4 research outputs found
Deep learning model for detection of pain intensity from facial expression
Many people who are suffering from a chronic pain face pe- riods of acute pain and resulting problems during their illness and ade- quate reporting of symptoms is necessary for treatment. Some patients have difficulties in adequately alerting caregivers to their pain or describ- ing the intensity which can impact on effective treatment. Pain and its intensity can be noticeable in ones face. Movements in facial muscles can depict ones current emotional state. Machine learning algorithms can detect pain intensity from facial expressions. The algorithm can ex- tract and classify facial expression of pain among patients. In this paper, we propose a new deep learning model for detection of pain intensity from facial expressions. This automatic pain detection system may help clinicians to detect pain and its intensity in patients and by doing this healthcare organizations may have access to more complete and more regular information of patients regarding their pain
Performance improvement of decision trees for diagnosis of coronary artery disease using multi filtering approach
The heart is one of the strongest muscular organs in the human body. Every year, this disease can kill many people in the world. Coronary artery disease (CAD) is named as the most common type of heart disease. Four well-known decision trees (DTs) are applied on the Z-Alizadeh Sani CAD dataset, which consists of J48, BF tree, REP tree, and NB tree. A multi filtering approach, named MFA, was used to modify the weight of attributes to improve the performance of DTs in this study. The model was applied on three main coronary arteries including the Left Anterior Descending (LAD), Left Circumflex (LCX), and Right Coronary Artery (RCA). The obtained results show that data balancing has a valuable impact on the performance of DTs. The comparison results show that this study provides the best results applied on the Z-Alizadeh Sani dataset compared to previous studies. The proposed MFA could improve the performance of the classic DTs algorithms significantly, with the highest accuracies obtained by NB tree for LAD, LCX, and RCA are 94.90%, 92.97% and 93.43%, respectively
Personalized Federated Deep Learning for Pain Estimation From Face Images
Standard machine learning approaches require centralizing the users' data in
one computer or a shared database, which raises data privacy and
confidentiality concerns. Therefore, limiting central access is important,
especially in healthcare settings, where data regulations are strict. A
potential approach to tackling this is Federated Learning (FL), which enables
multiple parties to collaboratively learn a shared prediction model by using
parameters of locally trained models while keeping raw training data locally.
In the context of AI-assisted pain-monitoring, we wish to enable
confidentiality-preserving and unobtrusive pain estimation for long-term
pain-monitoring and reduce the burden on the nursing staff who perform frequent
routine check-ups. To this end, we propose a novel Personalized Federated Deep
Learning (PFDL) approach for pain estimation from face images. PFDL performs
collaborative training of a deep model, implemented using a lightweight CNN
architecture, across different clients (i.e., subjects) without sharing their
face images. Instead of sharing all parameters of the model, as in standard FL,
PFDL retains the last layer locally (used to personalize the pain estimates).
This (i) adds another layer of data confidentiality, making it difficult for an
adversary to infer pain levels of the target subject, while (ii) personalizing
the pain estimation to each subject through local parameter tuning. We show
using a publicly available dataset of face videos of pain (UNBC-McMaster
Shoulder Pain Database), that PFDL performs comparably or better than the
standard centralized and FL algorithms, while further enhancing data privacy.
This, has the potential to improve traditional pain monitoring by making it
more secure, computationally efficient, and scalable to a large number of
individuals (e.g., for in-home pain monitoring), providing timely and
unobtrusive pain measurement.Comment: 12 pages, 6 figure