Patient expenditure prediction using deep learning framework

Measurement of patient expenditure in healthcare is a critical task that has a variety of applications, including provider profiling, accountable care management, and capitated medical payment adjustment. Currently available methods rely on manually built features and linear regression-based models, both of which need a significant amount of medical domain expertise. and have low prediction accuracy. This study develops a multi-view deep learning system for forecasting future healthcare costs at the individual level based on prior claims data. Our multi-view technique efficiently models heterogeneous data such patient demographics, medical codes, medication usages, and facility usage. To execute spending forecasting tasks, we employed a real-world paediatric dataset with approximately 450,000 patients. According to the empirical data, our proposed technique beats all baselines for predicting medical cost. In the sphere of healthcare, these insights help to improve preventative and responsible care

Integrated design and control of reactive distillation processes using the driving force approach

Abstract Superior controllability of reactive distillation (RD) systems, designed at the maximum driving force (design-control solution) is demonstrated in this paper. Binary or multi-element single or double feed RD systems are considered. Reactive phase equilibrium data, needed for driving force analysis and design of the RD system, is generated through an in-house property prediction tool. Rigorous steady-state simulation is carried out in ASPEN plus in order to verify that the predefined design targets and dynamics are met. A multi-objective performance function is employed to evaluate the performance of the RD system in terms of energy consumption, sustainability metrics (total CO2 footprint), and control performance. Controllability of the designed system is evaluated using indices like the relative gain array (RGA) and Niederlinski Index (NI), to evaluate the degree of loop interaction, as well as through dynamic simulations using proportional-integral (PI) controllers and model predictive controllers (MPC). The design-control of the RD systems corresponding to other alternative designs that do not take advantage of the maximum driving force is also investigated. The analysis shows that the RD designs at the maximum driving force exhibit enhanced controllability and lower carbon footprint than the alternative RD designs. This article is protected by copyright. All rights reserved