Patient Health Monitoring Research Articles

A privacy preserve big data analysis system for wearable wireless sensor network

Big data and artificial intelligence develop rapidly. Big data analysis has been applied in many fields of smart healthcare. Once these data are leaked or modified during transmission, it will not only invade the privacy of patients, but also endanger their lives. Many researchers worked on encrypted personal health records (PHR). However, there are still some challenges, such as data leakage during deep learning and leakage of training models, and some users do not want their data to be leaked to the analysis organization. How to protect privacy while leveraging deep learning is a pressing issue. In this paper, we present a system for predicting disease and timely alarms by collecting data from sensors and using deep learning to analyze and monitor patient health data. In order to protect the privacy of health data, we adopt an assured data deletion approach which the data owner can choose to revoke some users’ access to their health data. Extensive analysis and experimental results are presented that demonstrate the security, and efficiency of our proposed approach.

An Overview of Patient’s Health Status Monitoring System Based on Internet of Things (IoT)

The Internet of Things (IoT) is a newly emerging term for the new generation of the Internet which allows understanding between interconnected devices. IoT acts as an assistant in healthcare and plays an extremely important role in wide scopes of medicinal services observing applications. Through determining the pattern of parameters that are observed, the character of the disease can be expected. Health specialists and technicians have developed a great system with low-cost healthcare monitoring for people suffering from many diseases using common techniques such as wearable devices, wireless channels, and other remote devices. Network-related sensors, either worn on the body or in living environments, collect rich information to assess the physical and mental state of the patient. This work focuses on scanning the existing e-health (electronic healthcare) monitoring system using integrated systems. The main goal of the e-health monitoring system is to offer the patient a prescription automatically according to his or her condition. The doctor can check patient health continuously without physical interaction. The study aims to explore the uses of IoT applications in the medical sector, and its role in raising the level of medical care services in health institutions. Also, the study will address the applications of IoT in the medical field and the extent of its use to enrich traditional methods in various health fields and to determine the extent of the ability of IoT to improve the quality of health services provided. The study relies on a descriptive research approach through an analysis of the literature published in this field. The results of the study refer to the application of IoT in the health institutions, it will help to obtain accurate diagnoses for patients, which will reflect on the quality of service provided to the patient. It will also reduce periodic patient reviews to the hospital by relying on IoT applications for remote diagnosis. Also, an application in health institutions will contribute to providing data correct for the diseases that patients suffer from, and hence employing them in preparing scientific research to obtain more accurate results. This paper introduces the review of the Internet-based healthcare monitoring system (HCMS) and the general outlines on opportunities and challenges of the patient’s Internet-based patient health monitoring system.

Foldscope as a primary diagnostic tool for oral and urinary tract infections and its effectiveness in oral health education.

Due to its speed, accuracy and cost-effectiveness, microscopy has become an integral part of clinical examination for disease diagnosis. However, modern microscopes are very costly and require skilled personnel for their operation and maintenance, and specimen processing and analysis is labour-intensive. Further, lack of such expensive diagnostic tools in remote areas is a serious concern. Affordable point-of-care diagnostic tools are the most useful for timely disease diagnosis and management. The Foldscope is an affordable origami-based microscopy device composed of a series of paper clippings, which, upon assembly, can hold a specimen slide for observation, and this specimen can be viewed via a mobile phone camera attached to it. The present study evaluated the use of the Foldscope in the clinical diagnosis of oral and urinary tract infections and evaluated its efficacy as a motivational tool for improving oral health among school children in India. We qualitatively compared the Foldscope to a clinical microscope by examining five different types of clinical samples. Of the different types of clinical samples, the Foldscope was effective in detecting infection in dental plaque samples and urine samples. Thus, we further analysed 31 dental plaque samples of patients aged 3-13 years and 25 urine samples of patients aged 11-62 years. We also evaluated the use of the Foldscope as an educational tool for motivating oral hygiene among 80 school children aged 12 years and found that students in the Foldscope intervention group had better measures of oral hygiene than did students in the nonintervention group. In summary, our study indicated that the Foldscope is useful in detecting urinary tract infections and kidney stones in urine samples and is a useful motivational tool for oral health education among school-aged children. Furthermore, it may also be useful in oral health monitoring in resource poor settings. LAY DESCRIPTION: Poor and remote population often suffers due to lack of capacity to buy products or avail services which are expensive. In such a society people are not able to afford even the basic detection of diseases. Foldscope may come to rescue here! Foldscope is a paper-based, use-and-throw, affordable microscope. This study explores the use of Foldscope in clinics and diseases diagnosis using patient samples. Preliminary detection of disease associated symptoms in dental and urinary infections and digital record keeping via storage in mobile phone memory and hospital OPD records for monitoring patient's health are some of the advantages of Foldscope for clinical use in resource poor settings. It identifies that Foldscope not only can be used by students or teachers for educational purposes but it can also pave a path for promotion of dental hygiene among young children. The study also suggests that further improvement in design or resolution of Foldscope will broaden the scope of its application, in future.

