By using efficient and timely medical diagnostic decision making,clinicians can positively impact the quality and cost of medical care.However,the high similarity of clinical manifestations between diseases and the li...By using efficient and timely medical diagnostic decision making,clinicians can positively impact the quality and cost of medical care.However,the high similarity of clinical manifestations between diseases and the limitation of clinicians’knowledge both bring much difficulty to decision making in diagnosis.Therefore,building a decision support system that can assist medical staff in diagnosing and treating diseases has lately received growing attentions in the medical domain.In this paper,we employ a multi-label classification framework to classify the Chinese electronic medical records to establish corresponding relation between the medical records and disease categories,and compare this method with the traditional medical expert system to verify the performance.To select the best subset of patient features,we propose a feature selection method based on the composition and distribution of symptoms in electronic medical records and compare it with the traditional feature selection methods such as chi-square test.We evaluate the feature selection methods and diagnostic models from two aspects,false negative rate(FNR)and accuracy.Extensive experiments have conducted on a real-world Chinese electronic medical record database.The evaluation results demonstrate that our proposed feature selection method can improve the accuracy and reduce the FNR compare to the traditional feature selection methods,and the multi-label classification framework have better accuracy and lower FNR than the traditional expert system.展开更多
With the rapid development of medical informatization and the popularization of digital imaging equipment,DICOM images contain the personal privacy of patients,and there are security risks in the process of storage an...With the rapid development of medical informatization and the popularization of digital imaging equipment,DICOM images contain the personal privacy of patients,and there are security risks in the process of storage and transmission,so it needs to be encrypted.In order to solve the security problem of medical images on mobile devices,a safe and efficient medical image encryption algorithm called ALCencryption is designed.The algorithm first analyzes the medical image and distinguishes the color image from the gray image.For gray images,the improved Arnold map is used to scramble them according to the optimal number of iterations,and then the diffusion is realized by the Logistic and Chebyshev map cross-diffusion algorithm.The color image is encrypted by cross-diffusion algorithm of double chaotic map.Security and efficiency analysis show that the ALCencryption algorithm has the characteristics of small neighboring pixels,large key space,strong key sensitivity,high safety and short encryption time.It is suitable for medical image encryption of mobile devices with high real-time requirements.展开更多
BACKGROUND: Inaccurate and incomplete documentation can lead to poor treatment and medicolegal consequences. Studies indicate that teaching programs in this field can improve the documentation of medical records. The ...BACKGROUND: Inaccurate and incomplete documentation can lead to poor treatment and medicolegal consequences. Studies indicate that teaching programs in this field can improve the documentation of medical records. The study aimed to evaluate the effect of an educational workshop on medical record documentation by emergency medicine residents in the emergency department.METHODS: An interventional study was performed on 30 residents in their first year of training emergency medicine(PGY1), in three tertiary referral hospitals of Tehran University of Medical Sciences. The essential information that should be documented in a medical record was taught in a 3-day-workshop. The medical records completed by these residents before the training workshop were randomly selected and scored(300 records), as was a random selection of the records they completed one(300 records) and six months(300 records) after the workshop.RESULTS: Documentation of the majority of the essential items of information was improved significantly after the workshop. In particular documentation of the patients' date and time of admission, past medical and social history. Documentation of patient identity, requests for consultations by other specialties, first and final diagnoses were 100% complete and accurate up to 6 months of the workshop.CONCLUSION: This study confirms that an educational workshop improves medical record documentation by physicians in training.展开更多
目的:探讨篮球运动干预对医学类大学生不同抑郁程度的影响。方法:通过病人健康问卷抑郁量表(Patient Health Quesyionnaire-9,PHQ-9),调查桂林医学院医学类137名大学生的抑郁情况,根据抑郁程度将大学生分为正常组、轻度抑郁组、中度抑...目的:探讨篮球运动干预对医学类大学生不同抑郁程度的影响。方法:通过病人健康问卷抑郁量表(Patient Health Quesyionnaire-9,PHQ-9),调查桂林医学院医学类137名大学生的抑郁情况,根据抑郁程度将大学生分为正常组、轻度抑郁组、中度抑郁组及重度抑郁组,对篮球运动干预实验前、干预4周、12周及停止干预10周各组PHQ-9抑郁程度进行比较,获得篮球运动干预对抑郁程度变化的效果,同时对不同抑郁程度组干预前、后PHQ-9评分进行比较。结果:137例中,篮球运动干预前PHQ-9评分为正常、轻、中、重度抑郁者分别为51例、50例、21例、9例,抑郁发生率为62.77%,干预4周、12周、停止干预10周抑郁发生率为50.36%、47.45%、38.69%。篮球干预不同时期PHQ-9评分抑郁程度的差异具有统计学意义,通过篮球运动干预后,轻、中、重度抑郁组PHQ-9评分与干预前PHQ-9评分的差异具有统计学意义。结论:不同运动时长的篮球运动干预对改善医学类大学生抑郁程度有一定作用。该研究为体医融合背景下篮球运动干预医学类大学生抑郁症治疗提供了一定的运动干预治疗方案依据。展开更多
基金The authors would like to acknowledge the financial support from the National Natural Science Foundation of China(No.61379145)the Joint Funds of CETC(Grant No.20166141B08020101).
