Doctor Intervention Research Articles

Disease Classification of CXR Images by AlexNet Transfer Learning

The purpose of this study is to classify COVID-19 CXR(Chest X-Ray) images using a CNN(Convolutional Neural Network) modified from AlexNet using a transfer learning method so that it can be used as an auxiliary method when diagnosing diseases in medical settings. The Dataset used in the experiment uses "COVIDGR-1.0 Dataset". "COVIDGR-1.0 Dataset" is a collection of 784 anonymized X-ray images and, in collaboration with a team of radiologists, built into the dataset in accordance with labeling protocols.The proposed CNN is a fine-tuned build of pre-trained AlexNet, which takes the "COVIDGR-1.0 dataset" as input and categorizes it into Normal, Mild, Moderate and Sever according to the labels and categories. Experimental results of the disease classification of CXR images show that the AUC(Area Under the Curve) is 0.8781, with accuracy of 87.81%. It is believed that the results of this study can be used in the following fields. First, it can be used as an auxiliary tool for diagnosing lung diseases. Second, if the data set is ready, it can be used to classify other diseases. Third, data can be stored separately through classification. Fourth, in the medical field, the object to be classified can be imaged using a smartphone and then classified and stored. This study has the following limitations. The problem with automatically classifying diseases is the vastness and integrity of the dataset. If the data is not prepared, it will cause problems with the accuracy of classification. And in the medical field, doctors' decision-making (diagnosis) is not limited to image data. There is a limitation in that doctors make decisions based on comprehensive data and that medical decisions cannot be made by machines without the doctor's intervention. Determining the decision threshold in decision making problems by AI is also an important limitation. This is because setting reference points for sensitivity and specificity is not the domain of AI experts but of experts in the field. Therefore, medical staff in the field must participate in the process of developing AI in the medical field. Future tasks include continuing research in connection with program input stage development, performance improvement, app development, and medical big data servers.

Obstacle Analysis And Community Medicine Interprofessional Communication Solutions In Medical And Dental Students

Poor communication between doctors, patients and between health professionals, has a negative impact. The patient becomes disobedient to the doctor's intervention, the doctor becomes the target of violence due to the patient's ignorance. The World Health Organization (WHO) initiated Interprofessional Education (IPE) learning at the undergraduate level to improve communication and collaboration of health workers to improve patient health outcomes. This study aimed to analyze the barriers and solutions to community medicine interprofessional communication among medical and dental students. This study uses a qualitative method with a case study design. Observational data collection and focus group discussion (FGD). Students were divided into 16 groups, each consisting of 9 medical students and three dentistry students. Observation of IPE activities on collaborative practices in community residents in Sendangmulyo with five meetings for each group. FGD activities were carried out after collaborative approach with student representatives from each group. The results showed that the obstacles that occurred included: differences in competence and knowledge, lack of cooperation, lack of confidence, signal interference, differences in medical terms, miscommunication, and lack of response. Solutions: collaboration and complementarity, holding offline meetings, building closeness, gathering ideas, respecting each other's opinions, more accurate information, being open and trusting each other, being active listeners, giving others opportunities to express opinions, and using language that is easy to understand, repeating the explanation given

Injuries in sports

This article discusses the issues of injuries of any complexity or effects on the athlete's sports activities throughout his career. Injury is a continuous action on the human body of environmental factors (mechanical, physical, chemical, etc.), which lead to a violation of the anatomical integrity of tissues and functional disorders in them. There are the following types of injuries: industrial, domestic, street, transport, sports and military. Acute injuries that occur after strong simultaneous exposure. Chronic - occur after repeated exposure to a harmful factor of low power on a particular part of the body. Injuries may be accompanied by damage to the skin or mucous membranes - these are open injuries (wounds, fractures); can be without damage to the integuments - these are closed injuries (blows, sprains, tears, dislocations, bone fractures), etc. The article provides statistics on injuries in any sports specialization. In order to successfully implement measures to prevent injuries, it is necessary to know the causes and factors that cause them, as well as to provide timely medical care. The author's team conducted an analysis of literary sources. As a result, a questionnaire was formed to find out the circumstances of sports injuries of athletes in any sport. The questionnaire includes 21 questions, which are divided into four subgroups, namely: the concept of injuries and their causes; medicinal components (doctor's intervention or medicines); doping, change of time or climate zones and skin diseases; possible injuries that haunted athletes. The respondents were interviewed in December 2021. The survey involved applicants for higher education in the 2nd year (n = 10 people), 4th year (n = 15 people) of the first bachelor's level, and first-year masters (n = 17 people) in the specialization "Physical Culture and Sports" and representatives of the sports club "Fighting Frog" (n = 19 people). A total of 61 respondents took part in the survey. Also in this group of respondents were representatives of various sports (martial arts, power sports, playing sports, athletics, fitness, dance, etc.), athletes of different qualifications, ages and genders.

