The Importance of Health Information Seeking Among Diabetes Patients in Malaysia: A Preliminary Observation

The sharing of health information by educating patients and encouraging them to seek relevant information is one of the main strategies in disease prevention. By evaluating patient's information seeking behaviour and identifying the health information seeking trends, it is possible to establish effective techniques to deliver the necessary information to the patients to enhance their self-management skills. It is critical to keep people informed about effective preventative techniques that can control their disease progression. Thus, this study aimed to highlight the role and importance of health information seeking behaviour among people with diabetes in Malaysia.

Smart Ambulance System with Remote Knowledge Communications Through Cloud

An ambulance is one of saving many lives by taking the people who need health emergencies. Saving the life of the person is one of the challenging and precious ones. Our key idea is to deliver a patient’s health condition before the victim reaches the hospital in this project. Here we use some biomedical sensors like a heartbeat sensor, temperature sensor, and a respiratory sensor to check the patient health status. There will be a continuous update to the hospital about the patient’s condition through the cloud with the help of the internet of things. The hospitals can also track the ambulance’s live location through the GPS placed in the ambulance where it arrives, and they can know at what time the patient reaches the hospital. With this information, if the patient is in critical condition, the hospital staff can make all the earlier arrangements before the patient arrives at the hospital and saves their lives as soon as possible. Here we use the biometric sensor to know the patient’s information by scanning the patient’s fingerprint. The stored database obtains this information. In cases of accident situations, to avoid legal problems, the patient’s information is sent to the cops through the GSM, and it is also intimated to the patient’s relatives as soon as possible. The parameters which are measured by using biomedical sensors are viewed by doctors using the Blynk app.

A Novel Random Error Approximate Adder-Based Lightweight Medical Image Encryption Scheme for Secure Remote Monitoring of Health Data

In the present global scenario, social distancing is an inevitable one. The need for social distancing and advancements of technology to facilitate the patients and doctors around the world mandated the telemedicine and remote monitoring of patience details as the pivotal way to diagnose the disease. In this, it is essential to transmit the patient’s information such as X-ray and scan images of them to the doctor in the remote location. Preventing the medical data from the technological adversaries is the need of the hour. Infinitesimal attacks in medical images may cost human lives. This work proposes a lightweight, secure medical image encryption scheme for the remote monitoring of health data. The proposed encryption scheme uses computationally less complex weighted shift approximate adder (WSAA)–based encryption logic. The scheme uses a 256-bit key for the encryption process that strengthens the encryption and robust against various attacks. The proposed encryption scheme deploys the WSAA for diffusing the pixel values. A unique way of key distribution for pixel-wise encryption within the image is proposed that avoids the need for separate logic for the pixel-wise confusion. The proposed Encryption scheme is evaluated for its entropy and horizontal, vertical, diagonal correlation, histogram, key space, and sensitivity. Experimental results affirm that the proposed scheme significantly good with less computational complexity. The peak signal-to-noise ratio (PSNR) value of the decrypted image is infinity, and this matches the ideal requirement of the medical encryption scheme.

Effect of Intelligent Vital Sign Monitoring System on Postoperative Nursing Care of Severe Patients

In order to study the observation of postoperative vital sign data of critically ill patients, this paper developed a set of vital sign data acquisition system based on intelligence and realized the design of vital sign data acquisition system APP based on Android Studio programming software, which was used to realize the information collection of vital sign data of patients. PDA is connected with a vital sign measuring equipment through Bluetooth. The patient’s wristband is scanned with PDA to read the patient’s information, and then the measured vital sign data are obtained automatically by measuring APP. The initial alarm value is set to be greater than 1%, and it needs more than 60 and less than or equal to 120 RR interval data to judge apnea. Information collection of intelligent vital sign detection can not only save the time of nursing staff but also improve the nursing quality.

It Takes a Village: The Importance of Neuropsychological Findings in a Collaborative Approach for a Patient with Congenital Central Hypoventilation Syndrome and Specific Phobia

Congenital central hypoventilation syndrome (CCHS) is a life-threatening disorder characterized by respiratory symptoms such as hypoventilation during sleep, significantly reduced ventilatory and arousal responses, and sustained hypoxia. Patients with CCHS exhibit neurocognitive deficits due to structural abnormalities in the brainstem, cerebellum, and forebrain. Due to the potential for repeated hypoxemia and hypercarbia among patients with CCHS, neurocognitive functioning is often impaired. This is the first described report in which a patient with CCHS and specific phobia has been reported and highlights the importance of neuropsychological testing in directing treatment approaches. We report a case of a 26-year-old male, diagnosed with CCHS and specific phobia. This patient was overdue for a needed bronchoscopy to check his airway for abnormalities (recommended every 12-24 months). The patient had developed a specific phobia to procedures involving anesthesia. It was determined in the initial phase of treatment that the patient’s neurocognitive status was impacting his ability to engage in psychiatric and psychosocial treatment. This patient’s care consisted of neuropsychological testing, with medication consultation, and cognitive behavioral psychotherapy. Treatment involved consistent collaboration among the patient’s treating clinicians as well as collaboration with the patient’s family and team of nurses. At the conclusion of treatment, the patient had successfully completed his bronchoscopy and future treatment goals were identified. This case emphasizes the importance of a neuropsychological evaluation when there is a disconnect in a patient’s information processing, as the results may be highly informative in directing treatment for patients with CCHS and specific phobia. The collaborative care we provided offers insights which may direct future interventions for patients with CCHS and improve their quality of life. Our case adds support to the recommendation that patients with CCHS and impaired psychosocial functioning should receive neuropsychological testing to best direct treatment.

