learning healthcare

Introduction In recent years, artificial intelligence (AI) in medical imaging has undergone unprecedented innovation and advancement, sparking a revolutionary transformation in healthcare. The field of radiology is particularly implicated, as clinical radiologists are expected to interpret an ever-increasing number of complex cases in record time. Machine learning software purchased by our institution is expected to help our radiologists come to a more prompt diagnosis by delivering point-of-care quantitative analysis of suspicious findings and streamlining clinical workflow...

Integrating healthcare into traffic accident prevention through predictive modeling holds immense potential. Decentralized Defense presents a transformative vision for combating cyberbullying, prioritizing user privacy, fostering a safer online environment, and offering valuable insights for both healthcare and predictive modeling applications. As cyberbullying proliferates in social media, a pressing need exists for a robust and innovative solution that ensures user safety in the cyberspace. This paper aims toward introducing the approach of merging Blockchain and Federated Learning (FL), to create a decentralized AI solutions for cyberbullying...

Elderly falls are a major concerning threat resulting in over 1.5-2 million elderly people experiencing severe injuries and 1 million deaths yearly. Falls experienced by Elderly people may lead to a long-term negative impact on their physical and psychological health conditions. Major healthcare research had focused on this lately to detect and prevent the fall. In this work, an Artificial Intelligence (AI) edge computing based wearable device is designed and developed for detection and prevention of fall of elderly people...

PURPOSE: Our objectives were to (1) employ ensemble machine learning algorithms utilizing real-world clinical data to predict 90-day prognosis, including dialysis dependence and mortality, following the first hospitalized dialysis and (2) identify the significant factors associated with overall outcomes. PATIENTS AND METHODS: We identified hospitalized patients with Acute kidney injury requiring dialysis (AKI-D) from a dataset of the Taipei Medical University Clinical Research Database (TMUCRD) from January 2008 to December 2020...

BACKGROUND AND OBJECTIVES: Stem cell banking (SCB) is a promising area of modern medicine with the potential to yield innovative treatments and cures. To effectively educate parents and implement laws and regulations that address parental concerns and encourage informed decision-making, it is imperative to emphasize parental viewpoints and their consequences for future healthcare. The study aims to establish the Saudi Arabian population's level of understanding regarding SCB and to comprehend the elements influencing parental knowledge, attitudes, and SCB decision-making processes...

BACKGROUND: The use of virtual learning platforms is on the rise internationally, however, successful integration into existing curricula is a complex undertaking fraught with unintended consequences. Looking beyond medical and pedagogic literature can provide insight into factors affecting the user experience. The technology acceptance model, widely used in software evaluation, can be used to identify barriers and enablers of engagement with virtual learning platforms. Here, the technology acceptance model was used to scaffold the exploration of the factors that influenced students' perceptions of the virtual anatomy platform, Anatomage and how these shaped their intention to use it...

Accurately predicting patients' risk for specific medical outcomes is paramount for effective healthcare management and personalized medicine. While a substantial body of literature addresses the prediction of diverse medical conditions, existing models predominantly focus on singular outcomes, limiting their scope to one disease at a time. However, clinical reality often entails patients concurrently facing multiple health risks across various medical domains. In response to this gap, our study proposes a novel multi-risk framework adept at simultaneous risk prediction for multiple clinical outcomes, including diabetes, mortality, and hypertension...

BACKGROUND: For families with children diagnosed with complex illnesses, the COVID-19 pandemic added many challenges. In order to mitigate inevitable disruptions in pediatric care settings, caregivers may need added supports and resources. The Keeping Hope Possible (KHP) Toolkit is a self-administered intervention intended to enhance caregiving experiences of parents with a child with multiple needs. However, little is known about effectively disseminating the Toolkit. PURPOSE AND METHODS: A qualitative, thematic analysis was conducted to explore the opinions and perceptions of pediatric nurses and allied healthcare providers (HCPs) in relation to the dissemination and use of the KHP Toolkit for use by families with complex medical needs...

RATIONALE: Although numerous candidate features exist for predicting risk of higher risk of healthcare utilization in patients with chronic obstructive pulmonary disease (COPD), the process for selecting the most discriminative features remains unclear. OBJECTIVE: The objective of this study was to develop a robust feature selection method to identify the most discriminative candidate features for predicting healthcare utilization in COPD, and compare the model performance with other common feature selection methods...

Evaluating the rehabilitation status of individuals with serious mental illnesses (SMI) necessitates a comprehensive analysis of multimodal data, including unstructured text records and structured diagnostic data. However, progress in the effective assessment of rehabilitation status remains limited. Our study develops a deep learning model integrating Bidirectional Encoder Representations from Transformers (BERT) and TabNet through a late fusion strategy to enhance rehabilitation prediction, including referral risk, dangerous behaviors, self-awareness, and medication adherence, in patients with SMI...