Implementing the EBDI model in an E-health system

Health-care systems (patient monitoring and diagnosis systems) have recently attracted the interest of researchers. Intelligent software has been used as an agent in this field where research addresses the areas of electronic health (E-health) to develop and improve health care systems and to reduce the effort and time on health users (physicians, nurses) and to follow up on the condition of patients, especially the elderly, and also Intelligent software agents are using a result when most hospitals are out of capacity. The major goal of this study is to learn how we may use intelligent software for E-Health to assist physicians, nurses, and other health practitioners in collecting and tracking patient data on a daily basis in order to enhance treatment decisions. How can we achieve this in real-time in a dynamic and non-deterministic environment? As a result, we have built a model-based system such as the Extensible Beliefs Desires and Intentions (EBDI) model as architecture for located autonomic software agents in run-time under dynamic and non-deterministic environments circumstances in this study. We have formalized the EBDI agent in E-Health as an online observer to monitor the state of a patient linked to sensors (thermometer, glucose meter, oxygen meter, etc.). This agent filters the data (sensor readings) and determines which readings are normal or abnormal before sending the patient's information to the administrative agent, which is a different type of EBDI agent. In addition, the system proposes a treatment plan based on the sensor readings and necessitates the participation of a human doctor in emergency situations. This study proposes developing a monitoring system to follow up (monitor) the health status of patients based on a multi-agent-system (MAS) with the Clustering process (using the K-mean Algorithm), where the system is designed to support warnings and alerts in abnormal health conditions and to call for a doctor's intervention in emergencies (when it is necessary).

Comprehensive and Structured 3-month Stroke Follow-up Using the Post-stroke Checklist (The Struct-FU study): A Feasibility and Explorative Study

BackgroundThere is recent evidence supporting that a comprehensive post-stroke treatment program improves outcome. However, the prevalence of stroke-related health problems and the extent of needed interventions have not been well-delineated. The Struct-FU study aims to assess the feasibility of a comprehensive stroke follow-up model and to map stroke-related problems and subsequent multidisciplinary interventions using a modified Post-stroke Checklist (PSC). MethodsWe consecutively screened all acute stroke patients at Skåne University Hospital, Sweden during an eight-month period in 2018–2019. Patients discharged to own home were eligible for inclusion. We defined a stroke-related health problem as a health- or social issue corresponding to one of the 14 items in the modified PSC, and with onset after the stroke event. Three-months post-stroke, a semi-structured interview using a 14-item modified PSC was completed to map the prevalence of stroke-related health problems, as well as any subsequent multidisciplinary stroke team interventions prompted by the visit. ResultsThe number of included patients was 200. Of these, 165 (82.9%) completed three-month follow-up, one died, and 34 were lost to follow-up. All patients completed the full PSC, and the majority (92.7%) reported stroke-related problems identified using the PSC, with the median number being three per patient, and half having four or more problems. Patients <=65 years (n=44) presented with more stroke-related problems than patients >65 years (n=121) (5 vs. 3, p=0.003). The median number of interventions was two per patient. Doctor's interventions (changes in medication, referrals or further work-up) were done in 53% of patients, 77% needed a nurse's or other stroke team professional's intervention (tailored advice, information, audits, rehabilitation assessments) and 15% needed no intervention. Only 3.6% of patients reported other stroke-related challenges (PSC item 14) than those specified in item 1–13. ConclusionWe report a high burden of stroke-related health problems in community dwelling stroke patients with mild to moderate stroke. Multidisciplinary interventions were prompted in the vast majority (85%). Only 3.6% of patients reported challenges not captured with the Post-stroke Checklist, reflecting that the comprehensive approach captures the majority of stroke-related health problems, and gives a good estimate of the total stroke-related health burden for each individual.