文摘By using efficient and timely medical diagnostic decision making,clinicians can positively impact the quality and cost of medical care.However,the high similarity of clinical manifestations between diseases and the limitation of clinicians’knowledge both bring much difficulty to decision making in diagnosis.Therefore,building a decision support system that can assist medical staff in diagnosing and treating diseases has lately received growing attentions in the medical domain.In this paper,we employ a multi-label classification framework to classify the Chinese electronic medical records to establish corresponding relation between the medical records and disease categories,and compare this method with the traditional medical expert system to verify the performance.To select the best subset of patient features,we propose a feature selection method based on the composition and distribution of symptoms in electronic medical records and compare it with the traditional feature selection methods such as chi-square test.We evaluate the feature selection methods and diagnostic models from two aspects,false negative rate(FNR)and accuracy.Extensive experiments have conducted on a real-world Chinese electronic medical record database.The evaluation results demonstrate that our proposed feature selection method can improve the accuracy and reduce the FNR compare to the traditional feature selection methods,and the multi-label classification framework have better accuracy and lower FNR than the traditional expert system.
基金This work is partly supported by the Scientific Research Fund of Hunan Provincial Education Department(19B082)the Science and Technology Development Center of the Ministry of Education-New Generation Information Technology Innovation Project(2018A02020)+4 种基金the research supported by Science Foundation of Hengyang Normal University(19QD12)the Science and Technology Innovation Program of Hunan Province(2016TP1020)the Application-oriented Special Disciplines,Double First-Class University Project of Hunan Province(Xiangjiaotong[2018]469)the Hunan Province Special Funds of Central Government for Guiding Local Science and Technology Development(2018CT5001)the Subject Group Construction Project of Hengyang Normal University(18XKQ02).
文摘With the rapid development of medical informatization and the popularization of digital imaging equipment,DICOM images contain the personal privacy of patients,and there are security risks in the process of storage and transmission,so it needs to be encrypted.In order to solve the security problem of medical images on mobile devices,a safe and efficient medical image encryption algorithm called ALCencryption is designed.The algorithm first analyzes the medical image and distinguishes the color image from the gray image.For gray images,the improved Arnold map is used to scramble them according to the optimal number of iterations,and then the diffusion is realized by the Logistic and Chebyshev map cross-diffusion algorithm.The color image is encrypted by cross-diffusion algorithm of double chaotic map.Security and efficiency analysis show that the ALCencryption algorithm has the characteristics of small neighboring pixels,large key space,strong key sensitivity,high safety and short encryption time.It is suitable for medical image encryption of mobile devices with high real-time requirements.
文摘BACKGROUND: Inaccurate and incomplete documentation can lead to poor treatment and medicolegal consequences. Studies indicate that teaching programs in this field can improve the documentation of medical records. The study aimed to evaluate the effect of an educational workshop on medical record documentation by emergency medicine residents in the emergency department.METHODS: An interventional study was performed on 30 residents in their first year of training emergency medicine(PGY1), in three tertiary referral hospitals of Tehran University of Medical Sciences. The essential information that should be documented in a medical record was taught in a 3-day-workshop. The medical records completed by these residents before the training workshop were randomly selected and scored(300 records), as was a random selection of the records they completed one(300 records) and six months(300 records) after the workshop.RESULTS: Documentation of the majority of the essential items of information was improved significantly after the workshop. In particular documentation of the patients' date and time of admission, past medical and social history. Documentation of patient identity, requests for consultations by other specialties, first and final diagnoses were 100% complete and accurate up to 6 months of the workshop.CONCLUSION: This study confirms that an educational workshop improves medical record documentation by physicians in training.
文摘目的:探讨篮球运动干预对医学类大学生不同抑郁程度的影响。方法:通过病人健康问卷抑郁量表(Patient Health Quesyionnaire-9,PHQ-9),调查桂林医学院医学类137名大学生的抑郁情况,根据抑郁程度将大学生分为正常组、轻度抑郁组、中度抑郁组及重度抑郁组,对篮球运动干预实验前、干预4周、12周及停止干预10周各组PHQ-9抑郁程度进行比较,获得篮球运动干预对抑郁程度变化的效果,同时对不同抑郁程度组干预前、后PHQ-9评分进行比较。结果:137例中,篮球运动干预前PHQ-9评分为正常、轻、中、重度抑郁者分别为51例、50例、21例、9例,抑郁发生率为62.77%,干预4周、12周、停止干预10周抑郁发生率为50.36%、47.45%、38.69%。篮球干预不同时期PHQ-9评分抑郁程度的差异具有统计学意义,通过篮球运动干预后,轻、中、重度抑郁组PHQ-9评分与干预前PHQ-9评分的差异具有统计学意义。结论:不同运动时长的篮球运动干预对改善医学类大学生抑郁程度有一定作用。该研究为体医融合背景下篮球运动干预医学类大学生抑郁症治疗提供了一定的运动干预治疗方案依据。
