Application of Bayesian network and regression method in treatment cost prediction.

Charging according to disease is an important way to effectively promote the reform of medical insurance mechanism, reasonably allocate medical resources and reduce the burden of patients, and it is also an important direction of medical development at home and abroad. The cost forecast of single disease can not only find the potential influence and driving factors, but also estimate the active cost, and tell the management and reasonable allocation of medical resources. In this paper, a method of Bayesian network combined with regression analysis is proposed to predict the cost of treatment based on the patient's electronic medical record when the amount of data is small. Firstly, a set of text-based medical record data conversion method is established, and in the clustering method, the missing value interpolation is carried out by weighted method according to the distance, which completes the data preparation and processing for the realization of data prediction. Then, aiming at the problem of low prediction accuracy of traditional regression model, this paper establishes a prediction model combined with local weight regression method after Bayesian network interpretation and classification of patients' treatment process. Finally, the model is verified with the medical record data provided by the hospital, and the results show that the model has higher prediction accuracy.

Daporinad in vitro metabolite profiling via rat, dog, monkey and human liver microsomes by liquid chromatography/quadrupole-orbitrap mass spectrometry.

RATIONALE: Daporinad is a novel and potent inhibitor of nicotinamide phosphoribosyl transferase with potential antineoplastic and antiangiogenic activities. We aimed to explore the metabolites of daporinad generated from liver microsomes and to propose metabolic pathways. METHODS: The metabolites were generated by individually incubating daporinad (10 µM) with liver microsomes at 37°C for 60 min. The metabolites were identified by ultra-high-performance liquid chromatography/quadrupole-orbitrap mass spectrometry (UPLC/Q-Orbitrap-MS) using electrospray ionization in positive ion mode. They were deduced by accurate MS and MS/MS data. RESULTS: In total, 16 metabolites were found and their identities were characterized. In rat, dog and human, they were minor; in monkey, M11 was the most abundant. Daporinad was metabolized mainly through N-dealkylation, amide hydrolysis, hydrogenation, oxygenation and dehydrogenation. There was no human-specific metabolite. CONCLUSIONS: The current study provided an overview of the metabolism of daporinad, which is helpful in predicting in vivo metabolites and in selecting animal species for toxicology studies.