FedDSS: A data-similarity approach for client selection in horizontal federated learning.

Background And Objective: Federated learning (FL) is an emerging distributed learning framework allowing multiple clients (hospitals, institutions, smart devices, etc.) to collaboratively train a centralized machine learning model without disclosing personal data. It has the potential to address several healthcare challenges, including a lack of training data, data privacy, and security concerns.

Med-MGF: multi-level graph-based framework for handling medical data imbalance and representation.

Background: Modeling patient data, particularly electronic health records (EHR), is one of the major focuses of machine learning studies in healthcare, as these records provide clinicians with valuable information that can potentially assist them in disease diagnosis and decision-making.

Methods: In this study, we present a multi-level graph-based framework called MedMGF, which models both patient medical profiles extracted from EHR data and their relationship network of health profiles in a single architecture. The medical profiles consist of several layers of data embedding derived from interval records obtained during hospitalization, and the patient-patient network is created by measuring the similarities between these profiles.

The use of probabilistic graphical models in pediatric sepsis: a feasibility and scoping review.

Background: Recent research has demonstrated that machine learning (ML) has the potential to improve several aspects of medical application for critical illness, including sepsis. This scoping review aims to evaluate the feasibility of probabilistic graphical model (PGM) methods in pediatric sepsis application and describe the use of pediatric sepsis definition in these studies.

Methods: Literature searches were conducted in PubMed, Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL+), and Web of Sciences from 2000-2023.

Effective diagnosis of sepsis in critically ill children using probabilistic graphical model.

Background: Probabilistic graphical model, a rich graphical framework in modelling associations between variables in complex domains, can be utilized to aid clinical diagnosis. However, its application in pediatric sepsis remains limited. This study aims to explore the utility of probabilistic graphical models in pediatric sepsis in the pediatric intensive care unit.

Integrated Analysis of Healthcare Efficiency: A Systematic Review.

Data Envelopment Analysis (DEA) has been used as a performance measurement tool in efficiency assessment of healthcare systems. However, over the years, researchers and health practitioners presented the theoretical and methodological limitations of DEA that limits the full view of healthcare efficiency. To address these limitations, a commonly used strategy is to integrate other statistical methods and techniques with DEA to provide better efficiency evaluation.

Global energy minimization of alanine dipeptide via barrier function methods.

This paper presents an interior point method to determine the minimum energy conformation of alanine dipeptide. The CHARMM energy function is minimized over the internal coordinates of the atoms involved. A barrier function algorithm to determine the minimum energy conformation of peptides is proposed.

PGMC: a framework for probabilistic graphic model combination.

Decision making in biomedicine often involves incorporating new evidences into existing or working models reflecting the decision problems at hand. We propose a new framework that facilitates effective and incremental integration of multiple probabilistic graphical models. The proposed framework aims to minimize time and effort required to customize and extend the original models through preserving the conditional independence relationships inherent in two types of probabilistic graphical models: Bayesian networks and influence diagrams.

Time-critical dynamic decision modeling in medicine.

Many real-world medical applications require timely actions to be taken in time pressured situations. Existing approaches to dynamic decision modeling have provided relatively efficient methods for representing and reasoning, but the process of computing the optimal solution has remained intractable. A major reason for this difficulty is the lack of models that are capable of modeling temporal processes and dealing with time-critical situations.