Patient Record Information Research Articles

Optimizing Electronic Health Records for Enhanced Clinical Decision Support and Improved Patient Care through Advanced Data Integration and Machine Learning Algorithms

Electronic Health Records (EHRs) have changed the way healthcare is provided by recording patient information, making it easier to handle data, and letting healthcare workers talk to each other without any problems. But EHRs haven't completely come to their full guarantee however when it comes to making strides quiet care and making proficient choices superior. This theoretical talks almost a other way to make strides EHR frameworks by utilizing progressed information integration and machine learning methods. The objective is to create clinical choice back way better and, within the conclusion, make understanding comes about way better. The recommended approach makes the foremost of the colossal sum of information in electronic wellbeing records (EHRs) by combining distinctive sorts of information, such as organized clinical information, unorganized composed notes, restorative images, genetic information, and real-time real signs. Diverse information sources are brought together utilizing progressed information integration strategies to form a full understanding profile that appears the patient's wellbeing state and therapeutic foundation as a entirety. This bound together data environment is the premise for building progressed machine learning models that can draw valuable conclusions, figure how patients will do, and recommend the most excellent care for each individual. Machine learning methods are exceptionally vital for getting valuable data from EHR information. These calculations can discover mystery associations and complex designs by utilizing strategies like profound learning, normal dialect preparing, and expectation modeling. They can also accurately predict clinical events. These models are always learning and getting better, so they can change to new patient data and clinical situations.

A novel smartphone application for bladder cancer management (BCM App): Design, development, and usability evaluation

BACKGROUND: Providing clinical guidelines to clinicians using innovative technologies seems practical and useful. This study aimed to design, develop, and evaluate a smartphone application to assist urologists in managing bladder cancer (BCM App). MATERIALS AND METHODS: The study was conducted in three phases, following the user-centered design model, at the urology clinic of Khorshid Hospital (Isfahan, Iran) in 2021. Components and functionalities for BCM App were specified in three focus group sessions with urologists and urology residents using clinical guidelines. Adopting the Delphi technique, the participants completed a questionnaire addressing issues including educating and informing clinicians, performing clinical calculations, representing graphical information, and recording patient information. A conceptual model for the app was designed, and the final product was developed. The usability of the BCM App was evaluated using the think-aloud technique by usability experts and end-users. RESULTS: The app’s components were categorized into three groups: Patient evaluation, disease staging and management, and patient follow-up. The app’s main components included “bladder cancer classification,” “disease grading and staging,” “management based on the disease stages,” “principles of intravesical treatment,” and “classifying follow-ups by disease stages.” Moreover, the most prominent app’s functionalities were “using staging and grading table,” “using American Urological Association risk stratification,” “calculating disease stages,” “calculating patient’s follow-up,” and “recording patient information.” The usability evaluation results revealed that the BCM App was at a “highly acceptable” level. CONCLUSION: To design and develop a smartphone app for managing bladder cancer, the involvement of urologists is crucial. Apart from assisting urologists in effectively managing bladder cancer, the proposed app has the potential to enhance residents’ knowledge and streamline their access to evidence.

Improving radiology information systems for inclusivity of transgender and gender-diverse patients: what are the problems and what are the solutions? A systematic review.

In medical radiation science (MRS), radiology information systems (RISs) record patient information such as name, gender and birthdate. The purpose of RISs is to ensure the safety and well-being of patients by recording patient data accurately. However, not all RISs appropriately capture gender, sex or other related information of transgender and gender-diverse (TGD) patients, resulting in non-inclusive and discriminatory care. This review synthesises the research surrounding the limitations of RISs preventing inclusivity and the features required to support inclusivity and improve health outcomes. Studies were retrieved from three electronic databases (Scopus, PubMed and Embase). A quality assessment was performed using the Johns Hopkins Nursing Evidence-Based Practice Research and Non-Research Evidence Appraisal Tools. A thematic analysis approach was used to synthesise the included articles. Eighteen articles were included based on the predetermined eligibility criteria. The pool of studies included in this review comprised primarily of non-research evidence and reflected the infancy of this research field and the need for further empirical evidence. The key findings of this review emphasise how current systems do not record the patient's name and pronouns appropriately, conflate sex and gender and treat sex and gender as a binary concept. For current systems to facilitate inclusivity, they must implement more comprehensive information and data models incorporating sex and gender and be more flexible to accommodate the transient and fluid nature of gender. However, implementation of these recommendations is not without challenges. Additionally, further research focused on RISs is required to address the unique challenges MRS settings present to TGD patients.

