scholarly journals The Classificatied Prediction of Coronary Heart Disease Based on Patient Similarity Analysis

2021 ◽  
Author(s):  
Xinyao LI ◽  
Linlin ZHANG ◽  
Xuehua BI ◽  
Ying ZHANG ◽  
Guanglei YU ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Predictive Models ◽  
Distance Matrix ◽  
Clinical Decision ◽  
Medical Database ◽  
Traditional Classification ◽  
Promising Solution ◽  
Structured Representation

Abstract Objective:It is important for physicians' clinical decision support to classify the coronary heart disease (CHD).Customizing personalized predictive models for patients requires selecting a patient group from an existing medical database that most closely resembles the indexed patients. In this study,we introduce a new concept that using the patient similarity for the classification of patient with CHD.Materials and methods: We performed a structured representation of CHD patients. Obtain the multidimensional attribute distance matrix between patient pairs by calculating the multidimensional attribute distance of the patients. Predict similarity between patient pairs using machine learning (ML) models to predict clinical outcomes for indexed patients based on matched similar patients.Results:The new measure shows marked improvements over the traditional classification measures. LightGBM is the top-performing ML model. The best model achieved 88.52% accuracy.Conclusion:The medical applications of ML supported by similarity analytics represent a promising solution through which to reduce the physican workload to achieve the goal of “precision medicine”.

Abstract 129: Clinical Predictive Models for Valvular Heart Disease: A Systematic Review of the Literature

2017 ◽  
Vol 10 (suppl_3) ◽  
Author(s):  
Benjamin S Wessler ◽  
Muhammad Ajlan ◽  
Christine Lundquist ◽  
Zuhair Natto ◽  
Jessica Paulus ◽  
...  
Keyword(s):  
Systematic Review ◽  
Heart Disease ◽  
Predictive Models ◽  
Valvular Heart Disease ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Surgical Interventions ◽  
Percutaneous Interventions ◽  
C Statistic ◽  
Original Report

Objectives: Pre-procedure risk assessment is central to clinical decision making for patients with advanced valvular heart disease (VHD) and treatments are increasingly being offered to patients with elevated pre-procedure risk. While there are numerous clinical predictive models (CPMs) available for patients with VHD, the relative performance of these CPMs is largely unknown. Here we describe the performance of CPMs available for patients with VHD with specific attention to whether CPMs have been externally validated. Methods: To identify CPMs for patients with VHD, we conducted a systematic review of the Tufts PACE CPM Registry, a comprehensive database of cardiovascular CPMs. For each identified CPM for patients with VHD, we performed a complete citation search using Scopus to identify any external validations of these models published in other articles. We extracted information on CPM performance in both the original report and also the external validations. For external validations we calculated the relative percent decrease in discrimination. Results: We identified 41 CPMs predicting outcomes for patients with VHD. 33 (81%) predict outcomes following surgical intervention, 5 (12%) predict outcomes following percutaneous interventions, and 3 (7%) predict outcomes in the absence of intervention. Only 30/41 (73%) of the CPMs report a c-statistic. The median reported c- statistic was 0.77 [IQR, 0.04] for CPMs predicting outcomes following surgical interventions, 0.68 [IQR, 0.04] for CPMs for percutaneous interventions, and 0.83 [IQR, 0.07] for CPMs predicting outcomes in the absence of intervention. While a total of 69 external validations of these CPMs have been published, only 21 (51%) of the CPMs have ever been externally validated. For external validations that report c- statistics, we noted a median percent decrement in discrimination of -27.6% [IQR, -37.4] ( Figure) . Conclusion: While there are numerous CPMs for patients with VHD, performance is often incompletely reported and half of these CPMs have never been externally validated. The CPMs that have been externally validated generally show substantially worse discrimination in external datasets compared to the derivation datasets.

The use of a network medicine approach might result in innovative strategies for lowering coronary heart disease and CV risks

2021 ◽  
Author(s):  
Moataz Dowaidar
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Single Cell Analysis ◽  
Clinical Decision ◽  
Clinical Situation ◽  
Molecular Networks ◽  
Network Medicine ◽  
Innovative Strategies ◽  
Comprehensive Picture ◽  
Correct Estimation

