Predicting Venous Thrombosis in Osteoarthritis Using a Machine Learning Algorithm: A Population-Based Cohort Study

Osteoarthritis (OA) is the most common joint disease associated with pain and disability. OA patients are at a high risk for venous thrombosis (VTE). Here, we developed an interpretable machine learning (ML)-based model to predict VTE risk in patients with OA. To establish a prediction model, we used six ML algorithms, of which 35 variables were employed. Recursive feature elimination (RFE) was used to screen the most related clinical variables associated with VTE. SHapley additive exPlanations (SHAP) were applied to interpret the ML mode and determine the importance of the selected features. Overall, 3169 patients with OA (average age: 66.52 ± 7.28 years) were recruited from Xi’an Honghui Hospital. Of these, 352 and 2817 patients were diagnosed with and without VTE, respectively. The XGBoost algorithm showed the best performance. According to the RFE algorithms, 15 variables were retained for further modeling with the XGBoost algorithm. The top three predictors were Kellgren–Lawrence grade, age, and hypertension. Our study showed that the XGBoost model with 15 variables has a high potential to predict VTE risk in patients with OA.

Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer

Gut microbiome is gaining interest because of its links with several diseases, including colorectal cancer (CRC), as well as the possibility of being used to obtain non-intrusive predictive disease biomarkers. Here we performed a meta-analysis of 1042 fecal metagenomic samples from seven publicly available studies. We used an interpretable machine learning approach based on functional profiles, instead of the conventional taxonomic profiles, to produce a highly accurate predictor of CRC with better precision than those of previous proposals. Moreover, this approach is also able to discriminate samples with adenoma, which makes this approach very promising for CRC prevention by detecting early stages in which intervention is easier and more effective. In addition, interpretable machine learning methods allow extracting features relevant for the classification, which reveals basic molecular mechanisms accounting for the changes undergone by the microbiome functional landscape in the transition from healthy gut to adenoma and CRC conditions. Functional profiles have demonstrated superior accuracy in predicting CRC and adenoma conditions than taxonomic profiles and additionally, in a context of explainable machine learning, provide useful hints on the molecular mechanisms operating in the microbiota behind these conditions.

Interpretable Machine Learning

The emergence of machine learning as a society-changing technology in the past decade has triggered concerns about people's inability to understand the reasoning of increasingly complex models. The field of IML (interpretable machine learning) grew out of these concerns, with the goal of empowering various stakeholders to tackle use cases, such as building trust in models, performing model debugging, and generally informing real human decision-making.

Evaluating the Effectiveness of Marketing Campaigns for Malls Using a Novel Interpretable Machine Learning Model

We develop a novel interpretable machine learning model, GANNM, and use newly available data to evaluate how different types of marketing campaigns and budget allocations influence malls’ customer traffic. We observe that the response curves that measure the impact of campaign budget on customer traffic differ for different categories of campaigns, with sales incentives or experience incentives, during peak periods, off-peak periods, or online promotion periods. Based on such accurate response curves from GANNM, the optimized budget allocation is estimated to yield a 11.2% increase in customer traffic compared with the original allocation. Our findings provide novel insights on managing mall campaigns. Mall managers should increase marketing spending to areas that were likely overlooked before and avoid over-crowding budget to campaigns during times with high levels of competition and are likely already over-marketed. We provide empirical evidence showing that the recent trend of employing novel approaches for enhancing customer experience in physical stores can effectively encourage customers to visit malls. Furthermore, we show that online promotions could also create opportunities for offline businesses—investing in campaigns in the major online promotion periods could significantly increase customer traffic for malls, given sufficient investment in the campaigns to raise customer awareness.