Detection of Right Ventricular Dysfunction Based on Pulmonary Embolism Dataset

Published: 17 December 2024| Version 1 | DOI: 10.17632/bm4v4rzdyd.1
Contributor:
Mehmet Tahir HUYUT

Description

Only individuals over the age of 18 were included in the study. There are no individual characteristics (name-surname, ID number, etc.) that define the patients in our dataset. All individual data are anonymized. In this study, the characteristics of patients diagnosed with Pulmonary Embolism (PE) were analyzed and 20 characteristics with no missing data were determined. These characteristics include the patients' age, gender, comorbidities, and common characteristics used in the diagnosis of RVD. In this dataset, it was recorded whether 163 patients diagnosed with PE experienced Right Heart Failure (RHF) or Right Ventricular Dysfunction (RVD). Each row in the dataset provides information about a patient's characteristics. The first column shows the gender of the PE patients, the second column shows their ages, and the other columns show various characteristics used in the diagnosis of RVD. The last column shows whether the PE patients were diagnosed with RVD by the physician. The age characteristic was recorded as quantitative data. All other data were recorded in nominal data format (Yes-None and etc.). The dataset is summarized in the table below. This dataset includes a patient population that is very difficult to collect. Therefore, the detection of RVD in this patient population is important. This dataset can be used in the classification predictions of all supervised ML and artificial intelligence models. It is a suitable dataset for the classification performance of various models. Table 1. List of features indicating the range of changes in values. № Feature name Value range Value description 1 Sex ( 0-1 ) (1:Male, 0:Female) 2 Age ( 22-93 ) - 3 Thrombus Main Pulmonary ( 0-1 ) (1: Presence, 0: Absence) 4 Thrombus Lobe Arteries ( 0-1 ) (1: Presence, 0: Absence) 5 Thrombus Segment ( 0-1 ) (1: Presence, 0: Absence) 6 Thrombus Subsegment ( 0-1 ) (1: Presence, 0: Absence) 7 Thrombus ( 0-1 ) (1: Unilateral, 0: Bilateral) 8 Right Ventricular Involvement ( 0-1 ) (1: Presence, 0: Absence) 9 DVT ( 0-1 ) (1: Presence, 0: Absence) 10 DVT Distal Ven ( 0-1 ) (1: Presence, 0: Absence) 11 DVT Proksimal Ven ( 0-1 ) (1: Presence, 0: Absence) 12 Appearance of DVT ( 0-2 ) (Absence: 0 Unilateral :1 Bilateral: 2) 13 Comorbid Disease ( 0-1 ) (1: Presence, 0: Absence) 14 Malignancy ( 0-1 ) (1: Presence, 0: Absence) 15 Diabetes Mellitus ( 0-1 ) (1: Presence, 0: Absence) 16 Hypertension ( 0-1 ) (1: Presence, 0: Absence) 17 Coronary Artery Disease (CAD) ( 0-1 ) (1: Presence, 0: Absence) 18 COPD ( 0-1 ) (1: Presence, 0: Absence) 19 Asthma ( 0-1 ) (1: Presence, 0: Absence) 20 Cerebrovascular Occlusion ( 0-1 ) (1: Presence, 0: Absence) DVT: Deep vein thrombosis. Please cite the following four articles when you use any of these datasets:

Files

Institutions

Erzincan Universitesi Tip Fakultesi

Categories

Artificial Intelligence, Health Informatics, Medical Informatics, Medical Research Model

Licence