primary non-case algorithm, n = 360, and alternative case algorithm, n = 80). Non-cases were patients having gastrointestinal diagnoses other than inflammatory bowel disease. Sensitivity, specificity, and potrospective and prospective studies, and to optimize clinical care through the PEDSnet Learning Health System. Using diagnosis codes and medications available from PEDSnet, we developed a computable phenotype for pediatric Crohn's disease that had high specificity, sensitivity and predictive value. This process will be of use for developing computable phenotypes for other pediatric diseases, to facilitate cohort identification for retrospective and prospective studies, and to optimize clinical care through the PEDSnet Learning Health System. This study is part of the EU-funded project HarmonicSS, aimed at improving the treatment and diagnosis of primary Sjögren's syndrome (pSS). pSS is an underdiagnosed, long-term autoimmune disease that affects particularly salivary and lachrymal glands. We assessed the usability of routinely recorded primary care and hospital claims data for the identification and validation of patients with complex diseases such as pSS. pSS patients were identified in primary care by translating the formal inclusion and exclusion criteria for pSS into a patient selection algorithm using data from Nivel Primary Care Database (PCD), covering 10% of the Dutch population between 2006 and 2017. As part of a validation exercise, the pSS patients found by the algorithm were compared to Diagnosis Related Groups (DRG) recorded in the national hospital insurance claims database (DIS) between 2013 and 2017. International Classification of Primary Care (ICPC) coded general practitioner (GP) contacts combined with the mention of "S. Our study finds that GP electronic health records (EHRs) lack the granular information needed to apply the formal diagnostic criteria for pSS. The developed algorithm resulted in a patient selection that approximates the expected prevalence and characteristics, although only slightly over half of the patients were confirmed using the DIS. Without more detailed diagnostic information, the fitness for purpose of routine EHR data for patient identification and validation could not be determined. To identify depression subphenotypes from Electronic Health Records (EHRs) using machine learning methods, and analyze their characteristics with respect to patient demographics, comorbidities, and medications. Using EHRs from the INSIGHT Clinical Research Network (CRN) database, multiple machine learning (ML) algorithms were applied to analyze 11 275 patients with depression to discern depression subphenotypes with distinct characteristics. Using the computational approaches, we derived three depression subphenotypes Phenotype_A (n = 2791; 31.35%) included patients who were the oldest (mean (SD) age, 72.55 (14.93) years), had the most comorbidities, and took the most medications. The most common comorbidities in this cluster of patients were hyperlipidemia, hypertension, and diabetes. Phenotype_B (mean (SD) age, 68.44 (19.09) years) was the largest cluster (n = 4687; 52.65%), and included patients suffering from moderate loss of body function. Asthma, fibromyalgia, and Chronic Pain and Fatigue (CPF) were common comorbidities in this subphenotype. Phenotype_C (n = 1452; 16.31%) included patients who were younger (mean (SD) age, 63.47 (18.81) years), had the fewest comorbidities, and took fewer medications. https://www.selleckchem.com/products/crenolanib-cp-868596.html Anxiety and tobacco use were common comorbidities in this subphenotype. Computationally deriving depression subtypes can provide meaningful insights and improve understanding of depression as a heterogeneous disorder. Further investigation is needed to assess the utility of these derived phenotypes to inform clinical trial design and interpretation in routine patient care. Computationally deriving depression subtypes can provide meaningful insights and improve understanding of depression as a heterogeneous disorder. Further investigation is needed to assess the utility of these derived phenotypes to inform clinical trial design and interpretation in routine patient care.