Knowledge-based Classification System Research Articles

MBrain: towards the continuous follow-up and headache classification of primary headache disorder patients

BackgroundThe diagnosis of headache disorders relies on the correct classification of individual headache attacks. Currently, this is mainly done by clinicians in a clinical setting, which is dependent on subjective self-reported input from patients. Existing classification apps also rely on self-reported information and lack validation. Therefore, the exploratory mBrain study investigates moving to continuous, semi-autonomous and objective follow-up and classification based on both self-reported and objective physiological and contextual data.MethodsThe data collection set-up of the observational, longitudinal mBrain study involved physiological data from the Empatica E4 wearable, data-driven machine learning (ML) algorithms detecting activity, stress and sleep events from the wearables’ data modalities, and a custom-made application to interact with these events and keep a diary of contextual and headache-specific data. A knowledge-based classification system for individual headache attacks was designed, focusing on migraine, cluster headache (CH) and tension-type headache (TTH) attacks, by using the classification criteria of ICHD-3. To show how headache and physiological data can be linked, a basic knowledge-based system for headache trigger detection is presented.ResultsIn two waves, 14 migraine and 4 CH patients participated (mean duration 22.3 days). 133 headache attacks were registered (98 by migraine, 35 by CH patients). Strictly applying ICHD-3 criteria leads to 8/98 migraine without aura and 0/35 CH classifications. Adapted versions yield 28/98 migraine without aura and 17/35 CH classifications, with 12/18 participants having mostly diagnosis classifications when episodic TTH classifications (57/98 and 32/35) are ignored.ConclusionsStrictly applying the ICHD-3 criteria on individual attacks does not yield good classification results. Adapted versions yield better results, with the mostly classified phenotype (migraine without aura vs. CH) matching the diagnosis for 12/18 patients. The absolute number of migraine without aura and CH classifications is, however, rather low. Example cases can be identified where activity and stress events explain patient-reported headache triggers. Continuous improvement of the data collection protocol, ML algorithms, and headache classification criteria (including the investigation of integrating physiological data), will further improve future headache follow-up, classification and trigger detection.Trial registration This trial was retrospectively registered with number NCT04949204 on 24 June 2021 at www.clinicaltrials.gov.

Improving the accuracy of land cover classification using sentinel 2 data and knowledge based classification system in the west of Amara city, Iraq

In this paper, we focus on the use of remote sensing analysis technique with the Sentinel-2 data in order to discriminate land cover in Al-Maimouna District, West of Amara, Iraq. The main objective of the study is to explore whether the expert classification technique can improve the accuracy of land cover classification. In our experiment, an expert classification technique was applied to the Sentinel-2 data set by using, normalized difference vegetation index, soil-adjusted vegetation index, normalized difference water index, modified normalized difference water index, urban index, normalized difference built-up index, normalized difference soil index and bare soil index. Build-up area was found to be the dominant type of land use classified which covers about 41.9% of the total study area, while the least classified was vegetation which accounts for 15.1%. The results of the study demonstrates that the expert classification technique produced an overall accuracy of 88.3%. The results of the study also indicate that by using an expert classification technique, a significantly higher discrimination accuracy can be achieved. Moreover, the expert classification technique reduces problems associated with high-resolution images; while reliabilities are achieved that are better than those achieved with traditional methods.

How to Illuminate the Druggable Genome Using Pharos.

Pharos is an integrated web-based informatics platform for the analysis of data aggregated by the Illuminating the Druggable Genome (IDG) Knowledge Management Center, an NIH Common Fund initiative. The current version of Pharos (as of October 2019) spans 20,244 proteins in the human proteome, 19,880 disease and phenotype associations, and 226,829 ChEMBL compounds. This resource not only collates and analyzes data from over 60 high-quality resources to generate these types, but also uses text indexing to find less apparent connections between targets, and has recently begun to collaborate with institutions that generate data and resources. Proteins are ranked according to a knowledge-based classification system, which can help researchers to identify less studied "dark" targets that could be potentially further illuminated. This is an important process for both drug discovery and target validation, as more knowledge can accelerate target identification, and previously understudied proteins can serve as novel targets in drug discovery. Two basic protocols illustrate the levels of detail available for targets and several methods of finding targets of interest. An Alternate Protocol illustrates the difference in available knowledge between less and more studied targets. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Search for a target and view details Alternate Protocol: Search for dark target and view details Basic Protocol 2: Filter a target list to get refined results.

