Building a Time-Series Model to Predict Hospitalization Risks in Home Health Care: Insights Into Development, Accuracy, and Fairness.

Objectives: Home health care (HHC) serves more than 5 million older adults annually in the United States, aiming to prevent unnecessary hospitalizations and emergency department (ED) visits. Despite efforts, up to 25% of patients in HHC experience these adverse events. The underutilization of clinical notes, aggregated data approaches, and potential demographic biases have limited previous HHC risk prediction models.

Decoding disparities: evaluating automatic speech recognition system performance in transcribing Black and White patient verbal communication with nurses in home healthcare.

Objectives: As artificial intelligence evolves, integrating speech processing into home healthcare (HHC) workflows is increasingly feasible. Audio-recorded communications enhance risk identification models, with automatic speech recognition (ASR) systems as a key component. This study evaluates the transcription accuracy and equity of 4 ASR systems-Amazon Web Services (AWS) General, AWS Medical, Whisper, and Wave2Vec-in transcribing patient-nurse communication in US HHC, focusing on their ability in accurate transcription of speech from Black and White English-speaking patients.

Machine learning-based infection diagnostic and prognostic models in post-acute care settings: a systematic review.

Objectives: This study aims to (1) review machine learning (ML)-based models for early infection diagnostic and prognosis prediction in post-acute care (PAC) settings, (2) identify key risk predictors influencing infection-related outcomes, and (3) examine the quality and limitations of these models.

Materials And Methods: PubMed, Web of Science, Scopus, IEEE Xplore, CINAHL, and ACM digital library were searched in February 2024. Eligible studies leveraged PAC data to develop and evaluate ML models for infection-related risks.

Developing a clinical decision support framework for integrating predictive models into routine nursing practices in home health care for patients with heart failure.

Background: The healthcare industry increasingly values high-quality and personalized care. Patients with heart failure (HF) receiving home health care (HHC) often experience hospitalizations due to worsening symptoms and comorbidities. Therefore, close symptom monitoring and timely intervention based on risk prediction could help HHC clinicians prevent emergency department (ED) visits and hospitalizations.

Empowering Graph Neural Network-Based Computational Drug Repositioning with Large Language Model-Inferred Knowledge Representation.

Computational drug repositioning, through predicting drug-disease associations (DDA), offers significant potential for discovering new drug indications. Current methods incorporate graph neural networks (GNN) on drug-disease heterogeneous networks to predict DDAs, achieving notable performances compared to traditional machine learning and matrix factorization approaches. However, these methods depend heavily on network topology, hampered by incomplete and noisy network data, and overlook the wealth of biomedical knowledge available.

Kidney fibrosis molecular mechanisms Spp1 influences fibroblast activity through transforming growth factor beta smad signaling.

Kidney fibrosis marks a critical phase in chronic kidney disease with its molecular intricacies yet to be fully understood. This study's deep dive into single-cell sequencing data of renal tissue during fibrosis pinpoints the pivotal role of fibroblasts and myofibroblasts in the fibrotic transformation. Through identifying distinct cell populations and conducting transcriptomic analysis, Spp1 emerged as a key gene associated with renal fibrosis.

Exploring home healthcare clinicians' needs for using clinical decision support systems for early risk warning.

Objectives: To explore home healthcare (HHC) clinicians' needs for Clinical Decision Support Systems (CDSS) information delivery for early risk warning within HHC workflows.

Methods: Guided by the CDS "Five-Rights" framework, we conducted semi-structured interviews with multidisciplinary HHC clinicians from April 2023 to August 2023. We used deductive and inductive content analysis to investigate informants' responses regarding CDSS information delivery.

Voices Unheard: Exploring New Data Sources in Nursing Through Speech Processing.

The complex nature of verbal patient-nurse communication holds valuable insights for nursing research, but traditional documentation methods often miss these crucial details. This study explores the emerging role of speech processing technology in nursing research, emphasizing patient-nurse verbal communication. We conducted case studies across various healthcare settings, revealing a substantial gap in electronic health records for capturing vital patient-nurse encounters.

Effectiveness of personalized meal recommendation in improving dietary behaviors of Chinese community-dwelling elders: study protocol for a cluster randomized controlled trial.

Background: Inappropriate eating behaviors, particularly a lack of food diversity and poor diet quality, have a significant impact on the prognosis of certain chronic conditions and exacerbate these conditions in the community-dwelling elderly population. Current dietary interventions for the elderly have not adequately considered the nutritional needs associated with multiple chronic conditions and personal dietary preferences of elderly individuals. A personalized recommendation system has been recognized as a promising approach to address this gap.

Comparison of Mortality Predictive Models of Sepsis Patients Based on Machine Learning.

Objective To compare the performance of five machine learning models and SAPS II score in predicting the 30-day mortality amongst patients with sepsis. Methods The sepsis patient-related data were extracted from the MIMIC-IV database. Clinical features were generated and selected by mutual information and grid search.

Exploring relationship between emotion and probiotics with knowledge graphs.

Purpose: Researchers have identified gut microbiota that interact with brain regions associated with emotion and mood. Literature reviews of those associations rely on rigorous systematic approaches and labor-intensive investments. Here we explore how knowledge graph, a large scale semantic network consisting of entities and concepts as well as the semantic relationships among them, is incorporated into the emotion-probiotic relationship exploration work.

Developing a Siamese Network for UTIs Risk Prediction in Immobile Patients Undergoing Stroke.

Stroke patients tend to suffer from immobility, which increases the possibility of post-stroke complications. Urinary tract infections (UTIs) are one of the complications as an independent predictor of poor prognosis of stroke patients. However, the incidence of new UTIs onsets during hospitalization was rare in most datasets with a prevalence of 4%.

An efficient curriculum learning-based strategy for molecular graph learning.

Computational methods have been widely applied to resolve various core issues in drug discovery, such as molecular property prediction. In recent years, a data-driven computational method-deep learning had achieved a number of impressive successes in various domains. In drug discovery, graph neural networks (GNNs) take molecular graph data as input and learn graph-level representations in non-Euclidean space.

COVID term: a bilingual terminology for COVID-19.

Background: The coronavirus disease (COVID-19), a pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has shown its destructiveness with more than one million confirmed cases and dozens of thousands of death, which is highly contagious and still spreading globally. World-wide studies have been conducted aiming to understand the COVID-19 mechanism, transmission, clinical features, etc. A cross-language terminology of COVID-19 is essential for improving knowledge sharing and scientific discovery dissemination.

A Mobile-Based Intervention for Dietary Behavior and Physical Activity Change in Individuals at High Risk for Type 2 Diabetes Mellitus: Randomized Controlled Trial.

Background: Intensive lifestyle modifications have proved effective in preventing type 2 diabetes mellitus (T2DM), yet the efficiency and effectiveness of these modifications need to be improved. Emerging social media interventions are considered useful in promoting these lifestyles; nevertheless, few studies have investigated the effectiveness of combining them with behavior theory.

Objective: This study aims to examine the effectiveness of a 6-month mobile-based intervention (DHealthBar, a WeChat applet) combined with behavioral theory compared with a printed intervention in improving dietary behaviors, physical activity, and intention to change these behaviors among populations at high risk for T2DM.