BACKGROUND: Metabolomics, the study of substrates and products of cellular metabolism, offers valuable insights into an organism's state under specific conditions and has the potential to revolutionise preventive healthcare and pharmaceutical research. However, analysing large metabolomics datasets remains challenging, with available methods relying on limited and incompletely annotated metabolic pathways. METHODS: This study, inspired by well-established methods in drug discovery, employs machine learning on metabolite fingerprints to explore the relationship of their structure with responses in experimental conditions beyond known pathways, shedding light on metabolic processes...

MXene-inspired two-dimensional (2D) materials like Ti3 C2 T x are widely known for their versatile properties, including surface plasmon, higher electrical conductivity, exceptional in-plane tensile strength, EMI shielding, and IR thermal properties. The MXene nanosheets coupled poly(vinylidene fluoride) (PVDF) nanofibers with d 33 ∼-26 pm V-1 are able to capture the smaller thermal fluctuation due to a superior pyroelectric coefficient of ∼130 nC m-2 K-1 with an improved (∼7 times with respect to neat PVDF nanofibers) pyroelectric current figure of merit (FOMi )...

BACKGROUND: Wearable sensors are increasingly being explored in healthcare, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consistency of data collected from wearable sensors. In particular, preprocessing pipelines to clean and standardize raw data have not been fully optimized. OBJECTIVE: The aim of this study was to conduct a systematic review of preprocessing techniques employed on wearable sensor data to ensure their readiness for artificial intelligence/machine learning ("AI/ML-ready") applications...

BACKGROUND: Following esophagogastric cancer treatment, patients with esophageal cancer and their relatives struggle with adjusting to a new everyday life as they experience various challenges after treatment requiring rehabilitation. Health professionals must address long-term impacts on patients' health, everyday life, family functioning, and support needs. OBJECTIVE: This qualitative descriptive study aimed to explore patients', relatives', and health professionals' experience with long-term impacts and rehabilitation after treatment for esophageal cancer...

PURPOSE: This study aims to understand the impact of the COVID-19 pandemic on social determinants of health (SDOH) of marginalized racial/ethnic US population groups, specifically African Americans and Asians, by leveraging natural language processing (NLP) and machine learning (ML) techniques on race-related spatiotemporal social media text data. Specifically, this study establishes the extent to which Latent Dirichlet Allocation (LDA) and Gibbs Sampling Dirichlet Multinomial Mixture (GSDMM)-based topic modeling determines social determinants of health (SDOH) categories, and how adequately custom named-entity recognition (NER) detects key SDOH factors from a race/ethnicity-related Reddit data corpus...

Gynecological health remains a critical aspect of women's overall well-being, with profound implications for maternal and reproductive outcomes. This comprehensive review synthesizes the current state of knowledge on four pivotal aspects of gynecological health: preterm birth, breast cancer and cervical cancer and infertility treatment. Machine learning (ML) has emerged as a transformative technology with the potential to revolutionize gynecology and women's healthcare. The subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions...

Mobile apps for healthcare (mHealth apps for short) have been increasingly adapted to help users manage their health or to get healthcare services. User feedback analysis is a pertinent method that can be used to improve the quality of mHealth apps. The objective of this paper is to use supervised machine learning algorithms to evaluate the quality of mHealth apps according to the ISO/IEC 25010 quality model based on user feedback. For this purpose, a total of 1682 user reviews have been collected from 86 mHealth apps provided by Google Play Store...

The RHMCD-20 dataset offers a thorough investigation of the dynamics of mental health in Bangladesh while under quarantine. The structured survey that was distributed to different demographic groups yielded a dataset that included a wide range of variables, such as age, gender, occupation, and stress levels. Predictive modelling, understanding the effects of quarantine on the workplace and society, and intergenerational insights are all greatly enhanced by this dataset. The dataset allows intelligent algorithms to be developed by bridging the gap between machine learning and healthcare...

AIM: To offer a practical way in which the status of healthcare assistants (HCAs) can be increased by drawing on their experience, knowledge and skillset, whilst mentoring medical students during an HCA project. DESIGN: Qualitative, reflexive thematic analysis. METHODS: One-to-one semi-structured interviews were conducted between April and June 2019, with 13 participants. Participants included five healthcare assistants; three practice development nurses, two of whom were former HCAs; one registered general nurse and four clinical educators...

This research work explores the integration of medical and information technology, particularly focusing on the use of data analytics and deep learning techniques in medical image processing. Specifically, it addresses the diagnosis and prediction of fetal conditions, including Down Syndrome (DS), through the analysis of ultrasound images. Despite existing methods in image segmentation, feature extraction, and classification, there is a pressing need to enhance diagnostic accuracy. Our research delves into a comprehensive literature review and presents advanced methodologies, incorporating sophisticated deep learning architectures and data augmentation techniques to improve fetal diagnosis...