Medical Prognosis Research Articles

Comparative Analysis of Advanced Data Mining Methods for Enhancing Medical Diagnosis and Prognosis

Accurate and early diagnosis, coupled with precise prognosis, is critical for improving patient outcomes in various medical conditions. This paper focuses on leveraging advanced data mining techniques to address two key medical challenges: diagnosis and prognosis. Diagnosis involves differentiating between benign and malignant conditions, while prognosis aims to predict the likelihood of recurrence after treatment. Despite significant advances in medical imaging and clinical data collection, achieving high accuracy in both diagnosis and prognosis remains a challenge. This study provides a comprehensive review of state-of-the-art machine learning and data mining techniques used for medical diagnosis and prognosis, including Neural Networks, K-Nearest Neighbors (KNN), Naïve Bayes, Logistic Regression, Decision Trees, and Support Vector Machines (SVM). These methods are evaluated on their ability to process large, complex datasets and produce actionable insights for medical practitioners.We conducted a thorough comparative analysis based on key performance metrics such as accuracy, Area Under the Curve (AUC), precision, recall, and specificity. Our findings reveal that Neural Networks consistently outperform other techniques in terms of diagnostic accuracy and predictive capacity, demonstrating their robustness in handling high-dimensional and nonlinear medical data. This research underscores the potential of advanced machine learning algorithms in revolutionizing early diagnosis and effective prognosis, thus facilitating more personalized treatment plans and improved healthcare outcomes.

Prediction of in-hospital mortality risk for patients with acute ST-elevation myocardial infarction after primary PCI based on predictors selected by GRACE score and two feature selection methods.

Accurate in-hospital mortality prediction following percutaneous coronary intervention (PCI) is crucial for clinical decision-making. Machine Learning (ML) and Data Mining methods have shown promise in improving medical prognosis accuracy. We analyzed a dataset of 4,677 patients from the Regional Vascular Center of Primorsky Regional Clinical Hospital No. 1 in Vladivostok, collected between 2015 and 2021. We utilized Extreme Gradient Boosting, Histogram Gradient Boosting, Light Gradient Boosting, and Stochastic Gradient Boosting for mortality risk prediction after primary PCI in patients with acute ST-elevation myocardial infarction. Model selection was performed using Monte Carlo Cross-validation. Feature selection was enhanced through Recursive Feature Elimination (RFE) and Shapley Additive Explanations (SHAP). We further developed hybrid models using Augmented Grey Wolf Optimizer (AGWO), Bald Eagle Search Optimization (BES), Golden Jackal Optimizer (GJO), and Puma Optimizer (PO), integrating features selected by these methods with the traditional GRACE score. The hybrid models demonstrated superior prediction accuracy. In scenario (1), utilizing GRACE scale features, the Light Gradient Boosting Machine (LGBM) and Extreme Gradient Boosting (XGB) models optimized with BES achieved Recall values of 0.944 and 0.954, respectively. In scenarios (2) and (3), employing SHAP and RFE-selected features, the LGB models attained Recall values of 0.963 and 0.977, while the XGB models achieved 0.978 and 0.99. The study indicates that ML models, particularly the XGB optimized with BES, can outperform the conventional GRACE score in predicting in-hospital mortality. The hybrid models' enhanced accuracy presents a significant step forward in risk assessment for patients post-PCI, offering a potential alternative to existing clinical tools. These findings underscore the potential of ML in optimizing patient care and outcomes in cardiovascular medicine.