Cy Sician-Revolutionizing Patient Health Monitoring System with auto-diagnosis

Cardiovascular diseases (CVD) has emerged as one of the major causes for death in all over the world. This paper displays a framework to remotely screen, health of Cardiovascular disease affected patients utilizing Machine to Machine (M2M) innovation which is a part of the project called CySician . Real time patient health monitoring system is advantageous to the patients and society as it will significantly reduce medical charges, waiting time for patient and improve patient handling capability of any hospital. In this patient health monitoring system pulse rate, ECG, body temperature, Body Mass Index(BMI) and general clinical interrogation is finished by a chatbot named “LifeBot”. The primary components associated with this project are pulse sensor, Raspberry Pi 3B+ (processing unit), temperature sensor module sensor, utilizing Machine Learning (ML) calculation it automatically analyzes the accumulated information to propose prescription to the patient. After the patient is diagnosed and the disease is detected, the patient will be notified with the kind of medication he needs. If the problem is nominal, the patient will be suggested with a basic treatment and will be monitored regularly. If the problem is of major scale, the patient will be directed to the payment gateway where he will be asked to pay a nominal fee for appointment from doctors to continue his check-up .Ultimately, the final well-being report is displayed to the doctor on the User interface that is visible on PC/Laptop.

A secure and efficient remote patient‐monitoring authentication protocol for cloud‐IoT

SummaryThe ongoing Cloud‐IoT (Internet of Things)–based technological advancements have revolutionized the ways in which remote patients could be monitored and provided with health care facilities. The real‐time monitoring of patient's health leads to dispensing the right medical treatment at the right time. The health professionals need to access patients' sensitive data for such monitoring, and if treated with negligence, it could also be used for malevolent objectives by the adversary. Hence, the Cloud‐IoT–based technology gains could only be conferred to the patients and health professionals, if the latter authenticate one another properly. Many authentication protocols are proposed for remote patient health care monitoring, but with limitations. Lately, Sharma and Kalra (DOI: 10.1007/s40998‐018‐0146‐5) present a remote patient‐monitoring authentication scheme based on body sensors. However, we discover that the scheme still bears many drawbacks including stolen smart card attack, session key compromise, and user impersonation attacks. In view of those limitations, we have designed an efficient authentication protocol for remote patient health monitoring that counters all the above‐mentioned drawbacks. Moreover, we prove the security features of our protocol using BAN logic‐based formal security analysis and validate the results in ProVerif automated security tool.

Iot Based Health Monitoring System

In the present busy days constant monitoring of the patient’s body parameters such as temperature and heart beat rate etc., becomes difficult. In our day-to-day life health has prime importance. Maintaining the health is a daily work . Hence to remove the burden of monitoring patients health from doctor’s head. This project present the methodology for monitoring patients remotely using GSM and embedded technology

Health Monitoring System of Patient Using IoT

Now a day’s IoT brings gadgets together and assumes a fundamental part in different methodologies like smart home automation, brilliant industries, smart environment, agricultural fields and patient health monitoring system and so on. One of the approaches is to monitor the health state of the patient and screen it to doctors or paramedical staff through the IoT, as it is hard to screen the patient for 24 hours. So here the patient health condition or status i.e. Pulse rate, Body Temperature, ECG and so on can be measured by utilizing the protruding sensors. These sensors are associated with the node mcu and mcp 3008, it gathers the information i.e. biomedical data from the sensors and the detected biomedical information can be transmitted to the server. The "Thingspeak" named cloud is utilized here to place the detected information into the server. From this server, the information can be envisioned to the specialists and other paramedical staff either by Thingspeak website or Thingview android application. This system also notifies if there is any change in patient parameters. In this way, this Health monitoring system diminishes the toil of specialists and paramedical staff to screen the patient for 24 hours and further reduces time and cost for support.

