Explore the vast range of titles available.

Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning algorithms in healthcare applications. After a comprehensive overview and investigation of supervised and unsupervised machine learning algorithms, we also demonstrated time series tasks based on past values (along with reviewing their feasibility for both small and large datasets).

Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

BACKGROUND: In recent years, artificial intelligence (AI) applications have been increasingly used as sources of medical information, alongside their applications in many other fields. This study is the first to evaluate ChatGPT's performance in addressing urological emergencies (UE). METHODS: The study included frequently asked questions (FAQs) by the public regarding UE, as well as UE-related questions formulated based on the European Association of Urology (EAU) guidelines. The FAQs were selected from questions posed by patients to doctors and hospital accounts on social media platforms (Facebook, Instagram, and X) and on websites. All questions were presented to ChatGPT 4 (premium version) in English, and the responses were recorded. Two urologists assessed the quality of the responses using a Global Quality Score (GQS) on a scale of 1 to 5. RESULTS: Of the 73 total FAQs, 53 (72.6%) received a GQS score of 5, while only two (2.7%) received a GQS score of 1. The questions with a GQS score of 1 pertained to priapism and urosepsis. The topic with the highest proportion of responses receiving a GQS score of 5 was urosepsis (82.3%), whereas the lowest scores were observed in questions related to renal trauma (66.7%) and postrenal acute kidney injury (66.7%). A total of 42 questions were formulated based on the EAU guidelines, of which 23 (54.8%) received a GQS score of 5 from the physicians. The mean GQS score for FAQs was 4.38 [+ or -] 1.14, which was significantly higher (p=0.009) than the mean GQS score for EAU guideline-based questions (3.88 [+ or -] 1.47). CONCLUSION: This study demonstrated for the first time that nearly three out of four FAQs were answered accurately and satisfactorily by ChatGPT However, the accuracy and proficiency of ChatGPT's responses significantly decreased when addressing guideline-based questions on UE. Keywords: Artificial intelligence; ChatGPT; urological emergencies. AMAC: Son yillarda, yapay zeka (AI) uygulamalari tipta ve bircok diger alanda bir bilgi kaynagi olarak kullanilmaktadir. Bu calisma, ChatGPT'nin urolojik aciller (UA) konusunda gosterdigi performansi degerlendiren ilk calismadir. GEREC VE YONTEM: Calisma, halk tarafindan urolojik acillerle ilgili sikca sorulan sorulari (SSS) ve Avrupa Uroloji Dernegi (EAU) kilavuzlarini incelenerek olusturulan urolojik acillerle ilgili sorulari icermektedir. SSS, sosyal medya (Facebook, Instagram ve X) veya doktor / hastane web sayfalarinda halk tarafindan sorulan sorular arasindan secilmistir. Tum sorular ingilizce olarak ChatGPT 4 (Premium versiyonu) ile sorulmus ve cevaplar kaydedilmistir. iki urolog, yanitlari global kalite puani (GQS) skalasina gore 1-5 puan arasinda degerlendirmistir. BULGULAR: Toplam 73 yanitin 53'u (%72.6) 5 GQS puanina sahipti ve yalnizca 2 yanit (%2.7) 1 GQS puanina sahipti. 1 GQS puanina sahip yanitlar priapizm ve urosepsis ile ilgiliydi. En yuksek GQS puanina (%82.3) sahip konu urosepsis iken, en dusuk puanlar renal travma (%66.7) ve postrenal akut bobrek 15 hasari konularindaydi (%66.7). EAU kilavuzuna dayali olarak olusturulan soru sayisi 42 idi. Bu sorulara olusturulan yanitlarin 23'u (%54.8) hekimlerden 5 GQS puani aldi. SSS'ye yonelik yanitlar icin GQS ortalama puani 4.38 [+ or -] I.I4 idi ve bu, EAU kilavuzuna dayali sorular icin ortalama GQS puanindan (3.88 [+ or -] 1.47) istatistiksel olarak daha yuksekti (p=0.009). SONUC: Bu calisma, ilk kez ChatGPT'nin SSS'lerin yaklasik dortte ucunu dogru ve tatmin edici bir sekilde yanitladigini gostermistir. Buna karsilik, UA hakkinda kilavuz temelli sorulari yanitlarken ChatGPT'nin dogrulugu ve yetkinligi onemli olcude azalmistir. Anahtar sozcukler: ChatGPT; urolojik acil durumlar; yapay zeka.