Predicting in-hospital mortality among patients admitted with a diagnosis of heart failure: a machine learning approach.

Existing risk prediction models for hospitalized heart failure patients are limited. We identified patients hospitalized with a diagnosis of heart failure between 7 May 2013 and 26 April 2022 from a large academic, quaternary care medical centre (training cohort). Demographics, medical comorbidities, vitals, and labs were collected and were used to construct random forest machine learning models to predict in-hospital mortality. Models were compared with logistic regression, and to commonly used heart failure risk scores. The models were subsequently validated in patients hospitalized with a diagnosis of heart failure from a second academic, community medical centre (validation cohort). The entire cohort comprised 21802 patients, of which 14539 were in the training cohort and 7263 were in the validation cohort. The median age (25th-75th percentile) was 70 (58-82) for the entire cohort, 43.2% were female, and 6.7% experienced inpatient mortality. In the overall cohort, 7621 (35.0%) patients had heart failure with reduced ejection fraction (EF≤40%), 1271 (5.8%) had heart failure with mildly reduced EF (EF 41-49%), and 12910 (59.2%) had heart failure with preserved EF (EF≥50%). Random forest models in the validation cohort demonstrated a c-statistic (95% confidence interval) of 0.96 (0.95-0.97), sensitivity (SN) of 87.3%, and specificity (SP) of 90.6% for the prediction of in-hospital mortality. Models for those with HFrEF demonstrated a c-statistic of 0.96 (0.94-0.98), SN 88.2%, and SP 91.0%, and those for patients with HFpEF showed a c-statistic of 0.95 (0.93-0.97), SN 87.4%, and SP 89.5% for predicting in-hospital mortality. The random forest model significantly outperformed logistic regression (c-statistic 0.87, SN 75.9%, and SP 86.9%), and current existing risk scores including the Acute Decompensated Heart Failure National Registry risk score (c-statistic of 0.70, SN 69%, and SP 62%), and the Get With the Guidelines-Heart Failure risk score (c-statistic 0.69, SN 67%, and SP 63%); P<0.001 for comparison. Machine learning models built from commonly recorded patient information can accurately predict in-hospital mortality among patients hospitalized with a diagnosis of heart failure.

Design and evaluation of a web-based electronic health record for amblyopia.

Amblyopia, or lazy eye, is a type of visual impairment in which the eyesight is not complete, even with the use of glasses. For the treatment of this disease, accurate and continuous examinations are needed. Nowadays, patient-centered care, by relying on web-based electronic records for amblyopia, has the potential to reduce treatment costs, increase the quality of care, and improve the safety and effectiveness of treatment. Therefore, the purpose of this study is to design and evaluate an Electronic Health Record (EHR) for patients with amblyopia. The present study is applied developmental research. Using a Morgan table as a sampling tool, a straightforward random sampling technique selected 150 records from 1,500 records that were free of flaws. The design of the electronic version proceeded in a cascading manner so that after the design of each part, it was presented to the amblyopia experts, and if approved, the next part was designed. To design this EHR, the C# programming language and MySQL database were used. A system evaluation was performed by entering and recording patient information. For this purpose, the standard Questionnaire of User Interaction Satisfaction (QUIS), consisting of 18 questions, was used. According to the amblyopia EHR data elements, the data of physician and patient, examinations, website members, and members' roles were determined. After defining the fields and classes that explain the tables, the EHR was designed. The usability evaluation of the system showed that the mean selection of very good and good options by the users of EHRs was over 90%, indicating the patients' acceptance of web-based EHRs. The design of an EHR for amblyopia is an effective step toward integrating and improving the information management of these patients. It will also enable the storage and retrieval of patients' information to reduce and facilitate the control of amblyopia complications.