Big Data, IoT Wearable and m Health Cloud Platform Integration Triads a Logical Way to Patient Health Monitoring

IoT along with big data capabilities is useful in fall detection, medical fridges, sportsman care, patient surveillance, chronic disease management, sleep control and monitoring, etc. Every year a large number of patients are identified with diabetes or cardiac disorders. There is a greater need to handle many patients with the existing medical staff and doctors like cardiologists and diabetologists. This chapter aims at establishing the logical conceptualization and linkages of IoT enabled system linkages with wearable, big-data platforms and cloud-based mhealth delivery. The study subsequently aims at qualitatively and quantitatively validating and putting forth a feasible nuanced understanding framework linking the major contemporary technology triads/automated care delivery process platform in the healthcare context with prime focus on patient health monitoring and care-delivery.

Mobile Wi-Fi Based Scheduling of Cyber-Physical Systems in Healthcare

Wireless Body Area Networks (WBANs) and Wireless Local Area Networks (WLANs) have been widely regarded as solution providers for future Cyber-Physical Systems (CPS)-based ehealthcare amenities. The IEEE 802.11 standard specifies media access protocols in wireless networks, along with channel access methods. WBANs are expected to improve the existing healthcare services significantly, but several research challenges also have to be tackled for apt utilization of the technology. Guarantee of Quality-of-Service (QoS) differentiation between various health parameters, such as temperature and blood pressure, during mobility is a major challenge for the provision of ehealthcare services. The scheme proposed in this paper for the Mobile Wi-Fi based connectivity of WBANs is designed to provide QoS-based priorities for ehealthcare subscribers by altering the Contention Window (CW) for different applications of patient health monitoring. The relationship between CW and QoS is utilized to achieve efficient resource assignment. Three different health parameters, i.e., ECG (Electrocardiogram), BP (blood pressure) and temperature. are monitored using medical CPS in this work. The performance evaluation results, such as end-to-end packet delay and throughput for various data traffic classes reveal that the proposed scheme improves QoS provision.

Energy-Efficient IoT-Health Monitoring System using Approximate Computing

Wireless Body Sensor Nodes (WBSN) are frequently used for real time IoT-based health monitoring of patients outside the hospital environment. These WBSNs involve bio-sensors to capture signals from a patient’s body and wireless transmitters to transmit the collected signals to a server located in private/public cloud in real time. These WBSNs include hardware for processing of signals before being transmitted to the cloud. Simultaneous occurrence of all these processes inside energy constrained WBSNs results in considerable amount of power consumption, thus limiting their operational lifetime. Due to the inherent error-resilience in signal processing algorithms, most of these data reaching the servers are redundant in nature and hence of not much clinical importance. Transmission and storage of these excess data result in inefficient usages of transmission bandwidth and storage capabilities. In this paper, we develop a real time encoding scheme that performs iterative thresholding and approximation of wavelet coefficients for sparse encoding of bio-signals (ECG signals), thereby reducing the energy and bandwidth consumption of the WBSN. The encoding scheme compresses bio-signals (ECG signals), while still maintaining the clinically important features. We optimize various process parameters to model a low power hardware prototype for the implementation of our algorithm on a real time microcontroller based IoT platform that operates as an end-to-end WBSN system in real time. Experimental results show a system-level energy improvement of 96% with a negligible impact on signal quality (2%).

Potential of Raman spectroscopy for the analysis of plasma/serum in the liquid state: recent advances

There is compelling evidence in the literature to support the application of Raman spectroscopy for analysis of bodily fluids in their native liquid state. Naturally, the strategies described in the literature for Raman spectroscopic analysis of liquid samples have advantages and disadvantages. Herein, recent advances in the analysis of plasma/serum in the liquid state are reviewed. The potential advantages of Raman analysis in the liquid form over the commonly employed infrared absorption analysis in the dried droplet form are initially highlighted. Improvements in measurement protocols based on inverted microscopic geometries, clinically adaptable substrates, data preprocessing and analysis and applications for routine monitoring of patient health as well as therapeutic administration are reviewed. These advances suggest that clinical translation of Raman spectroscopy for rapid biochemical analysis can be a reality. In the future, this method will prove to be highly beneficial to clinicians for rapid screening and monitoring of analytes and drugs in the biological fluids, and to the patients themselves, enabling early treatment, before the disease becomes symptomatic, allowing early recovery.