Disclosure of true medical information: the case of Bangladesh

BackgroundTruth-telling in health care is about providing patients with accurate information about their diagnoses and prognoses to enable them to make decisions that can benefit their overall health. Physicians worldwide, especially in the United Kingdom (U.K.) and the United States (U.S.), openly share such medical information. Bangladesh, however, is a Muslim-majority society with different social norms than Western societies. Therefore, we examined whether Muslim culture supports truth disclosure for patients, particularly how and to what extent medical information about life-threatening diseases is provided to patients in Bangladesh.MethodsThis was a phenomenological qualitative study. We conducted thirty in-depth interviews with clinicians, nurses, patients and their relatives at Shaheed Suhrawardy Medical College Hospital in Dhaka, Bangladesh. We also used observations to explore interactions between patients, families and healthcare professionals regarding their involvement in medical decisions and truth disclosure issues. NVivo software was used to identify common themes, and a thematic analysis method was utilised to analyse the datasets.ResultsThis study identified three recurring themes relevant to the ethics and practice of truth disclosure: best interest rather than autonomy, the ambivalent value of deception and who understands what. The participants revealed that physicians often withhold fatal medical prognoses from terminally ill patients to ensure the best healthcare outcomes. The results indicate that deception towards patients is commonly accepted as a means of reducing burden and providing comfort. The participants opined that true medical information should be withheld from some patients, assuming that such disclosures may create a severe burden on them. Whether or to what extent medical information is disclosed primarily depends on a family’s wishes and preferences.ConclusionsWhile truth disclosure to patients is considered an ethical norm in many cultures, such as in the U.K. and the U.S., the practice of concealing or partially revealing severe medical prognoses to patients is an actual medical practice in Bangladeshi society. This study emphasises the importance of recognising a patient’s active involvement and respecting the cultural values that shape family involvement in medical decision-making. These findings may have significant policy and practical implications for promoting patient autonomy within Bangladeshi family dynamics and religious-based cultural values.

Clinical study protocol on assessment of quality of medical care and clinical prognosis in patients who underwent myocardial revascularization accounting modifiable risk factors

Introduction. Every year in Russia, the number of people who have undergone cardiac surgery exceeds 600 thousand. Among them, anemia occurs in 30-70% of cases, and cognitive dysfunction is noted in 20-80% of cases. Anemia increases the risk of death 4-fold within a year, 5-fold the risk of stent thrombosis, 1.3–fold the risk of recurrence of acute coronary syndrome and 2-fold the risk of bleeding, worsens the quality of life, cognitive status and compliance. Unresolved problems of managing medical care for specialized patients are associated with insufficient coupling of components of clinical and socio-psychological interaction, taking into account modifiable risk factors, and irrational use of limited industry resources distributed according to the quota mechanism. Patient-centered digital systems contribute to improving the quality of life of patients, optimizing the use of healthcare resources.Aim: To evaluate the parameters of the quality of medical care and clinical prognosis in patients who underwent myocardial revascularization, taking into account an expanded range of modifiable risk factors, including anemia, cognitive dysfunction and depression, using a developed digital intelligent data collection system.Material and Methods. A prospective cohort study of 300 adult patients who underwent myocardial revascularization is planned for 12 months in 3 groups (depending on the consent and technical feasibility of using a digital information system (CIS): 1 group will undergo questionnaires and cognitive behavioral training online through CIS; 2 group will undergo only questionnaires through CIS; 3 group will not use CIS. There are 2 face-to-face visits to the doctor (6 and 12 months), at which the following groups of endpoints will be evaluated: clinical outcomes; parameters of the quality of medical care and rehabilitation; patient-reported experience indicator (PREM). Validated and authored self-questionnaires will be used.

Advanced Prediction of Glomerular Filtration Rate After Kidney Transplantation Using Gradient Boosting Techniques.

Clinicians often face uncertainty when interpreting whether a decline in estimated glomerular filtration rate is within the patient's expected range of fluctuation or if the decline signals a substantial deviation. Thus, accurate predictions of glomerular filtration rate can be an early warning system, prompting timely interventions, such as biopsies to preclude early graft rejection and adjustments in immunosuppression. Traditional models, encompassing linear and conventional methods, typically struggle with variabilities and complexities in posttransplant data. We evaluated the efficacy of a gradient boosting model in predicting posttransplant glomerular filtration rate, to potentially enhance accuracy over traditional prediction approaches. Our patient dataset included 68 pediatric patients aged 1 to 18 years who underwent kidney transplant between 2017 and 2023 at Baskent University Hospital (Ankara, Turkey). The dataset comprised 2285 glomerular filtration rate measurements, along with patient demographics and transplant-related data. For our model, we included "days to transplant" (glomerular filtration rate values pretransplant), "days from transplant" (glomerular filtration rate values up to 7 days posttransplant), patient age, sex, and donor types. We divided the dataset into a training set (70%) and a test set (30%). To evaluate model performance, we used mean absolute error and root mean squared error, with a focus on the accuracy of glomerular filtration rate predictions at various posttransplant stages. In the training set, the gradient boosting model demonstrated a significant improvement in prediction accuracy, achieving an mean absolute error of ~5.64 mL/min/1.73 m². Our model underscored the promise of advanced machine learning techniques in refining prediction of glomerular filtration rate after kidney transplant. With its augmented precision, the model can support clinicians in making informed decisions regarding early biopsies and interventions, thus highlighting the vital role of sophisticated analytical methods in medical prognosis and the monitoring of pediatric patient care.