Optimized whole-genome sequencing workflow for tumor diagnostics in routine pathology practice.

Two decades after the genomics revolution, oncology is rapidly transforming into a genome-driven discipline, yet routine cancer diagnostics is still mainly microscopy based, except for tumor type-specific predictive molecular tests. Pathology laboratories struggle to quickly validate and adopt biomarkers identified by genomics studies of new targeted therapies. Consequently, clinical implementation of newly approved biomarkers suffers substantial delays, leading to unequal patient access to these therapies. Whole-genome sequencing (WGS) can successfully address these challenges by providing a stable molecular diagnostic platform that allows detection of a multitude of genomic alterations in a single cost-efficient assay and facilitating rapid implementation, as well as by the development of new genomic biomarkers. Recently, the Whole-genome sequencing Implementation in standard Diagnostics for Every cancer patient (WIDE) study demonstrated that WGS is a feasible and clinically valid technique in routine clinical practice with a turnaround time of 11 workdays. As a result, WGS was successfully implemented at the Netherlands Cancer Institute as part of routine diagnostics in January 2021. The success of implementing WGS has relied on adhering to a comprehensive protocol including recording patient information, sample collection, shipment and storage logistics, sequencing data interpretation and reporting, integration into clinical decision-making and data usage. This protocol describes the use of fresh-frozen samples that are necessary for WGS but can be challenging to implement in pathology laboratories accustomed to using formalin-fixed paraffin-embedded samples. In addition, the protocol outlines key considerations to guide uptake of WGS in routine clinical care in hospitals worldwide.

The Effectiveness of a Structured Training Program in Transforming to an Electronic System in Promoting the Quality of Nursing Care

Background: In today’s dynamic health systems, technology plays an essential role in education and nursing practice. Therefore, it is necessary to study the changing role of nurses and highlight the need for appropriate information technology educational programs to integrate with the ever-increasing pace of technology. Nursing informatics helps improve vital nursing processes, such as documentation, which is a critical aspect of the profession and essential for effective patient care. Before electronic health records, nurses recorded patient information on charts, which could easily be mismanaged. Today, nursing informatics simplifies documentation and automates the transmission of patient data via connected devices to provide access for nurses, physicians, and patients.

High imperceptibility and robustness watermarking scheme for brain MRI using Slantlet transform coupled with enhanced knight tour algorithm

This research introduces a novel and robust watermarking scheme for medical Brain MRI DICOM images, addressing the challenge of maintaining high imperceptibility and robustness simultaneously. The scheme ensures privacy control, content authentication, and protection against the detachment of vital Electronic Patient Record information. To enhance imperceptibility, a Dynamic Visibility Threshold parameter leveraging the Human Visual System is introduced. Embeddable Zones and Non-Embeddable Zones are defined to enhance robustness, and an enhanced Knight Tour algorithm based on Slantlet Transform shuffles the embedding sequence for added security. The scheme achieves remarkable results with a Peak Signal-to-Noise Ratio (PSNR) evaluation surpassing contemporary techniques. Extensive experimentation demonstrates resilience to various attacks, with low Bit Error Rate (BER) and high Normalized Cross-Correlation (NCC) values. The proposed technique outperforms existing methods, emphasizing its superior performance and effectiveness in medical image watermarking.

Basilar Skull Fractures and Their Complications in Patients With Traumatic Brain Injury.

Since traumatic brain injury is more common in young people, who are the main workforce and builders of society, it is important to consider the effects caused by brain injury on them. In this study, we investigated the clinical manifestations, complications, and prognosis of patients with basilar skull fractures. This cross-sectional study was conducted from March 2021 to March 2022 at the Kashani Hospital, Esfahan, Iran. Patients with basilar skull fractures were included in this study by census for one year. Recorded patient information was divided into two parts: demographic information, including age and sex, and disease information including loss of consciousness, signs of meningitis, need for surgery, and neurologic examination. In this study, 100 patients were included, of whom 89 were men. The most common complication was pain at the site of the trauma, followed by bruising and bleeding from the site of the trauma. Raccoon eyes and cerebrospinal fluid leakage were observed in 19% and 32% of the patients, respectively. As the occurrence of trauma has an economic burden on the country's health system, we must identify its consequences and problems and prevent its occurrence as much as possible by implementing educational measures.

