Mining Themes in Clinical Notes to Identify Phenotypes and to Predict
Length of Stay in Patients admitted with Heart Failure
- URL: http://arxiv.org/abs/2305.19373v1
- Date: Tue, 30 May 2023 19:30:40 GMT
- Title: Mining Themes in Clinical Notes to Identify Phenotypes and to Predict
Length of Stay in Patients admitted with Heart Failure
- Authors: Ankita Agarwal, Tanvi Banerjee, William L. Romine, Krishnaprasad
Thirunarayan, Lingwei Chen, Mia Cajita
- Abstract summary: Heart failure is a syndrome which occurs when the heart is not able to pump blood and oxygen to support other organs in the body.
Identifying the underlying themes in the diagnostic codes and procedure reports of patients admitted for heart failure could reveal the clinical phenotypes associated with heart failure.
- Score: 3.350712823657887
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Heart failure is a syndrome which occurs when the heart is not able to pump
blood and oxygen to support other organs in the body. Identifying the
underlying themes in the diagnostic codes and procedure reports of patients
admitted for heart failure could reveal the clinical phenotypes associated with
heart failure and to group patients based on their similar characteristics
which could also help in predicting patient outcomes like length of stay. These
clinical phenotypes usually have a probabilistic latent structure and hence, as
there has been no previous work on identifying phenotypes in clinical notes of
heart failure patients using a probabilistic framework and to predict length of
stay of these patients using data-driven artificial intelligence-based methods,
we apply natural language processing technique, topic modeling, to identify the
themes present in diagnostic codes and in procedure reports of 1,200 patients
admitted for heart failure at the University of Illinois Hospital and Health
Sciences System (UI Health). Topic modeling identified twelve themes each in
diagnostic codes and procedure reports which revealed information about
different phenotypes related to various perspectives about heart failure, to
study patients' profiles and to discover new relationships among medical
concepts. Each theme had a set of keywords and each clinical note was labeled
with two themes - one corresponding to its diagnostic code and the other
corresponding to its procedure reports along with their percentage
contribution. We used these themes and their percentage contribution to predict
length of stay. We found that the themes discovered in diagnostic codes and
procedure reports using topic modeling together were able to predict length of
stay of the patients with an accuracy of 61.1% and an Area under the Receiver
Operating Characteristic Curve (ROC AUC) value of 0.828.
Related papers
- Estimating the severity of dental and oral problems via sentiment
classification over clinical reports [0.8287206589886879]
Analyzing authors' sentiments in texts can be practical and useful in various fields, including medicine and dentistry.
Deep learning model based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network architecture, known as CNN-LSTM, was developed to detect severity level of patient's problem.
arXiv Detail & Related papers (2024-01-17T14:33:13Z) - Informing clinical assessment by contextualizing post-hoc explanations
of risk prediction models in type-2 diabetes [50.8044927215346]
We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state.
We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability.
Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
arXiv Detail & Related papers (2023-02-11T18:07:11Z) - Enriching Unsupervised User Embedding via Medical Concepts [51.17532619610099]
Unsupervised user embedding aims to encode patients into fixed-length vectors without human supervisions.
Medical concepts extracted from the clinical notes contain rich connections between patients and their clinical categories.
We propose a concept-aware unsupervised user embedding that jointly leverages text documents and medical concepts from two clinical corpora.
arXiv Detail & Related papers (2022-03-20T18:54:05Z) - Similarity-based prediction of Ejection Fraction in Heart Failure
Patients [0.0]
We propose a novel data-driven statistical machine learning approach, named Feature Imputation via Local Likelihood (FILL)
We test our method using a particularly challenging problem: differentiating heart failure patients with reduced versus preserved ejection fraction (HFrEF and HFpEF respectively)
Despite these difficulties, our method is shown to be capable of inferring heart failure patients with HFpEF with a precision above 80% when considering multiple scenarios.
arXiv Detail & Related papers (2022-03-14T14:19:08Z) - Identification of Ischemic Heart Disease by using machine learning
technique based on parameters measuring Heart Rate Variability [50.591267188664666]
In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects were used to train and validate a series of several ANN.
The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset.
arXiv Detail & Related papers (2020-10-29T19:14:41Z) - Personalized pathology test for Cardio-vascular disease: Approximate
Bayesian computation with discriminative summary statistics learning [48.7576911714538]
We propose a platelet deposition model and an inferential scheme to estimate the biologically meaningful parameters using approximate computation.
This work opens up an unprecedented opportunity of personalized pathology test for CVD detection and medical treatment.
arXiv Detail & Related papers (2020-10-13T15:20:21Z) - Trajectories, bifurcations and pseudotime in large clinical datasets:
applications to myocardial infarction and diabetes data [94.37521840642141]
We suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values.
The methodology is based on application of elastic principal graphs which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection and quantifying the geodesic distances (pseudotime) in partially ordered sequences of observations.
arXiv Detail & Related papers (2020-07-07T21:04:55Z) - Towards Patient Record Summarization Through Joint Phenotype Learning in
HIV Patients [1.598617270887469]
We propose an unsupervised phenotyping approach that jointly learns a large number of phenotypes/problems across structured and unstructured data.
We ground our experiments in phenotyping patients from an HIV clinic in a large urban care institution.
We find that the learned phenotypes and their relatedness are clinically valid when assessed by clinical experts.
arXiv Detail & Related papers (2020-03-09T15:41:58Z) - A Corpus for Detecting High-Context Medical Conditions in Intensive Care
Patient Notes Focusing on Frequently Readmitted Patients [28.668217175230822]
This dataset contains 1102 Discharge Summaries and 1000 Nursing Progress Notes.
Annotated phenotypes include treatment non-adherence, chronic pain, advanced/metastatic cancer, as well as 10 other phenotypes.
This dataset can be utilized for academic and industrial research in medicine and computer science, particularly within the field of medical natural language processing.
arXiv Detail & Related papers (2020-03-06T05:56:49Z) - Learning Dynamic and Personalized Comorbidity Networks from Event Data
using Deep Diffusion Processes [102.02672176520382]
Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals.
In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition.
We develop deep diffusion processes to model "dynamic comorbidity networks"
arXiv Detail & Related papers (2020-01-08T15:47:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.