7,785 research outputs found
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
Significance of Machine Learning Algorithms to Improve Predictive Analytics in Chronic Disease Management through Pharmacogenomics
The treatment of chronic diseases is an important concern in global health. Machine learning (ML)-based disease prediction models are becoming more important for making informed medical decisions in light of the paradigm shift towards preventative care. Integrating genetic, pharmacogenomic, personal health, and psychosocial data can greatly assist healthcare practitioners in making treatment-related decisions for patients with chronic diseases. This study utilizes a Deep Convolutional Neural Network-assisted Chronic Disease Management (DCNN-CDM) through pharmacogenomics and an improved predictive analytics model to enable informed real-time decision-making at the point of care. Data augmentation in terms of feature space allows the DCNN model to avoid over-fitting while effectively capturing high-level features submerged in chronic disease datasets. Afterwards, this work suggests an attention-empowered DCNN model to enhance sick case diagnosis accuracy, which augments data regarding sample space, thereby alleviating the class imbalance issue. Electronic health information data mining is now using predictive analytics to determine individuals at risk of acquiring chronic disease problems. The suggested model may aid in the early and accurate diagnosis of chronic diseases. The numerical outcomes demonstrate that the recommended DCNN-CDM model increases the accuracy rate of 98.7%, patient monitoring rate of 97.5%, F1-score rate of 96.3% and predictive performance rate of 95.1% compared to other existing methodologies
A comprehensive study on disease risk predictions in machine learning
Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. Comprehensive survey on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavours have been shifted
Risk assessment for progression of Diabetic Nephropathy based on patient history analysis
A nefropatia diabética (ND) é uma das complicações mais comuns em doentes com
diabetes. Trata-se de uma doença crónica que afeta progressivamente os rins,
podendo resultar numa insuficiência renal. A digitalização permitiu aos hospitais
armazenar as informações dos doentes em registos de saúde eletrónicos (RSE). A
aplicação de algoritmos de Machine Learning (ML) a estes dados pode permitir a
previsão do risco na evolução destes doentes, conduzindo a uma melhor gestão da
doença. O principal objetivo deste trabalho é criar um modelo preditivo que tire
partido do historial do doente presente nos RSE. Foi aplicado neste trabalho o maior
conjunto de dados de doentes portugueses com DN, seguidos durante 22 anos pela
Associação Protetora dos Diabéticos de Portugal (APDP). Foi desenvolvida uma
abordagem longitudinal na fase de pré-processamento de dados, permitindo que
estes fossem servidos como entrada para dezasseis algoritmos de ML distintos. Após
a avaliação e análise dos respetivos resultados, o Light Gradient Boosting Machine
foi identificado como o melhor modelo, apresentando boas capacidades de previsão.
Esta conclusão foi apoiada não só pela avaliação de várias métricas de classificação
em dados de treino, teste e validação, mas também pela avaliação do seu
desempenho por cada estádio da doença. Para além disso, os modelos foram
analisados utilizando gráficos de feature ranking e através de análise estatística.
Como complemento, são ainda apresentados a interpretabilidade dos resultados
através do método SHAP, assim como a distribuição do modelo utilizando o Gradio
e os servidores da Hugging Face. Através da integração de técnicas ML, de um
método de interpretação e de uma aplicação Web que fornece acesso ao modelo,
este estudo oferece uma abordagem potencialmente eficaz para antecipar a evolução
da ND, permitindo que os profissionais de saúde tomem decisões informadas para
a prestação de cuidados personalizados e gestão da doença
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