Background A growing body of research suggests that the use of computerized decision support systems can better guide disease treatment and reduce the use of social and medical resources. Artificial intelligence (AI) technology is increasingly being used in medical decision-making systems to obtain optimal dosing combinations and improve the survival rate of sepsis patients. To meet the real-world requirements of medical applications and make the training model more robust, we replaced the core algorithm applied in an AI-based medical decision support system developed by research teams at the Massachusetts Institute of Technology (MIT) and IMPERIAL College London (ICL) with the deep deterministic policy gradient (DDPG) algorithm. The main objective of this study was to develop an AI-based medical decision-making system that makes decisions closer to those of professional human clinicians and effectively reduces the mortality rate of sepsis patients. Methods We used the same public intensive care unit (ICU) dataset applied by the research teams at MIT and ICL, i.e., the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III) dataset, which contains information on the hospitalizations of 38,600 adult sepsis patients over the age of 15. We applied the DDPG algorithm as a strategy-based reinforcement learning approach to construct an AI-based medical decision-making system and analyzed the model results within a two-dimensional space to obtain the optimal dosing combination decision for sepsis patients. Results The results show that when the clinician administered the exact same dose as that recommended by the AI model, the mortality of the patients reached the lowest rate at 11.59%. At the same time, according to the database, the baseline mortality rate of the patients was calculated as 15.7%. This indicates that the patient mortality rate when difference between the doses administered by clinicians and those determined by the AI model was zero was approximately 4.2% lower than the baseline patient mortality rate found in the dataset. The results also illustrate that when a clinician administered a different dose than that recommended by the AI model, the patient mortality rate increased, and the greater the difference in dose, the higher the patient mortality rate. Furthermore, compared with the medical decision-making system based on the Deep-Q Learning Network (DQN) algorithm developed by the research teams at MIT and ICL, the optimal dosing combination recommended by our model is closer to that given by professional clinicians. Specifically, the number of patient samples administered by clinicians with the exact same dose recommended by our AI model increased by 142.3% compared with the model based on the DQN algorithm, with a reduction in the patient mortality rate of 2.58%. Conclusions The treatment plan generated by our medical decision-making system based on the DDPG algorithm is closer to that of a professional human clinician with a lower mortality rate in hospitalized sepsis patients, which can better help human clinicians deal with complex conditional changes in sepsis patients in an ICU. Our proposed AI-based medical decision-making system has the potential to provide the best reference dosing combinations for additional drugs.

BackgroundA growing body of research indicates that the use of computerized decision support systems can better guide disease treatment and save resources. An artificial intelligence (AI) clinical decision system may be a useful tool to predict how different dose combinations affect survival outcomes in patients with sepsis. We boldly use Deep Deterministic Policy Gradient (DDPG) algorithm to improve the sepsis AI clinical decision system developed by MIT and IMPERIAL College London (ICL) team. We believe that although the system has been verified by data, due to the limitations of the core algorithm, it cannot meet the needs of clinical use, it could also be that the model of training is not stable. The purpose of this study was to establish a more effective AI clinician decision-making system to guide sepsis treatment.MethodsWe chose the same data model as the MIT team for experiments. these datasets are from the publicly available Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-III v1.4) database. We utilized hospitalization information for 38,600 adult patients over 15 years of age。we adopted DDPG (a strategy-based reinforcement learning method) to build an artificial intelligence clinician model.ResultsCompared with the sepsis AI clinician decision model developed by MIT based on DNQ algorithm, the sepsis AI clinician decision model developed by DDPG algorithm had a faster convergence rate, was closer to the clinician decision, and had a higher survival rate.ConclusionsThe AI clinician decision system based on DDPG algorithm can better help the ICU (Intensive Care Unit ) clinicians to cope with the changes in the condition of sepsis patients, reduce the workload, and provide a reference dosage.