Design and implementation of a smart Internet of Things chest pain center based on deep learning.

The data input process for most chest pain centers is not intelligent, requiring a lot of staff to manually input patient information. This leads to problems such as long processing times, high potential for errors, an inability to access patient data in a timely manner and an increasing workload. To address the challenge, an Internet of Things (IoT)-driven chest pain center is designed, which crosses the sensing layer, network layer and application layer. The system enables the construction of intelligent chest pain management through a pre-hospital app, Ultra-Wideband (UWB) positioning, and in-hospital treatment. The pre-hospital app is provided to emergency medical services (EMS) centers, which allows them to record patient information in advance and keep it synchronized with the hospital's database, reducing the time needed for treatment. UWB positioning obtains the patient's hospital information through the zero-dimensional base station and the corresponding calculation engine, and in-hospital treatment involves automatic acquisition of patient information through web and mobile applications. The system also introduces the Bidirectional Long Short-Term Memory (BiLSTM)-Conditional Random Field (CRF)-based algorithm to train electronic medical record information for chest pain patients, extracting the patient's chest pain clinical symptoms. The resulting data are saved in the chest pain patient database and uploaded to the national chest pain center. The system has been used in Liaoning Provincial People's Hospital, and its subsequent assistance to doctors and nurses in collaborative treatment, data feedback and analysis is of great significance.

Complete functional and esthetic rehabilitation using full digital workflow and equipment

A 66-year-old male patient applied for dental rehabilitation due to esthetic and functional problems. Decrease of chewing ability and inferior esthetics were detectable due to his worn dentition and old PFM restorations. After taking photos and intraoral scans, a complete digital functional registration was recorded with the MODJAW system. By recording all the important functional motions, a new Vertical Dimension was selected within the patient's centric relation. Dental design for creating a digital waxup was done in exocad and 3Shape Dental Designer software using all the recorded patient information. The final design was physically created by 3D printing and transferred to the patient's mouth for functional and esthetic verification. After acceptance of the pre-design, removal of old prosthetics and precisional preparation were done and followed with digital impression taking. Final restoration shapes were copied from original wax up, and milling of the permanent restorations was done using IPS e.max ZirCAD Multi A1 material in Ivoclar PM7 milling machine. Staining and glazing of the restorations were created from IPS Ivocolor. Cementation was done adhesively, using SpeedCEM Plus self-adhesive, selfcuring resin cement. At the delivery stage a smile makeover was completed for the patient including 24 single-unit crowns and a three unit bridge. Vertical dimension was elevated and esthetics were improved. Digital technology using functional motion registration and 3D printed trials is a predictable way to securely apply full mouth rehabilitation workflows.

Improving care for hypertension and diabetes in india by addition of clinical decision support system and task shifting in the national NCD program: I-TREC model of care

BackgroundThe growing burden of hypertension and diabetes is one of the major public health challenges being faced by the health system in India. Clinical Decision Support Systems (CDSS) that assist with tailoring evidence-based management approaches combined with task-shifting from more specialized to less specialized providers may together enhance the impact of a program. We sought to integrate a technology “CDSS” and a strategy “Task-shifting” within the Government of India’s (GoI) Non-Communicable Diseases (NCD) System under the Comprehensive Primary Health Care (CPHC) initiative to enhance the program’s impact to address the growing burden of hypertension and diabetes in India.MethodsWe developed a model of care “I-TREC” entirely calibrated for implementation within the current health system across all facility types (Primary Health Centre, Community Health Centre, and District Hospital) in a block in Shaheed Bhagat Singh (SBS) Nagar district of Punjab, India. We undertook an academic-community partnership to incorporate the combination of a CDSS with task-shifting into the GoI CPHC-NCD system, a platform that assists healthcare providers to record patient information for routine NCD care. Academic partners developed clinical algorithms, a revised clinic workflow, and provider training modules with iterative collaboration and consultation with government and technology partners to incorporate CDSS within the existing system.DiscussionThe CDSS-enabled GoI CPHC-NCD system provides evidence-based recommendations for hypertension and diabetes; threshold-based prompts to assure referral mechanism across health facilities; integrated patient database, and care coordination through workflow management and dashboard alerts. To enable efficient implementation, modifications were made in the patient workflow and the fulcrum of the use of technology shifted from physician to nurse.ConclusionDesigned to be applicable nationwide, the I-TREC model of care is being piloted in a block in the state of Punjab, India. Learnings from I-TREC will provide a roadmap to other public health experts to integrate and adapt their interventions at the national level.Trial registrationCTRI/2020/01/022723.