B - 8 Preemie-Pediatric Left Middle Cerebral Arterial Cerebrovascular Accident and Comorbid Focal Left Central Epilepsy: a Case Study

Abstract Objective Literature regarding neurocognitive functioning in pediatric middle cerebral arterial (MCA) is limited. The aim of this study was to enhance the field’s knowledge and understanding of the presentation and impact of preemie-pediatric left MCA in utero with comorbid left-brain epilepsy in overall neurocognitive and behavioral functioning. Method The subject is a 12-year-old left-handed male with history of previously described events. The purpose of this evaluation was to determine current levels of neurocognitive and behavioral functioning as it relates to his medical history, treatment planning, and prognosis. Data collection was part of a pre-surgical consult to reduce epileptic activity. Results Results suggest overall cognitive functioning fell within the average range. However, relative difficulties in areas of attention and executive functioning were indicated. While all other domains were grossly intact, his profile indicates strengths in visual compared to verbal memory. Interestingly, the patient performed better on verbal tasks after a delay both with and without cuing. This suggests the presentation of a unique trait of pediatric epilepsy in that due to slowed processing speed as seen on the WISC, this patient is likely to remember more information over time. His academic achievements also fell within age-expectations. Conclusions This study impresses the importance of prompt neuropsychological evaluation in providing insight to the patient’s impairments and contribute to adaptive neurocognitive functioning moving forward. While idiopathic, this case demonstrates the need for continued research in pediatric-infantile infarct and provides the opportunity to improve the field’s understanding of the interpretation of similar cases.

Medical costs in the peridiagnosis period of various biopsy-confirmed kidney diseases in South Korea.

In-depth investigation is imperative to scrutinize medical costs associated with the periods before and after biopsies for diverse kidney diseases in South Korea. Long-term epidemiological data, including follow-up information, is essential for comparing risks linked to various kidney diseases and their adverse outcomes. Patients diagnosed with glomerulonephritis (GN), tubulointerstitial nephritis (TIN), and acute tubular necrosis (ATN) at Seoul National University Hospital between 2012 and 2018 were included. We linked the prospective cohort data of biopsy-confirmed kidney disease patients (KORNERSTONE) from our study hospital to the national claims database of Korea, covering both medical events and insured costs. We analyzed medical costs during the periods before and after kidney biopsies, categorized by specific diagnoses, and delved into adverse prognostic outcomes. Our study involved 1,390 patients with biopsy-confirmed GN, TIN, and ATN. After diagnosis, monthly average medical costs increased for most kidney diseases, excluding membranous nephropathy, Henoch-Schönlein purpura, and amyloidosis. The most substantial yearly average medical cost increase was observed in the ATN, acute TIN (ATIN), and chronic TIN (CTIN) groups. Costs rose for most kidney disease categories, except for amyloidosis. Higher myocardial infarction, stroke, and death rates were noted in CTIN, ATIN, and ATN compared to other types, with lupus nephritis displaying the highest end-stage kidney disease progression rate. In South Korea, medical costs for the majority of GN, TIN, and ATN patients increased following kidney biopsy diagnosis. This current data provides valuable epidemiological insights into the medical costs and prognosis of various kidney diseases in the country.

The Power of a Belief System: A Systematic Qualitative Synthesis of Spiritual Care for Patients with Brain Tumors.