The reductionist approach of stringent guidelines for treating dyslipidemia led to significant lipoprotein transported cholesterol (LDLC) reduction with statin-based treatment to prevent or postpone development of atherosclerosis. Correct estimation of residual CV risk, however, remains a critical problem requiring the development of new integrated approaches Using a network-medicine approach might lead to creative methods. This is clearly a multidisciplinary technique that will be difficult in a clinical situation. To get a comprehensive picture of nodes and disease modules, we need to dissect a dyslipidemic patient to the level of single-cell analysis and then reconstruct the picture. These efforts are crucial to understanding the molecular networks that drive dyslipidemia and atherosclerosis. Moreover, in clinical assessment of persons with suspected coronary heart disease (CHD), computer-aided decision-making is more widespread. On the other hand, doctors should remember that robots lack a "sense of thinking" and should thus distrust the reliability of a purely robotic clinical decision. As a consequence, AI should be called "increased intelligence," which can help physicians make judgments but should keep the ultimate strategy in the physician's hands.

scholarly journals Coronary Calcium Score Improves Classification of Coronary Heart Disease Risk in the Elderly

2010 ◽  
Vol 56 (17) ◽  
pp. 1407-1414 ◽  
Author(s):  
Suzette E. Elias-Smale ◽  
Rozemarijn Vliegenthart Proença ◽  
Michael T. Koller ◽  
Maryam Kavousi ◽  
Frank J.A. van Rooij ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Coronary Heart Disease Risk ◽  
Disease Risk ◽  
Calcium Score ◽  
The Elderly ◽  
Coronary Calcium ◽  
Coronary Calcium Score ◽  
Heart Disease Risk

scholarly journals Diagnostic Certainty as a Source of Medical Practice Variation in Coronary Heart Disease: Results from a Cross-National Experiment of Clinical Decision Making

2009 ◽  
Vol 29 (5) ◽  
pp. 606-618 ◽  
Author(s):  
Karen E. Lutfey ◽  
Carol L. Link ◽  
Lisa D. Marceau ◽  
Richard W. Grant ◽  
Ann Adams ◽  
...  
Keyword(s):  
Health Care ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Medical Practice ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
The United States ◽  
Practice Variation ◽  
Diagnostic Certainty ◽  
Cross National

The authors examined physician diagnostic certainty as one reason for cross-national medical practice variation. Data are from a factorial experiment conducted in the United States, the United Kingdom, and Germany, estimating 384 generalist physicians’ diagnostic and treatment decisions for videotaped vignettes of actor patients depicting a presentation consistent with coronary heart disease (CHD). Despite identical vignette presentations, the authors observed significant differences across health care systems, with US physicians being the most certain and German physicians the least certain (P < 0.0001). Physicians were least certain of a CHD diagnoses when patients were younger and female (P < 0.0086), and there was additional variation by health care system (as represented by country) depending on patient age (P < 0.0100) and race (P < 0.0021). Certainty was positively correlated with several clinical actions, including test ordering, prescriptions, referrals to specialists, and time to follow-up.

scholarly journals Multiscale classification of heart failure phenotypes by unsupervised clustering of unstructured electronic medical record data

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Tasha Nagamine ◽  
Brian Gillette ◽  
Alexey Pakhomov ◽  
John Kahoun ◽  
Hannah Mayer ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Disease ◽  
Language Processing ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Statistical Testing ◽  
Clinical Notes ◽  
Data Driven Approach ◽  
Obesity Hypertension

AbstractAs a leading cause of death and morbidity, heart failure (HF) is responsible for a large portion of healthcare and disability costs worldwide. Current approaches to define specific HF subpopulations may fail to account for the diversity of etiologies, comorbidities, and factors driving disease progression, and therefore have limited value for clinical decision making and development of novel therapies. Here we present a novel and data-driven approach to understand and characterize the real-world manifestation of HF by clustering disease and symptom-related clinical concepts (complaints) captured from unstructured electronic health record clinical notes. We used natural language processing to construct vectorized representations of patient complaints followed by clustering to group HF patients by similarity of complaint vectors. We then identified complaints that were significantly enriched within each cluster using statistical testing. Breaking the HF population into groups of similar patients revealed a clinically interpretable hierarchy of subgroups characterized by similar HF manifestation. Importantly, our methodology revealed well-known etiologies, risk factors, and comorbid conditions of HF (including ischemic heart disease, aortic valve disease, atrial fibrillation, congenital heart disease, various cardiomyopathies, obesity, hypertension, diabetes, and chronic kidney disease) and yielded additional insights into the details of each HF subgroup’s clinical manifestation of HF. Our approach is entirely hypothesis free and can therefore be readily applied for discovery of novel insights in alternative diseases or patient populations.

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