Incidence of Adverse Events Associated With the In-Hospital Transport of Critically Ill Patients.

IMPORTANCE:Despite various reports on the incidence of adverse events related to the in-hospital transport of critically ill patients, there is little verification of the correlation between the occurrence of adverse events and the use of checklists. The risk factors for the occurrence of adverse events during transport based on the use of checklists have not been well studied. Understanding them can contribute to making patient transport safer.OBJECTIVES:We aimed to investigate the frequency of adverse events and risk factors related to the in-hospital transport of critically ill patients in a hospital that uses a checklist for transporting patients.DESIGN, SETTING, AND PARTICIPANTS:This single-center, prospective, observational study was conducted between February 1, 2020, and July 31, 2020, at Kobe City Medical Center General Hospital, Japan. Patients greater than or equal to 18 years old who were admitted to the ICU and were transported for examination or procedures were included.MAIN OUTCOMES AND MEASURES:The transport member recorded patient information and any adverse events that occurred and filled out an information collection form. We then applied multivariate analysis to identify risk factors.RESULTS:A total of 117 transports for 117 patients were evaluated in this study. Twenty-two adverse events occurred in 20 transports (17.1%). There were nine transports (7.7%) in which the patients required treatment, all of which were related to patient instability. Multivariate logistic regression analysis showed that the use of sedative drugs was related to adverse events (odds ratio, 2.9; 95% CI, 1.0–8.5; p = 0.04). We were not able to show a relationship of either the severity of the illness or body mass index with the occurrence of adverse events.CONCLUSIONS AND RELEVANCE:This study revealed that the frequency of adverse events related to the in-hospital transportation of critically ill patients based on the use of a checklist was 17.1% and that the use of sedatives was associated with adverse events.

Modelling of IoT-WSN Enabled ECG Monitoring System for Patient Queue Updation

The advancement of communication technologies has led to the interconnection of different sensors using the Internet of Things (IoT) and Wireless Sensor Network (WSN). WSN for healthcare applications has expanded exponentially due to evolving advantages such as low power requirement of sensors, transmission accuracy, and cost-efficiency. For heart attack patients, the future lies in ECG monitoring in which wearable sensors can be used to acquire patient information. In this paper, an attempt has been made to develop a novel IoT-enabled WSN to record patient information for detection of heart attack and to update queue of patients to ensure prioritized medical attention to critical patients. In the WSN, the Rayleigh Fading channel has been used to transmit data that can be accessed using the cloud repository by the medical staff remotely. The distance from the patient to the medical staff is calculated using Euclidean distance. Further, SNR in comparison to throughput and BER has been computed. The higher SNR indicates the maximum information transfer from patient to hospital staff. The proposed system uses the Grasshopper Optimization and CBNN based disease classification system and bubble sort algorithm has been used for updating patient queue. The proposed GHOA and CBNN has shown improved accuracy of 2.14% over existing techniques like CNN which has accuracy around 82% for R-R feature selection of ECG signals as compared to 82.72% shown by GHOA-CBNN.

MHealth apps for gestational diabetes mellitus that provide clinical decision support or artificial intelligence: A scoping review.

