In recent years, cardiovascular disease has become a common disease. With the development of machine learning and big data technologies, the processing ability of electrocardiogram (ECG) signals has been greatly enhanced through new computer technologies, enabling the auxiliary diagnosis technology for cardiovascular disease (CVD) to achieve new improvements. This article discusses the application of machine learning in ECG processing, especially in the auxiliary diagnosis of diseases. Firstly, the conventional signal preprocessing methods are introduced, and then the EEG signal processing methods based on feature extraction and fuzzy classification are explored. Secondly, the application of auxiliary diagnosis in CVD is further summarized. Finally, the advantages and disadvantages of the two methods are analyzed, and based on this, a design of an auxiliary diagnostic system compatible with the two methods is proposed, providing a new perspective for similar applied researches in the future.
Machine Learning ; Cardiovascular Diseases/diagnosis* ; Humans ; Electrocardiography ; Signal Processing, Computer-Assisted ; Diagnosis, Computer-Assisted ; Fuzzy Logic ; Electroencephalography

Stroke is one of the common cerebrovascular diseases,which can be classified into acute(0-24 h),subacute(24 h-2 weeks)and chronic stage(>2 weeks).High-resolution T1-weighted imaging(T1WI)is one kind of magnetic resonance imaging(MRI)technique,which is main imaging mode of assessing the change of cerebral structure after stroke at chronic stage,and it can accurately judge the location and size of lesion.The precise segmentation for lesion is we accurately analyze dataset of neuroimaging in stroke with large scale,and is the key of predicting rehabilitation outcomes.The automatic segmentation algorithm is one kind of technique that can automatically segment data into many independent regions or object by specific regulation or model,which does not need manual intervention.Compared with manual segmentation,it has repeatable and scalable advantages,which does not need manual setting parameter.With the development of artificial intelligence technology,its application is gradually widespread in the field of medical imaging analysis.Adopting automatic segmentation algorithm to conduct reliable and repeatable segmentation for the lesions in T1WI,which can effectively assist clinical diagnosis and rehabilitation treatment for stroke.This review summarized the advance research of automatic segmentation algorithm based on T1WI for the lesion of chronic stroke from two aspects included conventional machine learning and deep learning,and the optimization approach of three kinds of algorithm structures,which can provide reference and inspiration for researching small lesion with higher difficulty in the field of automatic segmentation.

With the gradual advancement of medical reform policy,"non-pre-hospital medical emergency"has also emerged.Whether it will cause the plunder of pre-hospital emergency resources to be seized has become the focus of discussion in emergency centers at all levels.For the implementation of the provincial party committee,the provincial government on promoting the reform of government service value-added decision deployment,focusing on the whole life cycle,through the whole service chain whole process,around outpatient,hospitalization,referral treatment key link,speed up the building from convenient service to value-added services comprehensive upgrade new ecological health services.Jiaxing positive response,relying on the overall urban and rural development advantage,multiple departments together,opened a unified line service platform of 96120,and through the integration of the city's medical pre-hospital emergency resources,innovation pre-hospital emergency work responsibilities,the implementation of first aid and first aid classification management,establish the whole domain,urban and rural integration"non-emergency transport"service system.Efforts should be made to solve four major problems,such as insufficient supply of"non-emergency transport"services,poor service channels of"non-emergency transport",difficult management of"black ambulance"and the overall efficient recycling of first aid resources.We will innovate and establish a joint service system for non-emergency transport services,an efficient and convenient service model,a comprehensive and homogeneous management standard,a universal guarantee mechanism,and a training mechanism for professionals.So as to realize the province took the lead to build covering the whole city,urban and rural undifferentiated"emergency transport"service system,complete the province's first"emergency transport"municipal standards and industry internal specification,form can copy can promote ambulance vehicles"flat"operation mechanism,as well as a variety of security support"emergency transport"the 4 goals of universal public service.

The conventional identification and prediction for failures of medical equipment mainly depend on experience and knowledge of manager for equipment,which are not able to be quantified and have lower efficiency. Therefore,it is obvious that the prediction for the failure of medical equipment is not accurate. With the technical development of computer and machine learning,the conventional identification and prediction that depend on experiences can deal with characteristics of failures through machine learning method to improve efficiency,which are hopeful in filling the gap of discipline about the identification and prediction for failures of medical equipment. This article summarized the application situation of machine learning in identifying and predicting failures of the medical equipment and the similarly electric equipment at home and abroad. Based on the key technique of identification and prediction,this article proposed suggestion about corresponding design architecture. According to the characteristics of the failure of medical equipment,this article summarized a series of information about algorithms of various machine learning in scene and accurate rate of identification and prediction,so as to provide references for relevant research of this field.

