Surveying neuro-symbolic approaches for reliable artificial intelligence of things
Abstract The integration of Artificial Intelligence (AI) with the Internet of Things (IoT), known
as the Artificial Intelligence of Things (AIoT), enhances the devices' processing and analysis …
as the Artificial Intelligence of Things (AIoT), enhances the devices' processing and analysis …
Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review
Aim In this review, we investigated how Machine Learning (ML) was utilized to predict all-
cause somatic hospital admissions and readmissions in adults. Methods We searched eight …
cause somatic hospital admissions and readmissions in adults. Methods We searched eight …
[HTML][HTML] Enhancing Health Equity by Predicting Missed Appointments in Health Care: Machine Learning Study
Background The phenomenon of patients missing booked appointments without canceling
them—known as Did Not Show (DNS), Did Not Attend (DNA), or Failed To Attend (FTA) …
them—known as Did Not Show (DNS), Did Not Attend (DNA), or Failed To Attend (FTA) …
Improved Healthcare Access in Low-resource Regions: A Review of Technological Solutions
B Lamichhane, N Neupane - arxiv preprint arxiv:2205.10913, 2022 - arxiv.org
Technological advancements have led to significant improvements in healthcare for
prevention, diagnosis, treatments, and care. While resourceful regions can capitalize on …
prevention, diagnosis, treatments, and care. While resourceful regions can capitalize on …
Machine Learning-Based Prediction of ICU Readmissions in Intracerebral Hemorrhage Patients: Insights from the MIMIC Databases
Background: Intracerebral hemorrhage (ICH) is a life-risking condition characterized by
bleeding within the brain parenchyma. ICU readmission in ICH patients is a critical outcome …
bleeding within the brain parenchyma. ICU readmission in ICH patients is a critical outcome …
Analysis and Mortality Prediction using Multiclass Classification for Older Adults with Type 2 Diabetes
R Desure, GJ Krishna - arxiv preprint arxiv:2402.10999, 2024 - arxiv.org
Designing proper treatment plans to manage diabetes requires health practitioners to pay
heed to the individuals remaining life along with the comorbidities affecting them. Older …
heed to the individuals remaining life along with the comorbidities affecting them. Older …
AI Solutions for Inter-organisational Care: A Case Based Analysis
Health care is a complex domain containing large amounts of data, including clinical and
administrative data. Furthermore, the domain includes advanced decision-making utilising …
administrative data. Furthermore, the domain includes advanced decision-making utilising …
MAIPFE: An Efficient Multimodal Approach Integrating Pre-Emptive Analysis, Personalized Feature Selection, and Explainable AI.
MD Sirapangi, S Gopikrishnan - Computers, Materials & …, 2024 - search.ebscohost.com
Abstract Medical Internet of Things (IoT) devices are becoming more and more common in
healthcare. This has created a huge need for advanced predictive health modeling …
healthcare. This has created a huge need for advanced predictive health modeling …
Interpretable and robust hospital readmission predictions from Electronic Health Records
Rates of Hospital Readmission (HR), defined as unplanned readmission within 30 days of
discharge, have been increasing over the years, and impose an economic burden on …
discharge, have been increasing over the years, and impose an economic burden on …
Risk Prediction of Opioid Dependency Using Machine Learning Based on Electronic Health Record
S Madanian, S Dai, P Natarajan… - 2023 IEEE Asia …, 2023 - ieeexplore.ieee.org
Opioid abuse and dependence have emerged as a pressing global concern, posing
significant challenges to public health and society. Early identification and prediction of …
significant challenges to public health and society. Early identification and prediction of …