Real-time estimation and prediction of the mortality caused due to COVID-19 using particle swarm optimization and finding the most influential parameter.

On March 11, 2020, the World Health Organization has declared the outbreak of COVID-19 as Pandemic, which is the massive challenges faced globally. Previous studies have indicated that the meteorological parameters can play a vital role in transmissibility and Mortality. In the present work, the influence of Comorbidity and meteorological parameters are investigated for Mortality caused due to COVID. For this, the most affected city by COVID-19 is considered, i.e., Mumbai, India, as a case study. It was found that Comorbidity is the most influential parameter on the Mortality of COVID-19. The Spearman correlation coefficient for meteorological parameters lies between 0.386 and 0.553, whereas for Comorbidity was found as 0.964. A regression model is developed using particle swarm optimization to predict the mortality cases for Mumbai, India. Further, the developed model is validated for the COVID-19 cases of Delhi, India, to emphasize the utility of the developed model for other cities. The measured and predicted curve shows a good fit with a mean percentage error of 0.00957% and a coefficient of determination of 0.9828. Thus, particle swarm optimization techniques demonstrate very high potential for the prediction of Mortality caused due to COVID-19.It is insisted that by providing constant health monitoring and adequate care for the comorbidity patients, the Mortality can be suppressed drastically. The present work can serve as an input to the policymakers to overcome the COVID-19 pandemic in India as well as other parts of the world.

The physical health and side-effect monitoring of patients prescribed clozapine: data from a clinical audit conducted in UK mental health services.

Background:In addition to mandatory haematological monitoring, treatment guidelines recommend routine monitoring of adverse effects and physical health in patients prescribed clozapine.Methods:NHS trusts/healthcare organisations participated in a clinical audit in the context of a UK quality improvement programme addressing clozapine-prescribing practice.Results:Data relating to 6948 adult patients prescribed clozapine were submitted by 63 NHS trusts/healthcare organisations. Of 481 patients treated with clozapine for up to 18 weeks, there was documented pretreatment screening of blood pressure, heart rate and ECG in at least 90%, and body weight, plasma lipids, plasma glucose/glycated haemoglobin (HbA1c) and physical examination in approximately 80%. During the first 2 weeks of clozapine treatment there was documented daily measurement of both heart rate and blood pressure in 82% and body temperature in 77%. In a subsample of 411 patients, of the 72% who had weekly side-effect assessments documented in the first month of treatment, a structured assessment tool had been used in 29%. Treatment monitoring up to 18 weeks included an ECG in 90%, C-reactive protein (CRP) or creatine kinase in 42%, and troponin or B-type natriuretic peptide (BNP) in 29%. In the 5908 patients prescribed clozapine for at least 1 year, blood pressure and body weight/body mass index were documented in at least 80%, plasma lipids in 78% and plasma glucose in 73%, with an ECG in 55%. Two-thirds were prescribed medication to manage side effects of clozapine and one third of those with a diagnosis of schizophrenia were prescribed a second antipsychotic medication.Conclusion:The findings suggest that for most patients treated with clozapine in UK mental health services, physical health screening and side-effect monitoring follow recommended practice, but there was limited use of structured side-effect assessment tools. Monitoring for clozapine-induced myocarditis during the early risk period using markers of inflammation such as CRP, and cardiac damage such as troponin and BNP, was less consistent. This may partly reflect the variation in guideline recommendations for monitoring for myocarditis and partly the selected use of such tests when prompted by cardiac symptoms. The relatively common coprescription of medications for the majority of people on longer-term clozapine treatment may well further increase side-effect burden and physical health risks, reinforcing the need for continuing systematic monitoring.