Gestational diabetes (GDM) is the most common metabolic disorder of pregnancy, requiring complex management and empowerment of those affected. Mobile health (mHealth) applications (apps) are proposed for streamlining healthcare service delivery, extending care relationships into the community, and empowering those affected by prolonged medical disorders to be equal collaborators in their healthcare. This review investigates mHealth apps intended for use with GDM; specifically those powered by artificial intelligence (AI) or providing decision support. A scoping review using the novel Survey Tool approach for collaborative literature Reviews (STaR) process was performed. From 18 papers, 11 discrete GDM-based mHealth apps were identified, but only 3 were reasonably mature with only one currently in use in a clinical setting. Two-thirds of the apps provided condition-relevant contextual user feedback that could aid in patient self care. However, although each app targeted one or more components of the GDM clinical pathway, no app addressed the entirety from diagnosis to postpartum. There are limited mHealth apps for GDM that incorporate AI or AI-based decision support. Many exist only to record patient information like blood glucose readings or diet, provide generic patient education or advice, or to reduce adverse events by providing medication or appointment alerts. Significant barriers remain that continue to limit the adoption of mHealth apps in clinical care settings. Further research and development are needed to deliver intelligent holistic mHealth apps using AI that can truly reduce healthcare resource use and improve outcomes by enabling patient self care in the community.

The Efficacy of Combined VHIT and VNG in the Diagnosis of Vertigo Caused by Meniere's Disease.

This study aimed to evaluate the efficacy of video head impulse test (VHIT) and Videonystagmography (VNG) diagnostic tests in the diagnosis of vertigo caused by Meniere's disease. In this cross-sectional study 20 patients (10 in Meniere's group and 10 in control group) with vertigo attacks were involved. Patients were diagnosed with Meniere's disease (according to clinical criteria) or acute vertigo due to other causes after taking a history, complete examination, audiometry screening, and recording patient information in a pre-prepared checklist. Patients were referred to an audiology clinic for performing ECOG, VHIT, and VNG. All variables and sensitivity, specificity, positive predictive value, and negative predictive value were recorded and analyzed. There was no statistical difference between two groups regarding the mean age and gender of the patients (p > 0.05). The results showed no statistically significant difference between the study groups regarding the frequency distribution of ECOG, VNG, VHIT results. Also the results showed no statistically significant difference between the study groups regarding the frequency distribution of combined VNG and VHIT results. Combined VNG and VHIT had a relatively low sensitivity but high specificity in diagnosing Meniere's disease compared with the ECOG test, which had a sensitivity of 70% and a specificity of 90%. At the time of attack in this study, combined VNG and VHIT had a sensitivity of 50% and a specificity of 90%. According to the results of the current study, it can be concluded that patients in whom the result of combined VNG and VHIT was positive for Meniere's were more likely to have Meniere's disease based on clinical criteria.

Barcode scanning of human milk and enteral formulas improves efficiency and patient safety: A 7-year review.

Safe handling of human milk (HM) and enteral formulas for patients of all ages is critical in healthcare. Barcode scanning is commonly used to reduce errors. The purpose of this study was to evaluate the impact of barcode scanning for HM and formulas on patient safety. Scanning is used at the time of feeding preparation for HM, facility-prepared formulas, and ready-to-feed enteral formulas for patients of all ages to confirm the feeding matches the provider order, record lot numbers, and ensure expired items are not used. HM feedings are scanned at the time of administration and discharge to ensure the correct milk is being provided to the correct patient. All formulas dispensed as samples to hospital inpatients and outpatients are scanned to record patient and product information in the event of a recall. Seven-year data showed that scanning has prevented HM misadministration 1226 times. Data for 2 and a half years of fortifier and formula scanning show 480 errors have been prevented. The results show benefits for both patient safety and staff efficiency. The benefits of barcode scanning systems in healthcare are well documented. Beyond the scanning of HM, scanning of fortifiers and formulas can improve patient safety by preventing misadministration for patients of all ages. Systems that offer features such as calculating fortified HM and formula recipes, tracking product lot numbers, and verifying correct products to the provider order can further improve safety as well as efficiency.

