2,252 research outputs found
Intuitive and interpretable visual communication of a complex statistical model of disease progression and risk
Computer science and machine learning in particular are increasingly lauded for their potential to aid medical practice. However, the highly technical nature of the state of the art techniques can be a major obstacle in their usability by health care professionals and thus, their adoption and actual practical benefit. In this paper we describe a software tool which focuses on the visualization of predictions made by a recently developed method which leverages data in the form of large scale electronic records for making diagnostic predictions. Guided by risk predictions, our tool allows the user to explore interactively different diagnostic trajectories,or display cumulative long term prognostics, in an intuitive and easily interpretable manner.Postprin
DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways
Clinical researchers use disease progression models to understand patient
status and characterize progression patterns from longitudinal health records.
One approach for disease progression modeling is to describe patient status
using a small number of states that represent distinctive distributions over a
set of observed measures. Hidden Markov models (HMMs) and its variants are a
class of models that both discover these states and make inferences of health
states for patients. Despite the advantages of using the algorithms for
discovering interesting patterns, it still remains challenging for medical
experts to interpret model outputs, understand complex modeling parameters, and
clinically make sense of the patterns. To tackle these problems, we conducted a
design study with clinical scientists, statisticians, and visualization
experts, with the goal to investigate disease progression pathways of chronic
diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's
disease, and chronic obstructive pulmonary disease (COPD). As a result, we
introduce DPVis which seamlessly integrates model parameters and outcomes of
HMMs into interpretable and interactive visualizations. In this study, we
demonstrate that DPVis is successful in evaluating disease progression models,
visually summarizing disease states, interactively exploring disease
progression patterns, and building, analyzing, and comparing clinically
relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic
A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease
Alzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease riskThis work was supported by National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT)-NRF-2020R1A2B5B02002478). In addition, Dr. Jose M. Alonso is Ramon y Cajal Researcher (RYC-2016-19802), and its research is supported by the Spanish Ministry of Science, Innovation and Universities (grants RTI2018-099646-B-I00, TIN2017-84796-C2-1-R, TIN2017-90773-REDT, and RED2018-102641-T) and the Galician Ministry of Education, University and Professional Training (grants ED431F 2018/02, ED431C 2018/29, ED431G/08, and ED431G2019/04), with all grants co-funded by the European Regional Development Fund (ERDF/FEDER program)S
Leveraging Historical Medical Records as a Proxy via Multimodal Modeling and Visualization to Enrich Medical Diagnostic Learning
Simulation-based Medical Education (SBME) has been developed as a
cost-effective means of enhancing the diagnostic skills of novice physicians
and interns, thereby mitigating the need for resource-intensive
mentor-apprentice training. However, feedback provided in most SBME is often
directed towards improving the operational proficiency of learners, rather than
providing summative medical diagnoses that result from experience and time.
Additionally, the multimodal nature of medical data during diagnosis poses
significant challenges for interns and novice physicians, including the
tendency to overlook or over-rely on data from certain modalities, and
difficulties in comprehending potential associations between modalities. To
address these challenges, we present DiagnosisAssistant, a visual analytics
system that leverages historical medical records as a proxy for multimodal
modeling and visualization to enhance the learning experience of interns and
novice physicians. The system employs elaborately designed visualizations to
explore different modality data, offer diagnostic interpretive hints based on
the constructed model, and enable comparative analyses of specific patients.
Our approach is validated through two case studies and expert interviews,
demonstrating its effectiveness in enhancing medical training.Comment: Accepted by IEEE VIS 202
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
A Survey of Multimodal Information Fusion for Smart Healthcare: Mapping the Journey from Data to Wisdom
Multimodal medical data fusion has emerged as a transformative approach in
smart healthcare, enabling a comprehensive understanding of patient health and
personalized treatment plans. In this paper, a journey from data to information
to knowledge to wisdom (DIKW) is explored through multimodal fusion for smart
healthcare. We present a comprehensive review of multimodal medical data fusion
focused on the integration of various data modalities. The review explores
different approaches such as feature selection, rule-based systems, machine
learning, deep learning, and natural language processing, for fusing and
analyzing multimodal data. This paper also highlights the challenges associated
with multimodal fusion in healthcare. By synthesizing the reviewed frameworks
and theories, it proposes a generic framework for multimodal medical data
fusion that aligns with the DIKW model. Moreover, it discusses future
directions related to the four pillars of healthcare: Predictive, Preventive,
Personalized, and Participatory approaches. The components of the comprehensive
survey presented in this paper form the foundation for more successful
implementation of multimodal fusion in smart healthcare. Our findings can guide
researchers and practitioners in leveraging the power of multimodal fusion with
the state-of-the-art approaches to revolutionize healthcare and improve patient
outcomes.Comment: This work has been submitted to the ELSEVIER for possible
publication. Copyright may be transferred without notice, after which this
version may no longer be accessibl
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