Smart Patient Assistance and Health Monitoring System Using IOT

Paralyzed patients who cannot talk and are assisted by a wheel chair needs to be taken care of by their relatives or by a care taker. Their health status like body temperature and pulse along with their daily requirements need to be monitored constantly. Sometimes, taking care of each and every patient constantly at the same time becomes a difficult task for the care taker. To take care of them at every moment in a day is also difficult. So, we propose an innovative smart patient assistance and health care monitoring system using sensors that keeps a track of their pulse and body temperature and informs the care taker for any critical situations. In addition to this, it sends the notifications to the Smartphone of the care taker based on their requirements and emergency using gestures through a accelerometer. Some of the basic requirements like need for food and water need to go for washroom are informed to the caretaker through these gestures. The sensors are connected to NodeMCU device powered by IOT through Wi-Fi. In case, the person falls down `from wheel chair, an emergency mail is sent to the caretaker. The remaining patients in a ward cannot use the above module as it is unnecessarily triggered when the patient moves. So we propose a separate module here. A 2.4’inch TFT LCD Touchscreen interfaced to a Arduino Mega that can send same messages to the caretaker by a mere touch which also monitors the patient’s body temperature and heart-beat using the required sensors and sends messages to the caretaker during abnormal conditions.

Проблемы развития домашнего мониторинга состояния здоровья

Due to its rapid development, telemonitoring technologies in the field of healthcare significantly outstripped the possibilities of implementation in real practice. Therefore, there is a need for the active development of this area of prevention and treatment of chronic patients, identifying emerging problems and finding possible solutions. The aim of this work was to assess the status and development prospects of monitoring of patient health in residential settings and analyze the problems of its inclusion in the existing health care system. Based on the analysis of scientific publications and expert assessments, the existing at-home telemonitoring systems, the main pathologies observed, and the organization of medical care for chronic patients under remote monitoring are examined. It was shown that the on-line treatment of monitored patients in the current health care system is very difficult due to its unpreparedness for the introduction of such technologies and the undeveloped concept of clinical and economic efficiency of their use. The article proposes to carry out special training of patients on effective participation in the process of remote monitoring, and it is also proposed to provide in the treatment and prevention institutions the position of a consultant (general practitioner, with an appropriately equipped workstation) for conducting remote patient consultations.

A Dashboard Prototype for Tracking the Impact of Diabetes on Hospital Readmissions Using a National Administrative Database.

BackgroundOver the past several decades, diabetes mellitus has contributed to a significant disease burden in the general population. Evidence suggests that patients with a coexisting diabetes diagnosis consume more hospital resources, and have higher readmission rates compared to those who do not. Against the backdrop of bundled-payment programs, healthcare systems cannot underestimate the importance of monitoring patient health information at the population level.MethodsUsing the data from the Centers for Medicare and Medicaid Services (CMS) administrative claims database, we created a dashboard prototype to enable hospitals to examine the impact of diabetes on their all-cause readmission rates and financial implications if diabetes was present at the index hospitalization. The technical design involved loading the relevant 10th revision of International Classification of Diseases (ICD-10) codes provided by the medical team and flagging diabetes patients at the claim. These patients were then tagged for readmissions within the same database. The odds ratios were determined based on data from two groups: those with diabetes at index hospitalization which include type 1 only, type 2 only, and type 1 and type 2 diabetes, plus those without diabetes at index hospitalization.ResultsThe dashboard presents summary data of diabetes readmissions quality metrics at a national level. Users can visualize summary data of each state and compare odds ratios for readmissions as well as raw hospitalization data at their facility. Dashboard users can also view data classified by a diagnosis-related group (DRG) system. In addition to a “national” data view, for users who inquire about data specific to demographic regions, the DRG view can be further stratified at the state level or county level. At the DRG level, users can view data about the cost per readmissions for all index hospitalization with and without diabetes.ConclusionsThe dashboard prototype offers users a virtual interface which displays visual data for quick interpretation, monitors changes at a population level, and enables administrators to benchmark facility data against local and national trends. This is an important step in using data analytics to drive population level decision making to ultimately improve medical systems.

Teledermatology: idea, benefits and risks of modern age - a systematic review based on melanoma.

Telemedicine may be described as a modern technology supporting health care at a distance. Dermatology, as a visually-dependent specialty, is particularly suited for this kind of the health care model. This has been proven in a number of recent studies, which emphasized feasibility and reliability of teledermatology. Many patients in the world still do not have access to appropriate dermatological care, while skin cancers morbidity is on an upward trend. Technological development has enabled clinicians to care for diverse patient populations in need of skin expertise without increasing their overhead costs. Teledermatology has been used for various purposes: health care workers can use this technology to provide clinical services to patients, to monitor patient health, to consult with other health care providers and to provide patients with access to educational resources. It seems that teledermatology might be the answer to numerous issues concerning diagnosing, screening and managing cancers as well as pigmented skin lesions.