The conventional identification and prediction for failures of medical equipment mainly depend on experience and knowledge of manager for equipment,which are not able to be quantified and have lower efficiency. Therefore,it is obvious that the prediction for the failure of medical equipment is not accurate. With the technical development of computer and machine learning,the conventional identification and prediction that depend on experiences can deal with characteristics of failures through machine learning method to improve efficiency,which are hopeful in filling the gap of discipline about the identification and prediction for failures of medical equipment. This article summarized the application situation of machine learning in identifying and predicting failures of the medical equipment and the similarly electric equipment at home and abroad. Based on the key technique of identification and prediction,this article proposed suggestion about corresponding design architecture. According to the characteristics of the failure of medical equipment,this article summarized a series of information about algorithms of various machine learning in scene and accurate rate of identification and prediction,so as to provide references for relevant research of this field.

Stroke is one of the common cerebrovascular diseases,which can be classified into acute(0-24 h),subacute(24 h-2 weeks)and chronic stage(>2 weeks).High-resolution T1-weighted imaging(T1WI)is one kind of magnetic resonance imaging(MRI)technique,which is main imaging mode of assessing the change of cerebral structure after stroke at chronic stage,and it can accurately judge the location and size of lesion.The precise segmentation for lesion is we accurately analyze dataset of neuroimaging in stroke with large scale,and is the key of predicting rehabilitation outcomes.The automatic segmentation algorithm is one kind of technique that can automatically segment data into many independent regions or object by specific regulation or model,which does not need manual intervention.Compared with manual segmentation,it has repeatable and scalable advantages,which does not need manual setting parameter.With the development of artificial intelligence technology,its application is gradually widespread in the field of medical imaging analysis.Adopting automatic segmentation algorithm to conduct reliable and repeatable segmentation for the lesions in T1WI,which can effectively assist clinical diagnosis and rehabilitation treatment for stroke.This review summarized the advance research of automatic segmentation algorithm based on T1WI for the lesion of chronic stroke from two aspects included conventional machine learning and deep learning,and the optimization approach of three kinds of algorithm structures,which can provide reference and inspiration for researching small lesion with higher difficulty in the field of automatic segmentation.

With the gradual advancement of medical reform policy,"non-pre-hospital medical emergency"has also emerged.Whether it will cause the plunder of pre-hospital emergency resources to be seized has become the focus of discussion in emergency centers at all levels.For the implementation of the provincial party committee,the provincial government on promoting the reform of government service value-added decision deployment,focusing on the whole life cycle,through the whole service chain whole process,around outpatient,hospitalization,referral treatment key link,speed up the building from convenient service to value-added services comprehensive upgrade new ecological health services.Jiaxing positive response,relying on the overall urban and rural development advantage,multiple departments together,opened a unified line service platform of 96120,and through the integration of the city's medical pre-hospital emergency resources,innovation pre-hospital emergency work responsibilities,the implementation of first aid and first aid classification management,establish the whole domain,urban and rural integration"non-emergency transport"service system.Efforts should be made to solve four major problems,such as insufficient supply of"non-emergency transport"services,poor service channels of"non-emergency transport",difficult management of"black ambulance"and the overall efficient recycling of first aid resources.We will innovate and establish a joint service system for non-emergency transport services,an efficient and convenient service model,a comprehensive and homogeneous management standard,a universal guarantee mechanism,and a training mechanism for professionals.So as to realize the province took the lead to build covering the whole city,urban and rural undifferentiated"emergency transport"service system,complete the province's first"emergency transport"municipal standards and industry internal specification,form can copy can promote ambulance vehicles"flat"operation mechanism,as well as a variety of security support"emergency transport"the 4 goals of universal public service.

Objective To evaluate the significant clinical value of an intelligent dispatching platform based on the 120 command system and provide the basis for improving emergency service efficiency. Methods Convenience sampling was used to choose 356 patients who received pre-hospital care in the Jiaxing Emergency Center from August 2023 to May 2024 as research objects. The 178 patients who made phone calls to the 120 center from August to December 2023 (namely before the establishment of intelligent dispatching platform) served as a control group,while another 178 cases who made phone calls to the 120 center from January to May 2024 (namely after the establishment of intelligent dispatching platform) served as an observation group. The optimal ambulance was chosen and sent to receive the patients by the dispatcher himself in the control group,while the intelligent dispatching platform designed by the research team and computer engineers was used in the observation group. The platform was made up of multimodal receipt of alarms,accurate location of phone call places,matching and dispatching of ambulance resources,and collaborative management functions within the region. At the same time,geographic information,hospital information,and emergency vehicle information were completed to achieve united dispatching and whole-process quality control all over the region. The emergency response time,2 minutes ambulance dispatching rate,coincidence rate of medical treatment principles,and success rate of cardiopulmonary resuscitation in cities and rural areas were compared between the control group (before the establishment of intelligent dispatching platform) and the observation group (after the establishment of intelligent dispatching platform). Results The emergency response time in the observation group after the establishment of intelligent dispatching platform was shorter than that in the control group in cities (minutes:8.64±0.99 vs. 10.57±1.00,P<0.05),while there was no statistical significance between the observation group and the control group in rural areas (minutes:11.66±1.49 vs. 11.94±1.70,P>0.05). The 2 minutes ambulance dispatching rate higher than those in the control group[99.44％ (177/178) vs. 98.31％ (175/178)],there was no statistical significance (P>0.05),the coincidence rate of medical treatment principles in the observation group were significantly higher than those in the control group[98.88％ (176/178) vs. 89.89％ (160/178),P<0.05],For the observation group,the success rate of cardiopulmonary resuscitation in the observation group was slightly higher than that in the control group[18.92％ (7/37) vs. 13.51％ (5/37)],but there was no statistical significance (P>0.05). Conclusion The construction of an intelligent dispatching platform with the function of united dispatching,collaborative management of resources,and whole-process quality control all over the region can improve emergency service efficiency and effectiveness,thus promoting the development of pre-hospital care course.

