OBJECTIVE: To determine whether data-driven family histories (DDFH) derived from linked EHRs of patients and their parents can improve prediction of patients' 10-year risk of diabetes and atherosclerotic cardiovascular disease (ASCVD). MATERIALS AND METHODS: A retrospective cohort study using data from Israel's largest healthcare organization. A random sample of 200 000 subjects aged 40-60 years on the index date (January 1, 2010) was included. Subjects with insufficient history (<1 year) or insufficient follow-up (<10 years) were excluded. Two separate XGBoost models were developed-1 for diabetes and 1 for ASCVD-to predict the 10-year risk for each outcome based on data available prior to the index date of January 1, 2010. RESULTS: Overall, the study included 110 734 subject-father-mother triplets. There were 22 153 cases of diabetes (20%) and 11 715 cases of ASCVD (10.6%). The addition of parental information significantly improved prediction of diabetes risk (P < .001), but not ASCVD risk. For both outcomes, maternal medical history was more predictive than paternal medical history. A binary variable summarizing parental disease state delivered similar predictive results to the full parental EHR. DISCUSSION: The increasing availability of EHRs for multiple family generations makes DDFH possible and can assist in delivering more personalized and precise medicine to patients. Consent frameworks must be established to enable sharing of information across generations, and the results suggest that sharing the full records may not be necessary. CONCLUSION: DDFH can address limitations of patient self-reported family history, and it improves clinical predictions for some conditions, but not for all, and particularly among younger adults.

E-Commerce marketplaces support millions of daily transactions, and some disagreements between buyers and sellers are unavoidable. Resolving disputes in an accurate, fast, and fair manner is of great importance for maintaining a trustworthy platform. Simple cases can be automated, but intricate cases are not sufficiently addressed by hard-coded rules, and therefore most disputes are currently resolved by people. In this work we take a first step towards automatically assisting human agents in dispute resolution at scale. We construct a large dataset of disputes from the eBay online marketplace, and identify several interesting behavioral and linguistic patterns. We then train classifiers to predict dispute outcomes with high accuracy. We explore the model and the dataset, reporting interesting correlations, important features, and insights.

A significant portion of web search queries directly refers to named entities. Search engines explore various ways to improve the user experience for such queries. We suggest augmenting search results with trivia facts about the searched entity. Trivia is widely played throughout the world, and was shown to increase users' engagement and retention.