Evidence-based personal applications of medical computing models in risk factors of cardiovascular disease for the middle-aged and elderly

Medical applications on cardiovascular disease (CVD) for hybrid computing models are an emerging research area. The CVD, including stroke, hypertension, and high cholesterol, is one of 10 leading causes of death in Taiwan in middle-aged and elderly; in particular, the CVD has become the top killer in advanced countries. Thus, this serious but interesting issue triggers the study to focus on patients of the CVD. The study explores variables, influencing cardiovascular functions for four risk factors of blood pressure, blood glucose, blood fat, and kidney diseases, in the middle-aged and elderly. By the data collection of regular physical examination system from a regional hospital, the original dataset contains 52 variables collected from October 2011 to February 2014. We model a hybrid knowledge-based classification system to organize expert experiences, integrated linear and nonlinear attribute selection methods, data discretization of smart expert method, rough set theory, the LEM2 algorithm, and rule-filtering technique to classify the CVD for the early warning purpose. After data cleaning, 20 attributes with 2027 records are remained. For effectively identifying the variables of CVD subjects, this study reclassifies the above four risk diseases into three classes: no disease, 1&2 diseases, and 3&4 diseases. To verify performance of the proposed procedure, we experience an empirical experiment to compare the full 20 used attributes, the used attributes of integrated linear and nonlinear attribute selections with rule-filtering technique, and various classifiers. Conclusively, the 13 used attributes obtained from optimal accuracy become the key determinants that affect the four risk factors of the CVD. The empirical results and findings benefit doctors’ and medical institutions’ early medical recommendations and treatments with the advantages of significantly reducing morbidity of CVD.

A Knowledge-based Approach to Urban Feature Classification Using Aerial Imagery with Lidar Data

While the spatial resolution of remotely sensed data has improved, multispectral imagery is still not sufficient for urban classification. Problems include the difficulty in discriminating between trees and grass, the misclassification of buildings due to diverse roof compositions and shadow effects, and the misclassification of cars on roads. Recently, lidar (light detection and ranging) data have been integrated with remotely sensed data to obtain better classification results. In this study, we first conducted maximum likelihood classification (MLC) experiments, a traditional pixelbased classification method, to identify features suitable for urban classification using lidar data and aerial imagery. The addition of lidar height data improved the overall accuracy by up to 28 and 18 percent, respectively, compared to cases with only red‐green‐blue (RGB) and multispectral imagery. To further improve classification, we propose a knowledgebased classification system (KBCS) that includes a three-level height, “asphalt road, vegetation, and non-vegetation” (A‐V‐N) classification rule-based scheme and knowledgebased correction (KBC). The proposed KBCS improved overall accuracy by 12 and 7 percent compared to maximum likelihood and object-based classification, respectively.

Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain

Introduction Detection of acute intracranial hemorrhage (AIH) is a primary task in image interpretation of computer tomography (CT) of brain for patients suffering from acute neurological disturbance or head injury. Although CT readily depicts AIH, interpretation can be difficult especially when the lesion is inconspicuous or the reader is inexperienced. Objective To develop a computer aided detection system that improves diagnostic accuracy of small AIH on brain CT. Materials and methods Intracranial contents are first segmented by thresholding and morphological operations, which are then subjected to denoising and adjustment for CT cupping artifacts. The brain is then automatically realigned into normal position. AIH candidates are extracted based on top-hat transformation and left–right asymmetry. AIH candidates are registered against a normalized coordinate system such that the candidates are rendered anatomical information. True AIH is differentiated from mimicking normal variants or artifacts by a knowledge-based classification system incorporating rules that make use of quantified imaging features and anatomical information. A total of 186 clinical cases, including 62 CT studies showing small (<1 cm) AIH, and 124 controls, were retrospectively collected. Forty positive cases and 80 controls were used for the training of the CAD. Twenty-two positive cases and 44 controls were used in the validation of the CAD system. Regions of AIH identified by two experienced radiologists were used as gold standard. The size of individual AIH volume was also recorded. Results On a per patient basis, the system achieved sensitivity of 95% (38/40) and specificity of 88.8% (71/80) in the training dataset. The sensitivity and specificity were 100% (22/22) and 84.1% (37/44) respectively for the diagnosis of AIH in the validation cases. Individual cases contained variable number of AIH volumes. There were 77 lesions in the 40 training cases and 46 lesions in the 22 validation cases. On a per lesion basis, the sensitivities were 84.4% (65/77) and 82.6% (38/46) for all lesions 10 mm or smaller for the training and validation datasets, respectively. False positive rates were 0.19 (23/120) and 0.29 (19/66) false positive lesion per case for the training and validation datasets, respectively. Conclusion This study demonstrated that CAD is valuable for detection of small AIH on brain CT.