4,008 research outputs found
Neural networks versus Logistic regression for 30 days all-cause readmission prediction
Heart failure (HF) is one of the leading causes of hospital admissions in the
US. Readmission within 30 days after a HF hospitalization is both a recognized
indicator for disease progression and a source of considerable financial burden
to the healthcare system. Consequently, the identification of patients at risk
for readmission is a key step in improving disease management and patient
outcome. In this work, we used a large administrative claims dataset to
(1)explore the systematic application of neural network-based models versus
logistic regression for predicting 30 days all-cause readmission after
discharge from a HF admission, and (2)to examine the additive value of
patients' hospitalization timelines on prediction performance. Based on data
from 272,778 (49% female) patients with a mean (SD) age of 73 years (14) and
343,328 HF admissions (67% of total admissions), we trained and tested our
predictive readmission models following a stratified 5-fold cross-validation
scheme. Among the deep learning approaches, a recurrent neural network (RNN)
combined with conditional random fields (CRF) model (RNNCRF) achieved the best
performance in readmission prediction with 0.642 AUC (95% CI, 0.640-0.645).
Other models, such as those based on RNN, convolutional neural networks and CRF
alone had lower performance, with a non-timeline based model (MLP) performing
worst. A competitive model based on logistic regression with LASSO achieved a
performance of 0.643 AUC (95%CI, 0.640-0.646). We conclude that data from
patient timelines improve 30 day readmission prediction for neural
network-based models, that a logistic regression with LASSO has equal
performance to the best neural network model and that the use of administrative
data result in competitive performance compared to published approaches based
on richer clinical datasets
Prediction of future hospital admissions - what is the tradeoff between specificity and accuracy?
Large amounts of electronic medical records collected by hospitals across the
developed world offer unprecedented possibilities for knowledge discovery using
computer based data mining and machine learning. Notwithstanding significant
research efforts, the use of this data in the prediction of disease development
has largely been disappointing. In this paper we examine in detail a recently
proposed method which has in preliminary experiments demonstrated highly
promising results on real-world data. We scrutinize the authors' claims that
the proposed model is scalable and investigate whether the tradeoff between
prediction specificity (i.e. the ability of the model to predict a wide number
of different ailments) and accuracy (i.e. the ability of the model to make the
correct prediction) is practically viable. Our experiments conducted on a data
corpus of nearly 3,000,000 admissions support the authors' expectations and
demonstrate that the high prediction accuracy is maintained well even when the
number of admission types explicitly included in the model is increased to
account for 98% of all admissions in the corpus. Thus several promising
directions for future work are highlighted.Comment: In Proc. International Conference on Bioinformatics and Computational
Biology, April 201
Systematic review of hospital readmissions in stroke patients
Background Previous evidence on factors and causes of readmissions associated with high-impact users of stroke is scanty. The aim of the study was to investigate common causes and pattern of short- and long-term readmissions stroke patients by conducting a systematic review of studies using hospital administrative data. Common risk factors associated with the change of readmission rate were also examined. Methods The literature search was conducted from 15th February to 15th March 2016 using various databases, such as Medline, Embase, and Web of Science. Results There were total of 24 studies (n=2,126,617) included in the review. Only 4 studies assessed causes of readmissions in stroke patients with the follow-up duration from 30 days to 5 years. Common causes of readmissions in majority of the studies were recurrent stroke, infections and cardiac conditions. Common patient-related risk factors associated with increased readmission rate were age and history of coronary heart disease, heart failure, renal disease, respiratory disease, peripheral arterial disease and diabetes. Among stroke-related factors, length of stay of index stroke admission was associated with increased readmission rate, followed by bowel incontinence, feeding tube and urinary catheter. Conclusion Although risk factors and common causes of readmission were identified, but none of the previous studies investigated causes and their sequence of readmissions among high-impact stroke users
Predicting Unplanned Hospital Readmissions using Patient Level Data
The rate of unplanned hospital readmissions in the US is likely to face a steady rise after 2020. Hence, this issue has received considerable critical attention with the policy makers. Majority of hospitals in the US pay millions of dollars as penalty for readmitting patients within 30 days due to strict norms imposed by the Hospital Readmission Reduction Program. In this study, we develop two novel models: PURE (Predicting Unplanned Readmissions using Embeddings) and Hybrid DeepR, which uses the historical medical events of patients to predict readmissions within 30 days. Both these models are hybrid sequence models that leverage both sequential events (history of events) and static features (like gender, blood pressure) of the patients to mine patterns in the data. Our results are promising, and they benchmark previous results in predicting hospital readmissions. The contributions of this study add to existing literature on healthcare analytics
Deepr: A Convolutional Net for Medical Records
Feature engineering remains a major bottleneck when creating predictive
systems from electronic medical records. At present, an important missing
element is detecting predictive regular clinical motifs from irregular episodic
records. We present Deepr (short for Deep record), a new end-to-end deep
learning system that learns to extract features from medical records and
predicts future risk automatically. Deepr transforms a record into a sequence
of discrete elements separated by coded time gaps and hospital transfers. On
top of the sequence is a convolutional neural net that detects and combines
predictive local clinical motifs to stratify the risk. Deepr permits
transparent inspection and visualization of its inner working. We validate
Deepr on hospital data to predict unplanned readmission after discharge. Deepr
achieves superior accuracy compared to traditional techniques, detects
meaningful clinical motifs, and uncovers the underlying structure of the
disease and intervention space
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