Background: Diagnosis with a brain tumor is a critical event in the lives of patients and their families due to poor medical prognoses and complex clinical care. Spiritual care interventions have been known to have meaningful effects in morbid diagnoses and palliative medicine, but their role in the neuro-oncologic patient's experience is poorly understood. This systematic review explores the role of spirituality and its relevance to patient care in the diverse setting of brain tumors. Methods: A comprehensive systematic review was conducted following PRISMA-SR guidelines. PUBMED was queried for studies on spirituality and neuro-oncology. Identified studies included RCTs, interviews, surveys, and case reports that examined spirituality in neuro-oncological clinical care, quality of life, and patient experience. Of 214 articles identified, 21 studies met the inclusion criteria, and the results were narratively synthesized. Results: Spirituality may play a significant role in mental well-being by reconciling existential questions faced by both patients and caregivers, and can serve as a valuable resource to improve mental well-being and reduce rates of palliative caregiver burnout. However, the paucity of studies examining the education and integration of spiritual awareness within the clinical literature warrants further study. Conclusions: While spiritual care interventions may improve the quality of life and mental wellness of patients and their caregivers, it is unclear how spiritual awareness and education should best be implemented. Further research is needed to better understand how key components of spiritual awareness can be integrated into medical education to deepen the patient-physician relationship and improve clinical experiences.

Accuracy of Medical Oncology Prognosis for Metastatic Cancer Patients Evaluated for Enrollment Onto an Ongoing Randomized Clinical Trial.

For patients with metastatic cancer, a key aspect of interdisciplinary care has involved the overall prognosis provided by Medical Oncology. This study represents prospective evaluation of Medical Oncology prognosis accuracy for patients considered for enrollment onto an ongoing randomized controlled trial. The Spine Patient Optimal Radiosurgery Treatment for Symptomatic Metastatic Neoplasms (SPORTSMEN) phase 2 randomized clinical trial examines optimal radiation therapy treatment of symptomatic spinal metastases with a primary end point of pain freedom at 3 months post-treatment. A key eligibility criterion for trial enrollment is overall prognosis exceeding 3 months, typically provided by Medical Oncology. During the first year of trial enrollment, Medical Oncology prognosis for patients considered for SPORTSMEN inclusion was prospectively assessed for accuracy. Twenty-seven patients with documented Medical Oncology prognosis were considered for SPORTSMEN enrollment. The prognosis administered by Medical Oncology exceeded 3 months in 26 patients, and <3 months in 1 patient. The overall accuracy of Medical Oncology prognosis was correct for 15 of 27 patients (56%), significantly worse for inpatients than outpatients ( P =0.0381). In patients with metastatic spine disease, the estimated prognosis provided by Medical Oncology is often optimistic, as nearly half of patients assigned a prognosis of >3 months failed to reach this threshold before experiencing death or hospice. These findings indicate that a more heuristic approach to assessing patient prognosis may be necessary to avoid unwarranted prognostic optimism, particularly for inpatients. Such an approach could potentially provide a more compassionate and cost-effective management of these patients' remaining lifespan thereby optimizing quality of life.

Homogeneous Ensemble Feature Selection for Mass Spectrometry Data Prediction in Cancer Studies

The identification of important proteins is critical for the medical diagnosis and prognosis of common diseases. Diverse sets of computational tools have been developed for omics data reduction and protein selection. However, standard statistical models with single-feature selection involve the multi-testing burden of low power with limited available samples. Furthermore, high correlations among proteins with high redundancy and moderate effects often lead to unstable selections and cause reproducibility issues. Ensemble feature selection in machine learning (ML) may identify a stable set of disease biomarkers that could improve the prediction performance of subsequent classification models and thereby simplify their interpretability. In this study, we developed a three-stage homogeneous ensemble feature selection (HEFS) approach for both identifying proteins and improving prediction accuracy. This approach was implemented and applied to ovarian cancer proteogenomics datasets comprising (1) binary putative homologous recombination deficiency (HRD)- positive or -negative samples; (2) multiple mRNA classes (differentiated, proliferative, immunoreactive, mesenchymal, and unknown samples). We conducted and compared various ML methods with HEFS including random forest (RF), support vector machine (SVM), and neural network (NN) for predicting both binary and multiple-class outcomes. The results indicated that the prediction accuracies varied for both binary and multiple-class classifications using various ML approaches with the proposed HEFS method. RF and NN provided better prediction accuracies than simple Naive Bayes or logistic models. For binary outcomes, with a sample size of 122 and nine selected prediction proteins using our proposed three-stage HEFS approach, the best ensemble ML (Treebag) achieved 83% accuracy, 85% sensitivity, and 81% specificity. For multiple (five)-class outcomes, the proposed HEFS-selected proteins combined with Principal Component Analysis (PCA) in NN resulted in prediction accuracies for multiple-class classifications ranging from 75% to 96% for each of the five classes. Despite the different prediction accuracies of the various models, HEFS identified consistent sets of proteins linked to the binary and multiple-class outcomes.

