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Optimization of medication therapy depends on maximizing benefits and minimizing side effects of medications. This research showed how a joint approach using text mining, natural language processing, and machine learning can provide information for personalized and optimized medication therapy. Reviews on the benefits and side effects of prescription and over-the-counter medications were used to determine how well an integrated supervised and unsupervised learning could learn medication satisfaction. Supervised learning with naïve-Bayes, non-linear support vector machine with radial basis function kernels, and random forests with CART decision trees was measured by a micro-aggregated Matthews correlation coefficient and a macro-averaged F1 measure. Random forests outperformed support vector machines by almost 250% and naive-Bayes by 600% on the two evaluation metrics. All models did better with three rating levels, instead of five. Topic modeling and stacked cluster analysis were coupled with parts-of-speech tagging and text mining operations to establish a robust data preprocessing procedure to eliminate noisy features from the data. Unsupervised topic modeling and clustering represented an exploratory validation of how easy supervised classification would be. Well-defined latent topics were discovered including topics on “sleep quality”, “the opportunity to get back to work”, and “weight gain”. Overlapping clusters revealed that incorporating more information on social, demographic, or medical history variables could improve classifier performance. This research provided evidence that medication satisfaction can be learned with carefully designed joint supervised, unsupervised, and natural language learning techniques.

Conflicts between supply chain members emerge because individual strategic actions may not be jointly optimal. Efforts to forecast consumer demand represent a source of conflict. The coordination of forecasts requires a powerful incentive alignment approach. This work proposes a smart contract equipped consortium blockchain system that creates an incentive structure that makes coordination with respect to forecasts economically appealing. Distortions of demand information due to uncoordinated forecasting are captured by a bullwhip measure that factors both forecast error and variance. Cooperation under the system is shown to help minimize this bullwhip measure, thus generating new outcomes for the participants that allow for a higher reward. Under a fixed payout structure, the system achieves credibility of continued cooperation, thus promoting an optimally coordinated equilibrium between the retailer and supplier. Blockchain technology represents a novel information system and consensus formation mechanism that can intermediate the behavior of a supply chain network.