The Internet of Medical Things(IoMT)will come to be of great importance in the mediation of medical disputes,as it is emerging as the core of intelligent medical treatment.First,IoMT can track the entire medical treat...The Internet of Medical Things(IoMT)will come to be of great importance in the mediation of medical disputes,as it is emerging as the core of intelligent medical treatment.First,IoMT can track the entire medical treatment process in order to provide detailed trace data in medical dispute resolution.Second,IoMT can infiltrate the ongoing treatment and provide timely intelligent decision support to medical staff.This information includes recommendation of similar historical cases,guidance for medical treatment,alerting of hired dispute profiteers etc.The multi-label classification of medical dispute documents(MDDs)plays an important role as a front-end process for intelligent decision support,especially in the recommendation of similar historical cases.However,MDDs usually appear as long texts containing a large amount of redundant information,and there is a serious distribution imbalance in the dataset,which directly leads to weaker classification performance.Accordingly,in this paper,a multi-label classification method based on key sentence extraction is proposed for MDDs.The method is divided into two parts.First,the attention-based hierarchical bi-directional long short-term memory(BiLSTM)model is used to extract key sentences from documents;second,random comprehensive sampling Bagging(RCS-Bagging),which is an ensemble multi-label classification model,is employed to classify MDDs based on key sentence sets.The use of this approach greatly improves the classification performance.Experiments show that the performance of the two models proposed in this paper is remarkably better than that of the baseline methods.展开更多
Objective To analyze the characteristics of medical malpractice from different grades of hospitals and to explore forensic investigation strategies in assessing medical dispute. Methods A total of206 cases of medical ...Objective To analyze the characteristics of medical malpractice from different grades of hospitals and to explore forensic investigation strategies in assessing medical dispute. Methods A total of206 cases of medical dispute from 2009 to 2010 investigated by the Department of Forensic Medicine in Nanjing Medical University were selected and analyzed according to fault incidence, fault-prone part,and degree of causality in the treatment. Results Among the 206 cases analyzed, tertiary hospitals, secondary hospitals and primary hospitals showed medium, high and low error rate, respectively. A majority of medical malpractice cases were distributed in the departments of surgery, medicine and gynecology.Conclusion The frequency and severity of medical malpractice in primary hospitals were high, which were gradually reduced in tertiary and secondary hospitals.展开更多
基金supported by the National Key R&D Program of China(2018YFC0830200,Zhang,B,www.most.gov.cn)the Fundamental Research Funds for the Central Universities(2242018S30021 and 2242017S30023,Zhou S,www.seu.edu.cn)the Open Research Fund from Key Laboratory of Computer Network and Information Integration In Southeast University,Ministry of Education,China(3209012001C3,Zhang B,www.seu.edu.cn).
文摘The Internet of Medical Things(IoMT)will come to be of great importance in the mediation of medical disputes,as it is emerging as the core of intelligent medical treatment.First,IoMT can track the entire medical treatment process in order to provide detailed trace data in medical dispute resolution.Second,IoMT can infiltrate the ongoing treatment and provide timely intelligent decision support to medical staff.This information includes recommendation of similar historical cases,guidance for medical treatment,alerting of hired dispute profiteers etc.The multi-label classification of medical dispute documents(MDDs)plays an important role as a front-end process for intelligent decision support,especially in the recommendation of similar historical cases.However,MDDs usually appear as long texts containing a large amount of redundant information,and there is a serious distribution imbalance in the dataset,which directly leads to weaker classification performance.Accordingly,in this paper,a multi-label classification method based on key sentence extraction is proposed for MDDs.The method is divided into two parts.First,the attention-based hierarchical bi-directional long short-term memory(BiLSTM)model is used to extract key sentences from documents;second,random comprehensive sampling Bagging(RCS-Bagging),which is an ensemble multi-label classification model,is employed to classify MDDs based on key sentence sets.The use of this approach greatly improves the classification performance.Experiments show that the performance of the two models proposed in this paper is remarkably better than that of the baseline methods.
文摘Objective To analyze the characteristics of medical malpractice from different grades of hospitals and to explore forensic investigation strategies in assessing medical dispute. Methods A total of206 cases of medical dispute from 2009 to 2010 investigated by the Department of Forensic Medicine in Nanjing Medical University were selected and analyzed according to fault incidence, fault-prone part,and degree of causality in the treatment. Results Among the 206 cases analyzed, tertiary hospitals, secondary hospitals and primary hospitals showed medium, high and low error rate, respectively. A majority of medical malpractice cases were distributed in the departments of surgery, medicine and gynecology.Conclusion The frequency and severity of medical malpractice in primary hospitals were high, which were gradually reduced in tertiary and secondary hospitals.