Embodied Relationality and Alterity in Verena Stefan’s Fremdschläfer

This article explores Verena Stefan’s images of non-oppositional relationality, relations between the self and other beings in the world, and Leib, the living body in her 2007 novel Fremdschläfer which is presented as an assemblage of sections with little temporal and spatial continuity. I examine relationality with reference to a nexus of ontological questions pertaining to non-oppositional models of relational selfhood, alterity and immanence and the relation between empirical experience and the conditions of experience. My analysis of the multilayered imagery in Fremdschläfer focuses on how these poetic images engender self-world relationality, being together with others, heteronomy and intersubjectivity, while also foregrounding the experience of immigration, multilingualism and living with a poor medical prognosis. I argue that Stefan figures the Leib (living body) as a sensible transcendental. It is the touch of and embodied listening by other material embodied selves that give shape to the self’s changing boundaries.

Cystic fibrosis-related metabolic defects: crosstalk between ion channels and organs.

Cystic fibrosis is a debilitating disease characterized by a poor medical prognosis due to devastating lung injury. Recent medical advances targeting the major genetic mutation ΔF508 of the cystic fibrosis transmembrane conductance regulator (CFTR) protein have dramatically increased the lifespan of patients with this mutation. This development has led to major changes in the field and has pushed research beyond the ion transport nature of cystic fibrosis and toward multiorgan physiological reprogramming. In this issue of the JCI, Bae, Kim, and colleagues utilized a large animal pig model prior to the onset of disease. They revealed metabolic reprogramming and organ crosstalk that occurred prior to disease progression. These findings provide paradigm-shifting insight into this complex disease.

Characteristics and health problems of cancer patients admitted to palliative care service at the Oncology Hospital in Ho Chi Minh City, Vietnam: a cross-sectional study

Vietnam has very few study on health problems of cancer patients receiving palliative care. Therefore, this study aimed to estimate the prevalence of characteristics and health problems of cancer patients who admitted to the palliative care department of the Ho Chi Minh City Hospital of Oncology. A cross-sectional study was conducted in cancer patients newly admitted to the palliative care department. Descriptive statistics and multivariable linear regression models were performed to estimate the prevalence and factors associated with patient’s health problems. The main outcome is the severity of health problems, as measured with the African Palliative Care Outcomes Scale (APCA African POS). This validated questionnaire measures health outcomes of people severely affected by diseases such as cancer. A total of 134 patients enrolled in the study showed a mean total APCA African POS score of 32.2±10.8 of 67. The most common symptoms of severe intensity were pain (57.5%), mobility difficulties (45.5%) and weakness (42%). The study determined that inpatient status, the patient’s health condition, and the extent of caregivers’ involvement in social activities were significantly associated with increased APCA African POS score. Pain, weakness, mobility issues, reduced peace of mind, a sense of life’s value, lack of information, and financial constraints are prominent problems in cancer patients receiving palliative care in Vietnam. The initial assessment of the patient’s symptoms and problems prior to palliative care is essential to inform treatment discussions regarding medical prognosis in order to improve the patient’s quality of life and treatment outcomes.

