Medical tests are crucial for treatment decision making. However, over-testing can often occur in any medical speciality or level of expertise. Since over-testing usually results in a financial burden for patients and is also a waste of medical resources, this naturally leads to the question: which medical test items (MTIs) are necessary and should be prioritized for the target patients? It is a nontrivial task to identify the right MTIs due to the diversified health status of patients and the complicated prerequisites of therapies. To this end, in this paper, we propose a data-driven approach to evaluate the priority which should be given to MTIs by modeling the relationships between MTIs and therapies. Specifically, we first develop a dual hierarchical topic model (DHTM), which views the adopted hierarchical therapies as labeled topics and the MTI reports, i.e., the set of hierarchical attribute-value pairs (AVPs), as documents. Then, with the therapy-AVP distribution and the partial MTI reports of the target patient, we can scope the candidate therapies, which are further utilized to evaluate the accumulated gain of MTIs to be tested. Moreover, the next MTI recommendation is conducted based on the gains. Finally, extensive experiments on real-world medical data validate the effectiveness of our approach, and some interesting observations are also provided. The code is available at https://github.com/mtirec/MtiRec.

Forecasting the stock market trend is a vital component of financial systems. Traditional methods mainly rely on quantitative trading data to make predictions. With the increasing volume of Web information, researchers begin to extract effective indicators (e.g., the events and sentiments) from the Web to facilitate the prediction. It is beneficial to fuse the heterogeneous multi-sourced data to achieve superior performance. Existing solutions adopt simple data fusing methods with conventional machine learning models, which may fail to effectively model the complex interactions among the data. With the success of deep neural networks (DNN) in various fields, we propose a DNN-based model that considers the various interactions and complex correlations among the multi-sourced data into one unified framework. In such a framework, the low-and high-order feature interactions are modeled with the factorization machine and the deep neural network respectively, and stock correlations are incorporated with an attention-based feature embedding method. Evaluations on the stock data from the year 2015 to 2017 show that our model can outperform the state-of-the-art methods.