With its antioxidant properties, hydrogen gas (H2) has been evaluated in vitro, in animal studies and in human studies for a broad range of therapeutic indications. A simple search of “hydrogen gas” in various medical databases resulted in more than 2000 publications related to hydrogen gas as a potential new drug substance. A parallel search in clinical trial registers also generated many hits, reflecting the diversity in ongoing clinical trials involving hydrogen therapy. This review aims to assess and discuss the current findings about hydrogen therapy in the 81 identified clinical trials and 64 scientific publications on human studies. Positive indications have been found in major disease areas including cardiovascular diseases, cancer, respiratory diseases, central nervous system disorders, infections and many more. The available administration methods, which can pose challenges due to hydrogens’ explosive hazards and low solubility, as well as possible future innovative technologies to mitigate these challenges, have been reviewed. Finally, an elaboration to discuss the findings is included with the aim of addressing the following questions: will hydrogen gas be a new drug substance in future clinical practice? If so, what might be the administration form and the clinical indications?

In Nepal, insufficient healthcare infrastructure and limited funding contribute to unmet public healthcare needs and reduced quality of care. While foreign health researchers have stepped in to support local research initiatives, their involvement has sparked ethical concerns regarding the sharing and ownership of data. This study aims to develop a locally governed framework for ethical healthcare data exchange, establish an evidence base to understand local challenges in data transfer, and to identify potential solutions for data sharing with international research teams. This cross-sectional qualitative study was conducted in Kathmandu, Nepal, using 11 multiple-choice and 12 open-ended questionnaire models. We conducted a pre-structured questionnaire survey to best identify local ethics issues related to international data transfer and proposed solutions for these challenges. The key representatives identified from the non-governmental and not-for-profit research institute (n = 14) and the life sciences society (n = 7) were invited to one-to-one blind interviews, and their recorded transcripts were coded using the QDA Miner Lite software (version 3.0) for analysis. The ratio of female to male participants was 2:3, while the ratio of junior-level staff to senior staff ([greater than or equal to]3 years of experience in the sector) was 1:9. Approximately 42.86% of participants shared both raw and analytical data, while <5% shared no data with collaborators. Concerning knowledge, attitudes, and practices, most (38.46%) preferred open-access storage, while approximately 23.1% had limited knowledge, and 15.38% opted for confidentiality. Additionally, < 10% were in the learning process and sought training in data transfer procedures. Within this group of key representatives, participants faced main challenges in the data transfer process from four key categories: (i) the lack of standardized guidelines from government or institutes for data transfer, (ii) inadequate awareness and training in data sharing, (iii) problems related to data sharing, and (iv) problems related to biological sample transfer. In summary, this study emphasizes the importance of a standardized data-sharing platform, focusing on protecting intellectual property rights and establishing a centralized data repository in Nepal. It also recommends educational reforms, legal measures, well-defined agreements, and dedicated oversight to ensure data integrity and security, while streamlining sample transfer processes to enhance transparency and scientific progress in Nepal's research landscape.

The scarcity of high-quality annotated medical imaging datasets is a major problem that collides with machine learning applications in the field of medical imaging analysis and impedes its advancement. Self-supervised learning is a recent training paradigm that enables learning robust representations without the need for human annotation which can be considered an effective solution for the scarcity of annotated medical data. This article reviews the state-of-the-art research directions in self-supervised learning approaches for image data with a concentration on their applications in the field of medical imaging analysis. The article covers a set of the most recent self-supervised learning methods from the computer vision field as they are applicable to the medical imaging analysis and categorize them as predictive, generative, and contrastive approaches. Moreover, the article covers 40 of the most recent research papers in the field of self-supervised learning in medical imaging analysis aiming at shedding the light on the recent innovation in the field. Finally, the article concludes with possible future research directions in the field.