Internet of Things based Heart Beat Monitoring System

Healthcare is an area that is rapidly developing in technology and services. A recent development in this area is remote monitoring of patients which has many advantages in a fast aging world population with increasing health complications. The Internet of Thongs (IOT) plays important role to monitor patient health remotely. With help of Arduino Microcontroller, Wifi module and sensors this can be possible. Sensors for monitoring essential vital signs such as electrocardiogram reading, heart rate, respiration rate, blood pressure, temperature, blood glucose levels and neural system activity are available today. Humans are facing a retardant of surprising death because of varied sickness that is because lack of medical aid to the patients at right time. Most of death happened due to right treatment at right time. With the help of technology only now it can be possible to minimize such death. The fetched data through sensors which is attached to human body can be retrieved and store on ThingSpeak cloud is used here. The Doctors can view patient's medical records remotely, recommend or prescribe drug, request for ambulance or visit patient based on data being sent from the monitoring system [13].

Artificial Intelligence in the Healthcare Sector

To an extent as never before in the history of medicine, computers are supporting human input, decision making and provision of data. In today’s healthcare sector and medical profession, AI, algorithms, robotics and big data are used to derive inferences for monitoring large-scale medical trends, detecting and measuring individual risks and chances based on data-driven estimations. A knowledge-intensive industry like the healthcare profession highly depends on data and analytics to improve therapies and practices. In recent years, there has been tremendous growth in the range of medical information collected, including clinical, genetic, behavioral and environmental data. Every day, healthcare professionals, biomedical researchers and patients produce vast amounts of data from an array of devices. These include electronic health records (EHRs), genome sequencing machines, high-resolution medical imaging, smartphone applications and ubiquitous sensing, as well as Internet of Things (IoT) devices that monitor patient health (OECD 2015). Through machine learning algorithms and unprecedented data storage and computational power, AI technologies have most advanced abilities to gain information, process it and give a well-defined output to the end-user. Daily monitoring thereby aids to create big data to recognize behavioral patterns’ relation to health status in order to create predictions with highest mathematical precision based on big data capturing large-scale samples. AI thereby enlightens to analyze the relation between prevention and treatment and patient outcomes in all stages of diagnosis, treatment, drug development and monitoring, personalized medicine, patient control and care. Advanced hospitals are looking into AI solutions to support and perform operational initiatives that increase precision and cost effectiveness. Robotics have been used for disabled and patient care assistance. Medical decision making has been supported through predictive analytics and general healthcare management technology. Network connectivity allows access to affordable healthcare around the globe in a cost-effective way.

Mathematical modeling of ischemic stroke

Objective: to determine the prognostic value of the indicators of fluid and electrolyte balance in the acutest period of severe ischemic stroke (IS). Patients and methods. A total of 150 patients with severe IS of various locations and pathogenetic subtypes were examined. The impact of plasma osmolarity or sodium levels on the course and prognosis of IS was studied on day 1 of the disease. Results and discussion. It has been established that in patients with severe IS, the most common type of fluid and electrolyte imbalance is hyperosmolar hypernatremic syndrome that develops at the onset of severe IS, serves as a factor for poor outcome, and is accompanied by high mortality. The rate of fatal outcomes in hypoosmolar syndromes is higher than that in normal plasma osmolarity, but significantly lower than that in hyperosmolar syndromes. Cerebral salt wasting (CSW) is associated with a higher mortality rate than syndrome of inappropriate antidiuretic hormone secretion (SIADH), which confirms a worse prognostic value in hypovolemia than in normo- and hypervolemia. The development of diabetes insipidus at the onset of IS reflects the degree of brainstem structural destruction and, accordingly, is associated with the highest rate of fatal outcomes. The cardioembolic pathogenetic subtype of IS is characterized by a more severe course and a higher probable mortality rate in both hypoosmolar and normosmolar conditions.Conclusion. Impaired fluid and electrolyte homeostasis is of significant prognostic value for the outcome of IS. In this case, the leading role is played by the hyperosmolar hypernatremic syndrome, in which the probability of a fatal outcome is highest and there is a need for continuous patient health monitoring and high-speed decision-making aimed to correct this condition. Therapeutic policy for diabetes insipidus depends on the duration of IS. The risk for fatal outcome in the cardioembolic pathogenetic subtype of IS is higher than that in atherothrombotic stroke, at any plasma osmolarity and sodium levels.