Computational Medicine:Past,Present and Future

Computational medicine is an emerging discipline that uses computer models and complex software to simulate the development and treatment of diseases.Advances in computer hardware and software technology,especially the development of algorithms and graphics processing units(GPUs),have led to the broader application of computers in the medical field.Computer vision based on mathematical biological modelling will revolutionize clinical research and diagnosis,and promote the innovative development of Chinese medicine,some biological models have begun to play a practical role in various types of research.This paper introduces the concepts and characteristics of computational medicine and then reviews the developmental history of the field,including Digital Human in Chinese medicine.Additionally,this study introduces research progress in computational medicine around the world,lists some specific clinical applications of computational medicine,discusses the key problems and limitations of the research and the development and application of computational medicine,and ultimately looks forward to the developmental prospects,especially in the field of computational Chinese medicine.

Artificial intelligence and the future of medicine: a multidimensional analysis

This multidimensional analysis of the impact of artificial intelligence on the future of medicine aims to give some clues on foreseen categories of applications as well as their societal implications in terms of risks/benefits.Artificial intelligence encompasses technologies recapitulating four dimensions of human intelligence,i.e.sensing,thinking,acting and learning.Intelligent machines are converging with advancing biotechnologies to shape the future of medicine,in synergy with continuous progress in our understanding of system biology,brain physiology,biology of aging,computational sciences and decision-making theories.Data-driven predictive models of health-related problems can be generated to inform decisions and actions,allowing to enhance productivity in new drug development,increase the cost-effectiveness of fully integrated health care systems and empower patients and healthy individuals to better manage their disease or their health,respectively.Consequently,the future will likely take the form of a computational precision medicine continuously informed by data capture and modeling to propose preventive measures or therapies precisely tailored to characteristics of each individual.

Rule of prescribing traditional Chinese medicine in the treatment of pneumoconiosis basedon association rules and k-means clustering algorithm

Objective:To analyze the rule of prescribing traditional Chinese medicine for treating pneumoconiosis,so as to provide reference for differential diagnosis and treatment of pneumoconiosis as well as for the development of new drugs for treatingthe disease.Methods:We searched China National Knowledge Infrastructure,Wanfang Database and VIP Chinese PublicationDatabase to retrieve relevant literatures which were then screened according to the enrollment criteria to establish a prescriptiondatabase of traditional Chinese medicine for the treatment of pneumoconiosis.The inheritance calculation platform of traditionalChinese medicine was used to analyze the prescribing rule of traditional Chinese medicine in the treatment of pneumoconiosisbased on association rules,k-means clustering algorithm and regression model analysis.Results:A total of 131 related literature were preliminarily selected,from which 97 prescriptions of traditional Chinese medicine with a total of 195 herbs were included.The most frequently prescribed herbs included Radix astragali,Platycodon grandiflorum,Pinellia ternata,licorice,Codonopsispilosula,Salvia miltiorrhiza,bitter almond etc.A total of 14 association rules,13 high-frequency herb pairs were found and 5groups of formulas were revealed by cluster analysis.Conclusion:The prescriptions for the treatment of pneumoconiosis are mainly composed of herbs for tonifying deficiency,resolving phlegm,relieving cough and asthma,activating blood circulation and removingblood stasis,which are supplemented with herbs for clearing heat,relieving appearance,regulating qi,promoting waterand permeating dampness,etc.,The prescribing rules reflect the basic pathological characteristics of lung deficiency and collateral arthralgia in pneumoconiosis,which provides some ideas for the clinical differentiation and treatment of pneumoconiosis in traditionalChinese medicine.It also provides reference for the research and development of new treatment methods.

Markov Chain Model for Smoking Cessation Treatment

Probabilistic models are commonly used in computational medicine for diagnostics. Smoking cessation is an important issue of modern medicine. According to statistics about third part of male in global population are smokers. It is important to develop new approaches for smoking cessation treatment including methods of early diagnosis and development of individual treatment programs for each patient according to his or her physical peculiarities. One of the promising methods is computerized approach for tobacco treatment including electronic survey and computer data analysis. In this work we propose a probabilistic model based on Markov chain for estimation of patient behavior in the process on medical survey. This analysis can help to find out patient＇s individual characteristics and develop effective personal treatment program. Based on probabilistic model software was developed with aim to enhance diagnosis and developing individual smoking cessation treatment programs for each patient.

Neuroprotective Effect of Shenfu Injection (参附注射液) Following Cardiac Arrest in Pig Correlates with Improved Mitochondrial Function and Cerebral Glucose Uptake

Objective：To test whether Shenfu Injection（参附注射液,SFI）might attenuate the impact of cerebral energy dysfunction after resuscitation in a pig model of cardiac arrest（CA）.Methods：Thirty-four Wuzhishan miniature inbred pigs were randomly divided into three groups：the SFI group（n=12）,the saline group（SA group,n=12）,and the sham-operated group（sham group,n=10）.Following successful return of spontaneous circulation（ROSC）from 8-min untreated ventricular fibrillation,animals received a continuous infusion of either SFI（0.2 mL/min）or saline for 6 h.Cerebral performance category score was evaluated at 24and 48 h after ROSC,followed by positron emission tomography and computed tomography scans of cerebral glucose uptake.Surviving pigs were euthanized 48 h after ROSC,and the brains were removed for detecting mitochondrial function.Results：Compared with the SA group,SFI treatment produced a better neurologic outcome48 h after ROSC（P〈0.05）.However,there was no significant difference of survival rate between the SA and SFI groups（83.3%vs.81.8%,P〉0.05）.After ROSC,the SA group showed a decrease in the maximum standardized uptake value of different regions in the brain tissue,where SFI treatment can ameliorate these decreases（P〈0.01or P〈0.05）.Improved mitochondrial respiratory properties and higher mitochondrial membrane potential were also found following SFI treatment compared with the SA group at 48 h after ROSC（P〈0.05 or P〈0.01）.Conclusion：SFI treatment after resuscitation has significant neuroprotective effects against disruption of cerebral energy metabolism from CA by improving glucose uptake and by normalizing mitochondrial function.