An Approach of CA with M-RIPPER for Heart Disease Prediction

In the present world, mobile computing devices are popular and are identified in each aspect of life. This combination among computing and the present world is not restricted to the everyday life. The medical field was similarly concerned, where care is given in a wide scope of areas and conditions. The medical domain is continually being immersed with new kinds of innovations, including context-aware system and application. In this research, a context aware healthcare model based on IoT application is proposed. The smart medical devices are used to measure the data from the patients and store it in database. From the database, the patient’s information and medical records are considered as context aware data. For analyzing and classifying the data, the MRIPPER (Modified Repeated Incremental Pruning to Produce ErroR) algorithm is used. This algorithm is a rule-based machine learning algorithm. By using this algorithm, the rules are framed for the analysis of dataset for the prediction of heart disease. The performance analysis of the proposed model is experimented in MATLAB simulation tool. Further, the performance of the proposed model is compared with other existing models like J48, random forest, CART, OneR, and JRip algorithms. The proposed algorithm has achieved 98.89% accuracy, Precision is 96.76%, recall or sensitivity is 99.05%, specificity is 94.35%, and f-score is 97.60%. Overall, the proposed model has obtained 97.38% accuracy in predicting normal class and 97.93% in predicting abnormal class subjects.

Bacterial and Fungal Co-Infections among ICU COVID-19 Hospitalized Patients in a Palestinian Hospital: Incidence and Antimicrobial Stewardship

Diagnosis of co-infections with multiple pathogens among hospitalized COVID-19 patients can be jointly challenging and very essential for appropriate treatment, shortening hospital stay and preventing antimicrobial resistance. This study proposes to investigate the burden of bacterial and fungal co-infections outcomes on COVID-19 patients. It is a single centre cross-sectional study of hospitalized COVID-19 patients at Beit-Jala hospital in Palestine. The study included 321 hospitalized patients admitted to the ICU between June 2020 and March 2021 aged ≥ 20 years, with a confirmed diagnosis of COVID-19 via RT-PCR conducted on a nasopharyngeal swab. The patient’s information was gathered using graded data forms from electronic medical reports. The diagnosis of bacterial and fungal infection was proved through the patient’s clinical presentation and positive blood or sputum culture results. All cases had received empirical antimicrobial therapy before the ICU admission, and different regimens during the ICU stay. The rate of bacterial co-infection was 51.1%, mainly from gram-negative isolates (Enterobacter species and K.pneumoniae). The rate of fungal co-infection caused by A.fumigatus was 48.9%, and the mortality rate was 8.1%. However, it is unclear if it had been attributed to SARS-CoV-2 or coincidental.

Model Data

After preparing your dataset, the business problem should be quite familiar, along with the subject matter and the content of the dataset. This section is about modeling data, using data to train algorithms to create models that can be used to predict future events or understand past events. The section shows where data modeling fits in the overall machine learning pipeline. Traditionally, we store real-world data in one or more databases or files. This data is extracted, and features and a target (T) are created and submitted to the “Model Data” stage (the topic of this section). Following the completion of this stage, the model produced is examined (Section V) and placed into production. With the model in the production system, present data generated from the real-world environment is inputted into the system. In the example case of a diabetes patient, we enter a new patient’s information electronic health record into the system, and a database lookup retrieves additional data for feature creation.

Enhancement and evaluation of a prescription audit system for direct oral anticoagulants using a check sheet

Background Renal function and use of concomitant medications should be carefully monitored in patients subjected to treatment with direct oral anticoagulants (DOACs); the dose should be individually designed for each patient. Owing to the complex therapeutic indications and dose reduction criteria, pharmacists exercise caution when determining the optimal dose for each patient. A DOAC check sheet has been developed that is automatically printed in the dispensing room at the same time as the prescription and can be used by pharmacists to dispense DOACs promptly and correctly. The purpose of this study was to evaluate the system for dispensing DOACs using a check sheet. Methods The study was conducted at Tohoku University Hospital in Japan; prescriptions containing DOACs dispensed by the hospital pharmacists were evaluated. The DOAC check sheet described indications, dosage regimens, dose reduction criteria, and contraindications for each drug and included the patient’s information. The check sheet was set to print automatically in the dispensing room at the same time as the prescription when an inpatient was prescribed DOACs. This check sheet was evaluated using a prescription survey and a questionnaire for pharmacists. Results The usefulness of this check sheet for the correct use of DOACs was evaluated. There were four inquiries out of 642 (0.6%) prescriptions from pharmacists to physicians regarding DOAC prescriptions, such as the dose introduced before DOAC check sheet utilization, and there were 21 out of 905 (2.3%) prescriptions when the DOAC check sheet was used it, showing a significant increase (p = 0.0089). After the introduction of this sheet, overdoses of DOACs were identified at the time of dispensing. Of the 52 pharmacists who responded to the questionnaire, 51 (98%) stated that the check sheet was useful. Conclusion The use of the DOAC check sheet is likely to render safety to DOAC drug therapy for individual patients.