Objective To design an intelligent and healthy light environment scheme in intensive care unit(ICU)from the experience perspective of nurses,and evaluate its effect on improving nurses'visual perception and work efficiency.Methods According to the Standard for Lighting Design of Buildings(GB50034-2013),reconstruction was performed from the 3 aspects of basic layout,intelligent lighting,and simulated natural light.Then,the usage experience of nurses was investigated via semi-structured interview.By combining the interview results with illuminance and color temperature,the changing curve of light effect was described to finally determine the optimal strategy and form the healthy light environment.Thirty nurses in emergency ICU(EICU)of The First Hospital of Jiaxing before the application of intelligent and healthy light environment from July to October 2023 were selected as the control group,and 30 nurses after the application of intelligent and healthy light environment from January to March 2024 were selected as the observation group.The visual fatigue score of nurses,the completion time of single venipuncture,the occurrence rate of risk events,and the difference in satisfaction of light environment in ICU were compared before and after the establishment of intelligent and healthy light environment.Results After being on duty,the visual fatigue score of nurses in both groups obviously increased,but the scores in the observation group was significantly lower compared to the control group(14.77±2.99 vs.38.43±4.67,P<0.05).Additionly,the completion time of single venipuncture in the observation group was evidently shorter than that in the control group(minutes:4.93±1.82 vs.14.87±2.50,P<0.05),the occurrence rate of risk events in the observation group was remarkably lower than that in the control group[3.33%(1/30)vs.20.00%(6/30),P<0.05],and the satisfaction of light environment in the observation group was distinctly higher than that in the control group[93.33%(28/30)vs.66.67%(20/30),P<0.05].Conclusions Aiming at the special work scenario in ICU,this paper designed intelligent scene lighting and created a bright,comfortable,safe,and healthy light environment,which could effectively relieve nurses'visual fatigue,improve the efficiency and quality of nursing work,and enhance their clinical work experience.Additionally,it provided data support and design strategies for the sound environmental development of ICU.

Objective:To reassess the prognostic value of minimal residual disease (MRD) and IKZF1 gene deletions in adults with B-cell acute lymphoblastic leukemia (B-ALL) who received pediatric-specific chemotherapy regimens during the Nanfang Hospital PDT-ALL-2016 trial.Methods:We retrospectively analyzed the prognosis of 149 adult patients with B-ALL who were admitted to Nanfang Hospital from January 2016 to September 2020. Prognostic factors were identified using Cox regression models.Results:The complete remission rate was 93.2% in 149 patients, with a 5-year overall survival (OS) rate of (54.3±5.0) % and a cumulative incidence of relapse (CIR) of (47.5±5.2) %. The Cox regression analysis revealed that MRD positivity at day 45 (MRD 3) after induction therapy was independently associated with relapse risk ( HR=2.535, 95% CI 1.122-5.728, P=0.025). Deletion of IKZF1 gene was independently associated with mortality risk ( HR=1.869, 95% CI 1.034-3.379, P=0.039). Based on MRD 3 and IKZF1 gene status, we categorized adult patients with B-ALL into the low-risk (MRD 3-negative and IKZF1 gene deletion-negative) and high-risk (MRD 3-positive and/or IKZF1 gene wild type) groups. The 5-year OS and CIR rates were (45.5±6.0) % vs (69.4±8.6) % ( P<0.001) and (61.6±8.3) % vs (25.5±6.5) % ( P<0.001), respectively, in the high-risk and low-risk groups, respectively. The multivariate analysis showed that the high-risk group was an independent risk factor for OS ( HR=3.937, 95% CI 1.975-7.850, P<0.001) and CIR ( HR=4.037, 95% CI 2.095-7.778, P<0.001) . Conclusion:The combined use of MRD and IKZF1 gene in prognostic stratification can improve clinical outcome prediction in adult patients with B-ALL, helping to guide their treatment.