A patient-similarity-based model for diagnostic prediction

Jia, Zheng; Zeng, Xian; Duan, Huilong; Lü, Xudong; Li, Haomin

doi:10.1016/j.ijmedinf.2019.104073

Cited by 45 publications

(27 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the calculation of patient similarity, the similarity is mainly weighted by traditional methods to optimize the similarity calculation such as Manhattan distance and Euclidean distance. Jia [12] et al proposed a diagnosis prediction framework based on patient similarity, where they defined patient similarity as the similarity between two sets of diagnoses. Q Suo [13] et al used a convolutional neural network (CNN) to capture locally important information in the EHR, and then fed the learned representations into a modified triple-loss neural network for training.…”

Section: Related Workmentioning

confidence: 99%

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

Zhang

et al. 2021

Preprint

View full text Add to dashboard Cite

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”.

show abstract

Section: Related Workmentioning

confidence: 99%

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

Zhang

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…can be defined as the process by which a healthcare professional interacts with a patient when interpreting patient data, weighting the benefits and risks of treatment options, and trying to incorporate patient preferences to finally design a personalized treatment plan [22]. Prior knowledge about similar patients could certainly guide such treatment decisions for the treatment plan, as well.…”

Section: Plos Onementioning

confidence: 99%

A framework to build similarity-based cohorts for personalized treatment advice – a standardized, but flexible workflow with the R package SimBaCo

2020

View full text Add to dashboard Cite

Along with increasing amounts of big data sources and increasing computer performance, real-world evidence from such sources likewise gains in importance. While this mostly applies to population averaged results from analyses based on the all available data, it is also possible to conduct so-called personalized analyses based on a data subset whose observations resemble a particular patient for whom a decision is to be made. Claims data from statutory health insurance companies could provide necessary information for such personalized analyses. To derive treatment recommendations from them for a particular patient in everyday care, an automated, reproducible and efficiently programmed workflow would be required. We introduce the R-package SimBaCo (Similarity-Based Cohort generation) offering a simple, but modular, and intuitive framework for this task. With the six built-in R-functions, this framework allows the user to create similarity cohorts tailored to the characteristics of particular patients. An exemplary workflow illustrates the distinct steps beginning with an initial cohort selection according to inclusion and exclusion criteria. A plotting function facilitates investigating a particular patient's characteristics relative to their distribution in a reference cohort, for example the initial cohort or the precision cohort after the data has been trimmed in accordance with chosen variables for similarity finding. Such precision cohorts allow any form of personalized analysis, for example personalized analyses of comparative effectiveness or customized prediction models developed from precision cohorts. In our exemplary workflow, we provide such a treatment comparison whereupon a treatment decision for a particular patient could be made. This is only one field of application where personalized results can directly support the process of clinical reasoning by leveraging information from individual patient data. With this modular package at hand, personalized studies can efficiently weight benefits and risks of treatment options of particular patients.

show abstract

“…In a major medical procedure, for instance, seeking advice from several specialists at different medical centers is a common practice. Even for internists and physicians, predicting diagnosis is still challenging [29] because of the availability of a vast volume of clinical data that exceeds the ability of the human brain to assimilate and analyze [30]. This issue raises the need for soliciting an expert and, thus, using agreement measure.…”

Section: Application: Diagnostic Predictionmentioning

confidence: 99%

Similarity and Agreement Measures and Their Application in Medical Diagnostic Prediction System

Alksasbeh

Al-Kaseasbeh

2020

IEEE Access

View full text Add to dashboard Cite

A patient-similarity-based model for diagnostic prediction

Cited by 45 publications

References 40 publications

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

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

A framework to build similarity-based cohorts for personalized treatment advice – a standardized, but flexible workflow with the R package SimBaCo

Similarity and Agreement Measures and Their Application in Medical Diagnostic Prediction System

Contact Info

Product

Resources

About