QOL-28. NEUROCOGNITIVE DEFICITS, EMOTIONAL DISTURBANCES, PSYCHOSOMATIC COMPLAINS – ADDITIONAL SYMPTOMS OR COMORBID DIAGNOSIS IN CNS GERMINOMA? OVERVIEW OF GROUP AND CASE STUDIES

Abstract BACKGROUND CNS Germinoma cases are associated with good medical prognosis, low risk for neurological deficits and high cognitive functioning. Deeper psychological and neuropsychological examination and longitudinal observation shows also neurocognitive problems and emotional disturbances. The purpose of the study was to assess patients with CNS Germinoma (during and after anticancer treatment), expressed specific psychological and physical symptoms and to find the reason for this. METHODS Since 1998 all patients treated at our center are under psychological control and care. From these group 78 patients with CNS Germinoma with additional significant symptoms, seriously affected their quality of life and everyday activity, were selected for psychological and neuropsychological assessment. Age at psychological intervention ranged from 6 5/12 to 34 8/12 yrs. The patients have no pre-diagnosis history of neurological, developmental or psychiatric disorder. They were examined using a battery of standardized psychological and neuropsychological methods. RESULTS In our sample FIQ (full-scale intelligence quotient) score ranged from 59 to 139. The most patients presented average level of intelligence. However, cognitive functioning was often reduced due to emotional disturbances and psychosomatic complains. 2 patients showed memory disorders, one of them TGA (transient global amnesia) at the time of diagnosis. All patients struggled with emotional disturbances at different grades, especially internalizing type of problems (immaturity, depression, anxiety, instability, inhibition, negative attitude) or were considered to be at risk for psychological adjustment difficulties and tendency to somatization. Analyzed group of patients showed also social competences deficits. They may exhibit deficit in age-appropriate social competence and peers relations. CONCLUSIONS Although CNS Germinoma patients shows similar behavioral and emotional phenotype, there are significant differences between them in terms of cognitive functions. The range of results is wide, from high levels to neurocognitive disorders. Therefore, risk factors, diagnostic criteria and effective neuropsychological intervention, will also be presented and discussed.

Accuracy of medical oncology prognosis for patients with metastatic cancer evaluated for enrollment onto an ongoing randomized clinical trial.

Accuracy of medical oncology prognosis for patients with metastatic cancer evaluated for enrollment onto an ongoing randomized clinical trial.

A bibliometric analysis of the advance of artificial intelligence in medicine

This bibliometric study analyzes the evolution of research in artificial intelligence (AI) applied to medicine from 2015 to September 2023. Using the Scopus database and keywords related to AI, machine learning, and deep learning in medicine, tools such as VOSviewer and Bibliometrix were used to explore publication trends, subject areas, co-authorship networks, and the most productive countries, among others. 2,064 articles were analyzed, and a significant increase in global academic production has been evident in the last five years. International collaboration was notable, with China and the United States leading in knowledge contribution. The keyword analysis highlights the breadth of topics and applications of AI in medicine, with particular emphasis on cancer detection, dengue diagnosis, and medical image analysis, among others. In conclusion, this study highlights the growing academic interest in the application of AI in medicine and the need for collaborative research. The findings underscore the relevance of these technologies in key areas of health care, contributing significantly to advances in medical diagnosis and prognosis.

Анаеробні можливості дівчат постпубертатного періоду онтогенезу мешканок гірських районів Закарпаття

Anaerobic productivity is characterized by the body’s ability to perform muscle work due to anaerobic processes of energy supply and is characterized by their power and capacity. Data on the age-related dynamics of the body’s anaerobic productivity are contradictory. Studying the anaerobic capabilities of girls in the mountainous regions of Zakarpattia Oblast, we understand that the somatotype and functional capabilities of the body are formed in this category of persons in conditions of relative hypoxia. Szymczak R.K. et al researching the effect on the physiology of the body of a long stay at a very high altitude (over 3500 m), male climbers note that in conditions of hypoxia, the anaerobic productivity of the body decreases, the maximum respiratory volume increases, and the relative content of the fat component of the body weight decreases. Therefore, the study of indicators of anaerobic productivity of the body’s energy supply in healthy girls may allow developing individual and population medical prognosis in the future. The level of physical health was assessed by indicators of anaerobic productivity of the body, assessing the ability of women living in mountainous regions to adapt to performing physical work in an anaerobic mode. For this purpose, we used the Wingat anaerobic test method described by Yu.M. Furman et al, the method of Shogy A., Cherebetin G. and somatotype was determined according to Heath-Carter. Having studied the anaerobic capabilities of 102 girls aged 16 to 20, residents of the mountainous regions of the Transcarpathian region. we noted that the performance of physical work in the anaerobic regime of the body’s energy supply depends on the somatotype. The power of anaerobic alactate and lactate processes turned out to be the highest in girls with an endomesomorphic somatotype. The power of anaerobic alactate and lactate processes turned out to be the lowest in girls with an ectomorphic somatotype. Studying the level of anaerobic productivity of females based on the relative indicator of the capacity of anaerobic lactate processes of energy supply showed that girls with a balanced somatotype have the lowest level of anaerobic capacity compared to girls of other somatotypes.

