Machine learning interpretability methods to characterize the importance of hematologic biomarkers in prognosticating patients with suspected infection.

Objective: To evaluate the effectiveness of Monocyte Distribution Width (MDW) in predicting sepsis outcomes in emergency department (ED) patients compared to other hematologic parameters and vital signs, and to determine whether routine parameters could substitute MDW in machine learning models.

Methods: We conducted a retrospective analysis of data from 10,229 ED patients admitted to a large regional safety-net hospital in Cleveland, Ohio who had suspected infections and developed sepsis-associated poor outcomes. We developed a new analytical framework consisting of seven data models and an ensemble of high accuracy machine learning (ML) algorithms (accuracy values ranging from 0.

A 360° View for Large Language Models: Early Detection of Amblyopia in Children using Multi-View Eye Movement Recordings.

Amblyopia is a neurodevelopmental visual disorder that affects approximately 3-5% of children globally and it can lead to vision loss if it is not diagnosed and treated early. Traditional diagnostic methods, which rely on subjective assessments and expert interpretation of eye movement recordings presents challenges in resource-limited eye care centers. This study introduces a new approach that integrates the Gemini large language model (LLM) with eye-tracking data to develop a classification tool for diagnosis of patients with amblyopia.

MaTiLDA: An Integrated Machine Learning and Topological Data Analysis Platform for Brain Network Dynamics.

Topological data analysis (TDA) combined with machine learning (ML) algorithms is a powerful approach for investigating complex brain interaction patterns in neurological disorders such as epilepsy. However, the use of ML algorithms and TDA for analysis of aberrant brain interactions requires substantial domain knowledge in computing as well as pure mathematics. To lower the threshold for clinical and computational neuroscience researchers to effectively use ML algorithms together with TDA to study neurological disorders, we introduce an integrated web platform called MaTiLDA.

Machine Learning Interpretability Methods to Characterize Brain Network Dynamics in Epilepsy.

The rapid adoption of machine learning (ML) algorithms in a wide range of biomedical applications has highlighted issues of trust and the lack of understanding regarding the results generated by ML algorithms. Recent studies have focused on developing interpretable ML models and establish guidelines for transparency and ethical use, ensuring the responsible integration of machine learning in healthcare. In this study, we demonstrate the effectiveness of ML interpretability methods to provide important insights into the dynamics of brain network interactions in epilepsy, a serious neurological disorder affecting more than 60 million persons worldwide.

Machine Learning Interpretability Methods to Characterize the Importance of Hematologic Biomarkers in Prognosticating Patients with Suspected Infection.

Early detection of sepsis in patients admitted to the emergency department (ED) is an important clinical objective as early identification and treatment can help reduce morbidity and mortality rate of 20% or higher. Hematologic changes during sepsis-associated organ dysfunction are well established and a new biomarker called Monocyte Distribution Width (MDW) has been recently approved by the US Food and Drug Administration for sepsis. However, MDW, which quantifies monocyte activation in sepsis patients, is not a routinely reported parameter and it requires specialized proprietary laboratory equipment.

Utilization of adolescent friendly health services and its associated factors among higher secondary students in mid-western Himalayan mountainous district of Nepal.

Adolescent friendly health services (AFHS) are designed to make health services accommodate the unique needs of adolescents. AFHS are characterized by three basic characteristics (programmatic, health facilities and health service providers) that should be applied. However, limited is known about the use of AFHS in the context of Nepal.

Prevalence of low birth weight and its associated factors: Hospital based cross sectional study in Nepal.

Low birth weight is still an important public health problem worldwide. It is a major contributor to neonatal death in developing countries, including Nepal. The government of Nepal has developed and implemented different programs to improve maternal and neonatal health, including baby's birth weight.

Ontology-based feature engineering in machine learning workflows for heterogeneous epilepsy patient records.

Biomedical ontologies are widely used to harmonize heterogeneous data and integrate large volumes of clinical data from multiple sources. This study analyzed the utility of ontologies beyond their traditional roles, that is, in addressing a challenging and currently underserved field of feature engineering in machine learning workflows. Machine learning workflows are being increasingly used to analyze medical records with heterogeneous phenotypic, genotypic, and related medical terms to improve patient care.

Patient satisfaction and its socio-demographic correlates in a tertiary public hospital in Nepal: a cross-sectional study.

Background: Patient satisfaction is one proxy indicator of the health care quality; however, enhancing patient satisfaction in low-income settings is very challenging due to the inadequacy of resources as well as low health literacy among patients. In this study, we assess patient satisfaction and its correlates in a tertiary public hospital in Nepal.

Methods: We conducted a cross sectional study at outpatient department of Bhaktapur Hospital of Nepal.

Frontline Healthcare Workers' Knowledge and Perception of COVID-19, and Willingness to Work during the Pandemic in Nepal.

This study investigated the contextual factors associated with the knowledge, perceptions, and the willingness of frontline healthcare workers (FHWs) to work during the COVID-19 pandemic in Nepal among a total of 1051 FHWs. Multivariable logistic regression analysis was applied to identify independent associations between predictors and outcome variables. Of the total study subjects, 17.

Sociodemographic correlates of antenatal care visits in Nepal: results from Nepal Demographic and Health Survey 2016.

Background: Good quality antenatal care visits are crucial to reduce maternal mortality and improve overall maternal and neonatal health outcomes. A previous study on antenatal care visits analyzed the nationally representative data of 2011; however, no studies have been conducted recently in Nepal. Therefore, we analyzed the sociodemographic correlates of the frequency and quality of antenatal care among Nepalese women from the nationally representative data of 2016.