Connected Health in Hypertension Management.

e-health is defined as the use of communication and information technologies (ICT) to manage patients and their health in a more efficient way, with the aim of improving the overall quality of care. Healthcare services relying on telehealth (or telemedicine) and mobile health (m-health) are the most popular e-health tools used by healthcare professionals and consumers. These applications allow the exchange of medical data between patients and their doctors or among healthcare professionals, mainly through the Internet, and are used to provide healthcare services remotely (so-called “connected health”). The most popular telemedicine application in the field of hypertension is blood pressure telemonitoring (BPT), which enables transmission of BP and various clinical information from patients' homes or from the community to the doctor's surgery or the hospital. Numerous randomized controlled trials have documented a significant BP reduction combined with an intensification and optimization of the use of antihypertensive medications in patients making use of BPT plus remote counseling by a case manager, with the supervision of a doctor or a community pharmacist (telepharmacy). The major benefits of BPT are usually observed in high-risk patients. BPT can also be based on m-health wireless solutions, provided with educational support, medication trackers and reminders, and teleconsultation. In this context, BPT may favor patient's self-management, as an adjunct to the doctor's intervention, and foster patient's participation in medical decision making, with consequent improvement in BP control and increase in medication adherence. In conclusion, e-health solutions, and in particular telemedicine, are increasingly attaining a key position in the management of the hypertensive patient, with an enormous potential in terms of improvement of the quality of the delivered care, increase in the chance of a successful BP control and effective prevention of cardiovascular diseases.

Telemedicine and M-Health in Hypertension Management: Technologies, Applications and Clinical Evidence.

Electronic processes and communication technologies are more and more often employed to provide healthcare services to caregivers and their patients. Such solutions are currently referred as e-health, the most popular and widely distributed being those based on telemedicine and mobile health (m-health). A specific application of telemedicine for hypertension management is blood pressure telemonitoring (BPT), which allows remote data transmission of BP and additional information on patients' health status from their living site or from a community setting to the doctor's office or the hospital. Several randomized studies have documented a significant BP reduction with regular BPT compared to usual care, particularly in high risk hypertensive patients. Additional benefits are observed when BPT is offered under the supervision of a team of healthcare professionals, including a community pharmacist. BPT may also be provided in the context of m-health solutions, which commonly include wireless diagnostic and clinical decision support tools. M-health has the potential to promote patient's self-management, as a complement to the doctor's intervention, and encourage greater participation in medical decision making. Current statistics show that half of smartphone owners gather health information through their phone and 19% use a health app. In case of hypertensive patients the most popular apps are those with tracking function, including BPT. Thus, e-health, and in particular BPT and m-health, are progressively gaining a key role in the management of hypertensive patients, having the potential to improve the quality of the delivered care and to more effectively prevent cardiovascular consequences of high BP.

The ‘scientific artworks’ of Doctor Paul Richer

This article examines the little-known sculptures of pathology created by Doctor Paul Richer (1849–1933) in the 1890s for the so-called Musée Charcot at the Hôpital de la Salpêtrière in Paris....

Does active referral by a doctor or 12-Step peer improve 12-Step meeting attendance? Results from a pilot randomised control trial

BackgroundActive engagement in 12-Step self-help groups (SHG) is associated with improvements in substance use outcomes during and after treatment, yet levels of participation in SHG meetings in the UK remain low. MethodAn RCT investigating the impact of active referral to SHG, delivered by doctors or 12-Step peers during inpatient treatment on both inpatient and post-treatment meeting attendance was conducted. 151 inpatients with alcohol, opiate, crack-cocaine or benzodiazepine dependence undergoing detoxification, received one of two active referral interventions: 12-Step peer intervention (PI), doctor intervention (DI), or no intervention (NI). 83% of the sample was followed up, 2–3 months following discharge. ResultsActive referral interventions significantly increased attendance at 12-Step meetings during inpatient treatment (88% versus 73%, p<.05). Rates of post-discharge meeting attendance were PI=64%, DI=48%, NI=33%, with those in the PI group significantly (OR=3.6; CI=1.3, 9.8) more likely to have attended. Inpatient meeting attenders were three times as likely to have attended meetings post-discharge (59% versus 20%, p<.01), and post-discharge meeting attenders reported significantly higher abstinence rates (60.8% versus 39.2%, p<.05) at follow-up. However, abstinence rates did not differ significantly across intervention groups (44% [PI], 41% [DI] and 36% [NI]). ConclusionAttendance at 12-Step SHGs is associated with greater rates of abstinence and active referral, especially by 12-Step peers, increases 12-Step SHG attendance rates. However, improved clinical outcomes among attendees might not occur until stronger recovery peer support networks are established. These results show promise for the benefit of incorporating Twelve Step Facilitation into UK treatment settings.