Healthcare predictive modeling

COVID-19 vaccine boosters are recommended because the protection provided by previous doses eventually decreases, posing a threat to immunity. Some people, however, remain hesitant or unwilling to get vaccinated. The present study sought to investigate factors associated with the intention to receive the COVID-19 booster vaccine based on (1) the constructs of the Health Belief Model, and (2) trust in healthcare workers and science. A sample of 165 adults with two doses of the COVID-19 vaccine were recruited using convenience sampling...

OBJECTIVES: Carpal tunnel syndrome (CTS) stands as the most prevalent upper extremity entrapment neuropathy, with a multifaceted etiology encompassing various risk factors. This study aimed to investigate whether anthropometric measurements of the hand, grip strength, and pinch strength could serve as predictive indicators for CTS through machine learning techniques. METHODS: Enrollment encompassed patients exhibiting CTS symptoms (n = 56) and asymptomatic healthy controls (n = 56), with confirmation via electrophysiological assessments...

<b><br>Introduction:</b> Skin cancers constitute a group of medical disorders remaining a field of interest for surgeons and dermatologists. Currently, this group is typically divided into malignant melanoma (MM) and keratinocyte cancers (KC).</br> <b><br>Aim:</b> The aim of this study is to analyze the cases of skin cancers treated in the Department of Plastic, Reconstructive, and Aesthetic Surgery in Lodz (Poland) during the COVID-19 pandemic (from 2020 to 2022) and then compare the results with the ones from the pre-pandemic period (from 2017 to 2019)...

BACKGROUND: Breast cancer requires evaluating treatment plans using dosimetric and biological parameters. Considering radiation dose distribution and tissue response, healthcare professionals can optimize treatment plans for better outcomes. OBJECTIVE: This study aimed to evaluate the effects of the different Dose Calculation Algorithms (DCAs) and Biologically Model-Related Parameters (BMRPs) on the prediction of cardiopulmonary complications due to left breast radiotherapy...

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...

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...

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: Breast reconstruction is an integral postoncologic procedure that has been associated with improved mental health and psychological outcomes. The possible interaction between existing psychiatric diagnoses hospital courses and postoperative complications warrants further exploration. METHODS: Bilateral breast reconstruction patients were identified from the 2016 to 2018 Healthcare Cost and Utilization Project-National Inpatient Sample (HCUP - NIS). Number and type of psychiatric diagnoses within the cohort were then evaluated using a host of ICD-10 codes...

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...

BACKGROUND: The aim of this study was to establish and validate a Susceptibility-weighted imaging (SWI)-based predictive model for neonatal intracranial haemorrhage (ICH). METHODS: A total of 1190 neonates suspected of ICH after cranial ultrasound screening in a tertiary hospital were retrospectively enrolled. The neonates were randomly divided into a training cohort and a internal validation cohort by a ratio of 7:3. Univariate analysis was used to analyze the correlation between risk factors and ICH, and the prediction model of neonatal ICH was established by multivariate logistic regression based on minimum Akaike information criterion (AIC)...

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...

Objective : To explore the relationship between international students' social support at intake and international student distress at end of treatment. Participants : Data was collected from participants ( n  = 40,085) from 90 United States universities using the Center for Collegiate Mental Health (CCMH) database. Methods : Participants completed measures of psychological distress and perceived social support. Using multilevel modeling, we predicted participants' distress at end of treatment by international student status, social support, race, and length of therapy...

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...

Accurate prediction of cumulative COVID-19 infected cases is essential for effectively managing the limited healthcare resources in India. Historically, epidemiological models have helped in controlling such epidemics. Models require accurate historical data to predict future outcomes. In our data, there were days exhibiting erratic, apparently anomalous jumps and drops in the number of daily reported COVID-19 infected cases that did not conform with the overall trend. Including those observations in the training data would most likely worsen model predictive accuracy...

Ever since the implementation of microfluidics in the biomedical field, in vitro models have experienced unprecedented progress that has led to a new generation of highly complex miniaturized cell culture platforms, known as Organs-on-a-Chip (OoC). These devices aim to emulate biologically relevant environments, encompassing perfusion and other mechanical and/or biochemical stimuli, to recapitulate key physiological events. While OoCs excel in simulating diverse organ functions, the integration of the immune organs and immune cells, though recent and challenging, is pivotal for more comprehensive representation of human physiology...

AIMS: Prediction and early detection of heart failure (HF) is crucial to mitigate its impact on quality of life, survival, and healthcare expenditure. Here, we explored the predictive value of serum metabolomics (168 metabolites detected by proton nuclear magnetic resonance [1 H-NMR] spectroscopy) for incident HF. METHODS AND RESULTS: Leveraging data of 68 311 individuals and >0.8 million person-years of follow-up from the UK Biobank cohort, we (i) fitted per-metabolite Cox proportional hazards models to assess individual metabolite associations, and (ii) trained and validated elastic net models to predict incident HF using the serum metabolome...

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...

The COVID-19 pandemic has led to a change in healthcare models. The aim of this study was to evaluate patient acceptance of telehealth as an alternative to physical consultations, and to identify factors predicting higher satisfaction. This was an observational, cross-sectional, multi-center, international study. All consecutive patients for whom telehealth was used in consultations between April and July 2020 were considered for inclusion. The validated Telehealth Usability Questionnaire (TUQ) was used as a model to measure patient acceptance...

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...