Early classification of time series has been receiving a lot of attention recently. In this paper we present a model, which we call the Early Classification Model (ECM), that allows for early, accurate and patient-specific classification of multivariate observations. ECM is comprised of an integration of the widely used Hidden Markov Model (HMM) and Support Vector Machine (SVM) models. It attained very promising results on the datasets we tested it on: in one set of experiments based on a published dataset of response to drug therapy in Multiple Sclerosis patients, ECM used only an average of 40% of a time series and was able to outperform some of the baseline models, which needed the full time series for classification. In the set of experiments tested on a sepsis therapy dataset, ECM was able to surpass the standard threshold-based method and the state-of-the-art method for early classification of multivariate time series.

Download full-text PDF

Source
http://dx.doi.org/10.1504/ijdmb.2015.067955DOI Listing

Publication Analysis

Top Keywords

early classification
16
time series
16
classification multivariate
12
multivariate observations
8
set experiments
8
classification
6
patient-specific early
4
early
4
observations early
4
time
4

Similar Publications

Introduction: This study aimed to compare the time spent on episodes seen by primary care emergency departments before (2017) and after (2019) the inclusion of an advanced practice nurse in patient classification.

Methods: Records from 3 primary care emergency departments in 2017 (n = 18,663) and 2019 (n = 22,632) were compared using Student t and chi-square tests. Waiting time for classification, classification time, and total time spent in the consultation area were compared for total episodes, levels of priority, reasons for consultation, and previous clinical processes.

View Article and Find Full Text PDF

Objective: Endovascular mechanical thrombectomy (EVMT) is widely employed in patients with acute intracranial carotid artery occlusion (AIICAO). This study aimed to predict the outcomes of EVMT following AIICAO by utilizing anatomic classification of the circle of Willis and its relative position to the thrombus.

Methods: In this study, we retrospectively analyzed a cohort of 108 patients with AIICAO who underwent endovascular mechanical thrombectomy (EVMT) at Shaoxing People's Hospital.

View Article and Find Full Text PDF

Background: Within the context of increasing transparency around public contributions, a framework for reporting and analysing public contributions to research and development (R&D) was previously developed and is piloted here using the example of antibiotics. The aim of this work is to check whether the category system is feasible, to revise and adjust the granularity of the category system where necessary, and to expand the range of sources for detailed analyses.

Methods: All antimicrobial medicinal products in development, discontinued and approved in the last 10 years were identified in the literature.

View Article and Find Full Text PDF

Morphology, phylogeography, phylogeny, and taxonomy of (Apiaceae).

Front Plant Sci

January 2025

Key Laboratory of Bio-Resources and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China.

Background: The genus is endemic to China and belongs to the Apiaceae family, which is widely distributed in the Himalaya-Hengduan Mountains (HHM) region. However, its morphology, phylogeny, phylogeography, taxonomy, and evolutionary history were not investigated due to insufficient sampling and lack of population sampling and plastome data. Additionally, we found that was not similar to members but resembled species in morphology, indicating that the taxonomic position of needs to be re-evaluated.

View Article and Find Full Text PDF

Purpose: Functional near-infrared spectroscopy (fNIRS) has shown feasibility in evaluating cognitive function and brain functional connectivity (FC). Therefore, this fNIRS study aimed to develop a screening method for subjective cognitive decline (SCD) and mild cognitive impairment (MCI) based on resting-state prefrontal FC and neuropsychological tests via machine learning.

Methods: Functional connectivity data measured by fNIRS were collected from 55 normal controls (NCs), 80 SCD individuals, and 111 MCI individuals.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!