Convolutional neural network with parallel convolution scale attention module and ResCBAM for breast histology image classification

Breast cancer is the most common cause of female morbidity and death worldwide. Compared with other cancers, early detection of breast cancer is more helpful to improve the prognosis of patients. In order to achieve early diagnosis and treatment, clinical treatment requires rapid and accurate diagnosis. Therefore, the development of an automatic detection system for breast cancer suitable for patient imaging is of great significance for assisting clinical treatment. Accurate classification of pathological images plays a key role in computer-aided medical diagnosis and prognosis. However, in the automatic recognition and classification methods of breast cancer pathological images, the scale information, the loss of image information caused by insufficient feature fusion, and the enormous structure of the model may lead to inaccurate or inefficient classification. To minimize the impact, we proposed a lightweight PCSAM-ResCBAM model based on two-stage convolutional neural network. The model included a Parallel Convolution Scale Attention Module network (PCSAM-Net) and a Residual Convolutional Block Attention Module network (ResCBAM-Net). The first-level convolutional network was built through a 4-layer PCSAM module to achieve prediction and classification of patches extracted from images. To optimize the network's ability to represent global features of images, we proposed a tiled feature fusion method to fuse patch features from the same image, and proposed a residual convolutional attention module. Based on the above, the second-level convolutional network was constructed to achieve predictive classification of images. We evaluated the performance of our proposed model on the ICIAR2018 dataset and the BreakHis dataset, respectively. Furthermore, through model ablation studies, we found that scale attention and dilated convolution play an important role in improving model performance. Our proposed model outperforms the existing state-of-the-art models on 200 × and 400 × magnification datasets with a maximum accuracy of 98.74 %.

Clinical Characteristics of Patients with Cryptococcal Meningitis in Hokkaido: A Case Series.

We retrospectively reviewed the medical histories, examination results, treatments, and prognoses of nine patients with cryptococcal meningitis who were diagnosed and treated at Hokkaido University Hospital and its affiliated hospitals over the past 10 years. Cryptococcal meningitis can develop even in immunocompetent hosts, and its prognosis is poor owing to diagnostic difficulties and delayed treatment. Although liposomal amphotericin B and oral 5-fluorocytosine are standard therapies, voriconazole or intraventricular administration of amphotericin B may also be considered treatment options for refractory patients. Some patients develop delayed exacerbations owing to immunological mechanisms that require steroid therapy.

Brain death diagnostics: use of ancillary tests

AbstractWith the determination of the definitive, irreversible loss of the overall function of the cerebrum, cerebellum and brain stem (irreversible loss of brain function, IHA, "brain death"), the death of the human being is determined from a scientific-medical point of view. The determination of IHA, with cardiovascular function being maintained by intensive care, requires special diagnostics ("brain death diagnostics"). This is carried out in the intensive care unit either to assess the medical prognosis, or as a prerequisite for post-mortem organ donation. In Germany, in accordance with the Transplantation Act, the procedural rules for determining IHA are laid down in corresponding guidelines of the German Medical Association (RL-BÄK), the 5th update of which has been in force since September 2022. Here, the indications, the requirements and the technical procedures of ancillary tests (electroencephalography, early auditory and somatosensory evoked potentials, Doppler and duplex sonography, computed tomography angiography, radionuclide perfusion scintigraphy, digital subtraction angiography) according to the German guidelines are outlined.