Comparison of Clinical and Laboratory Findings and Computed Tomography Findings of SARS-CoV-2 Infected Patients Followed-up in a Tertiary University Hospital

Introduction: The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection is a pandemic, a major global health concern. In this study, it was aimed to compare the clinical, laboratory and computed tomography (CT) findings of patients with SARS-CoV-2 infection followed up in our hospital. Materials and Methods: In this study, reverse transcriptase-polymerase chain reaction (RT-PCR) positive patients hospitalized between 01.03.2020-31.05.2020 were retrospectively analyzed. Computed tomography images of the patients were grouped as typical, indeterminate, atypical, and no pneumonia based on the Radiological Society of North America. After recording patient information on SPSS, clinical and laboratory findings of the patients were analyzed by comparing them to CT findings. Results: Among 237 RT-PCR positive patients, 104 (43.9%) were female and 133 (56.1%) were male. The mean age of the patients was 50.46 +/- 17.26 (18-92) years and the mean symptom onset time of the patients was 3.75 +/- 2.72 (median: 3) days. Eighty-seven of the patients (36.7%) had contact stories. Twenty-three (9.7%) patients were healthcare professionals. Of the patients, 49.8% had a comorbid disease. The most common referral complaint was cough with 66.7%. The most common treatment that patients received was hydroxychloroquine (96.2%). Anemia was detected in 61 (25.7%) patients, leukopenia in 104 (43.9%), lymphopenia in 25 (10.5%) and thrombocytopenia in 14 (5.9%). High rates were detected for C-reactive protein (CRP) in 221 (84%) patients, ferritin in 190 (80.2%) patients, D-dimer in 144 (60.8%) patients, fibrinogen in 147 (62%) patients and sedimentation (SED) in 172 (72.6%) patients. Headache was detected higher in patients with typical pneumonia findings in thorax CT (p=0.006). A statistically significant difference wasn't detected between other symptoms and CT findings. Leukocyte and neutrophil counts, SED, CRP, ferritin, D-dimer, fibrinogen, aspartate aminotransferase, and lactate dehydrogenase (p=0.001) levels were observed to be higher in patients with typical pneumonia findings on thorax CT. Conclusion: Some laboratory parameters, especially acute phase reactants, were found to be higher in patients with typical pneumonia on thorax CT compared to patients without pneumonia. In this viral infection, patients should be evaluated together with clinical, laboratory and CT findings.

The use of asymmetry and excess estimates to verify the results of medical observations on indicators for normality

Medical specialists know that most of their activities are related to information and its management, for example, obtaining and recording patient information, a consulting board, reading and evaluating scientific literature, planning diagnostic procedures, developing strategies for patient care, interpreting laboratory results and radiological research or working based on research. Statistical processing of the results of medical observations should be subordinate to the goals and objectives of specific studies. The choice of an adequate test in order to compare the indicators is a rather complicated process, since the researcher needs to choose between two families of tests - parametric and nonparametric. Many statistical tests are based on the assumption that the data were obtained as a sample from the normal distribution.

Lung disease classification using machine learning algorithms

In this study we compared support vector machines (SVM), k-nearest neighbor (k-NN), and Gaussian Bayes (GB) algorithms in classification of respiratory diseases with text and audio data. An electronic stethoscope and its software are used to record patient information and 17930 lung sounds from 1630 subjects. SVM, k-NN and GB algorithms were run on 6 datasets to classify patients into; (1) sick or healthy with text data, (2) sick or healthy with audio MFCC features, (3) sick or healthy with the text data and audio MFCC features, (4) 12 diseases with text data, (5) for 12 disease with audio MFCC features, (6) for 12 disease with the text data and audio MFCC features. Accuracy results in SVM were %75, %88, %64, %73, %63, %70; for k-NN %95, %92, %92, %67, %64, %66; for GB %98, %91, %97, %58, %48, %58 respectively. In 12 class classification of lung diseases, the most accurate algorithm was SVM with text data. In classifying via audio data, k-NN was the most accurate. Using both audio and text data, SVM was the most accurate. When we classify healthy versus sick via text, audio and combined data, GB was always the most accurate with very high accuracy, closely followed by k-NN. We can infer from here that when we have large number of features but limited amount of samples, SVM and k-NN are best in classifying the dataset in more than two classes. However GB is best when it comes to classifying into two classes.