Abstract: The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluates the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.
Abstract: In an access-control situation, the role of a user determines whether a data request is appropriate. This paper combines vetted web mining and logic modeling to build a lightweight system for determining the role of a health care provider based only on their prior authorized requests. The model identifies provider roles with 100% recall from very little data. This shows the value of vetted web mining in AI systems, and suggests the impact of the ICD classification on medical practice.
Abstract: Data mining has been used very frequently to extract
hidden information from large databases. This paper suggests the use
of decision trees for continuously extracting the clinical reasoning in
the form of medical expert-s actions that is inherent in large number
of EMRs (Electronic Medical records). In this way the extracted data
could be used to teach students of oral medicine a number of orderly
processes for dealing with patients who represent with different
problems within the practice context over time.
Abstract: This study used Item Analysis, Exploratory Factor
Analysis (EFA) and Reliability Analysis (Cronbach-s α value) to
exam the Questions which selected by the Delphi method based on the
issue of “Socio-technical system (STS)" and user-centered
perspective. A structure questionnaire with seventy-four questions
which could be categorized into nine dimensions (healthcare
environment, organization behaviour, system quality, medical data
quality, service quality, safety quality, user usage, user satisfaction,
and organization net benefits) was provided to evaluate EMR of the
Taiwanese healthcare environment.
Abstract: Transferring patient information between medical care
sites is necessary to deliver better patient care and to reduce medical
cost. So developing of electronic medical records is an important trend
for the world.The Continuity of Care Document (CCD) is product of
collaboration between CDA and CCR standards. In this study, we will
develop a system to generate medical records with entry level based on
CCD template module.
Abstract: The hospital and the health-care center of a
community, as a place for people-s life-care and health-care settings,
must provide more and better services for patients or residents. After
Establishing Electronic Medical Record (EMR) system -which is a
necessity- in the hospital, providing pervasive services is a further
step. Our objective in this paper is to use pervasive computing in a
case study of healthcare, based on EMR database that coordinates
application services over network to form a service environment for
medical and health-care. Our method also categorizes the hospital
spaces into 3 spaces: Public spaces, Private spaces and Isolated
spaces. Although, there are many projects about using pervasive
computing in healthcare, but all of them concentrate on the disease
recognition, designing smart cloths, or provide services only for
patient. The proposed method is implemented in a hospital. The
obtained results show that it is suitable for our purpose.