Within the software engineering context, agile approaches encourage customer collaboration, iterative development, and flexibility. However, the mass exodus of highly skilled professionals, known as the \"brain drain\" or \"Japa Syndrome,\" has emerged as a significant challenge, especially in Nigeria. This phenomenon has particularly impacted agile software development practitioners by undermining project continuity, knowledge transfer, and team dynamics. This study empirically examines the effect of brain drain, \"Japa Syndrome,\" on agile practitioners developing healthcare information systems software in Nigeria. It employed a qualitative, multi-method approach to gather empirical data from 13 agile practitioners in Nigeria's healthcare information systems sector. The study used semi-structured, open-ended interview questions and snowball sampling from our network of professional experts. The collected data were analysed using a grounded theory-based approach, including open coding, constant comparison, memoing, and reaching theoretical saturation. The study identified 24 codes and organised them into five memos, which cover the sudden loss of agile team members, increased technical debt, delays in decision-making, psychological effects on the agile team, and organisational coping mechanisms. The contribution of this research is a detailed analysis of these five memos. We recommend urgent systemic policy reforms and the development of a privacy and secure-by-design culture. These measures are vital to minimise reputational damage and to maintain the viability and competitiveness of Nigeria's healthcare information systems software development.

Background Among patients with acute coronary syndrome and percutaneous coronary intervention, stress hyperglycemia ratio (SHR) is primarily associated with short-term unfavorable outcomes. However, the relationship between SHR and long-term worsen prognosis in acute myocardial infarction (AMI) patients admitted in intensive care unit (ICU) are not fully investigated, especially in those with different ethnicity. This study aimed to clarify the association of SHR with all-cause mortality in critical AMI patients from American and Chinese cohorts. Methods Overall 4,337 AMI patients with their first ICU admission from the American Medical Information Mart for Intensive Care (MIMIC)-IV database (n = 2,166) and Chinese multicenter registry cohort Cardiorenal ImprovemeNt II (CIN-II, n = 2,171) were included in this study. The patients were divided into 4 groups based on quantiles of SHR in both two cohorts. Results The total mortality was 23.8% (maximum follow-up time: 12.1 years) in American MIMIC-IV and 29.1% (maximum follow-up time: 14.1 years) in Chinese CIN-II. In MIMIC-IV cohort, patients with SHR of quartile 4 had higher risk of 1-year (adjusted hazard radio [aHR] = 1.87; 95% CI: 1.40–2.50) and long-term (aHR = 1.63; 95% CI: 1.27–2.09) all-cause mortality than quartile 2 (as reference). Similar results were observed in CIN-II cohort (1-year mortality: aHR = 1.44; 95%CI: 1.03–2.02; long-term mortality: aHR = 1.32; 95%CI: 1.05–1.66). In both two group, restricted cubic splines indicated a J-shaped correlation between SHR and all-cause mortality. In subgroup analysis, SHR was significantly associated with higher 1-year and long-term all-cause mortality among patients without diabetes in both MIMIC-IV and CIN-II cohort. Conclusion Among critical AMI patients, elevated SHR is significantly associated with and 1-year and long-term all-cause mortality, especially in those without diabetes, and the results are consistently in both American and Chinese cohorts.

Studies demonstrate benefits of early palliative care. It is unclear how to best provide scalable, integrated supportive care alongside disease directed treatment in a way that expands primary palliative and effectively leverages specialty palliative care. Family meetings are frequently used to communicate medical information, but patients and families often have difficulty understanding the information provided. At City of Hope National Medical Center, we developed a 10-step model alongside provider specific training to facilitate shared medical decision making that aligns a patient's goals and values with provider medical recommendations. This program allows staff to work at the top of their license, improves efficiency, and leverages relevant palliative disciplines. We developed screening tools, palliative consult triggers, and patient-centric educational material. An electronic family meeting summary form was designed to highlight patient values, facilitate information retention, improve medical decision making, and ease documentation burden. Early iterations of the program in the ICU have demonstrated successful screening of patients and caregivers, increased provider efficiency and satisfaction, and correlated length of stay reductions. We will formally evaluate the model in its entirety in the coming months. Family meetings are important forums to communicate complex medical information and are a ubiquitous focal point to integrate relevant components of palliative medicine allowing for enhanced patient and family-centric care. The Department of Supportive Care Medicine will offer the model, educational sheets, consult triggers, and samples of the electronic family meeting summary form as free downloadable resources.