An ontology-based approach to patient follow-up assessment for continuous and personalized chronic disease management.

OBJECTIVE: Chronic diseases are complex and persistent clinical conditions that require close collaboration among patients and health care providers in the implementation of long-term and integrated care programs. However, current solutions focus partially on intensive interventions at hospitals rather than on continuous and personalized chronic disease management. This study aims to fill this gap by providing computerized clinical decision support during follow-up assessments of chronically ill patients at home. METHODS: We proposed an ontology-based framework to integrate patient data, medical domain knowledge, and patient assessment criteria for chronic disease patient follow-up assessments. A clinical decision support system was developed to implement this framework for automatic selection and adaptation of standard assessment protocols to suit patient personal conditions. We evaluated our method in the case study of type 2 diabetic patient follow-up assessments. RESULTS: The proposed framework was instantiated using real data from 115,477 follow-up assessment records of 36,162 type 2 diabetic patients. Standard evaluation criteria were automatically selected and adapted to the particularities of each patient. Assessment results were generated as a general typing of patient overall condition and detailed scoring for each criterion, providing important indicators to the case manager about possible inappropriate judgments, in addition to raising patient awareness of their disease control outcomes. Using historical data as the gold standard, our system achieved a rate of accuracy of 99.93% and completeness of 95.00%. CONCLUSIONS: This study contributes to improving the accessibility, efficiency and quality of current patient follow-up services. It also provides a generic approach to knowledge sharing and reuse for patient-centered chronic disease management.

Research and Development of Semantics-based Sharable Clinical Pathway Systems.

The clinical pathway (CP) as a novel medical management schema is beneficial for reducing the length of stay, decreasing heath care costs, standardizing clinical activities, and improving medical quality. However, the practicability of CPs is limited by the complexity and expense of adding the standard functions of electronic CPs to existing electronic medical record (EMR) systems. The purpose of this study was to design and develop an independent clinical pathway (ICP) system that is sharable with different EMR systems. An innovative knowledge base pattern was designed with separate namespaces for global knowledge, local knowledge, and real-time instances. Semantic web technologies were introduced to support knowledge sharing and intelligent reasoning. The proposed system, which was developed in a Java integrated development environment, achieved standard functions of electronic CPs without modifying existing EMR systems and integration environments in hospitals. The interaction solution between the pathway system and the EMR system simplifies the integration procedures with other hospital information systems. Five categories of transmission information were summarized to ensure the interaction process. Detailed procedures for the application of CPs to patients and managing exceptional alerts are presented by explicit data flow analysis. Compared to embedded pathway systems, independent pathway systems feature greater feasibility and practicability and are more advantageous for achieving the normalized management of standard CPs.

Creating hospital-specific customized clinical pathways by applying semantic reasoning to clinical data.

OBJECTIVE: Clinical pathways (CPs) are widely studied methods to standardize clinical intervention and improve medical quality. However, standard care plans defined in current CPs are too general to execute in a practical healthcare environment. The purpose of this study was to create hospital-specific personalized CPs by explicitly expressing and replenishing the general knowledge of CPs by applying semantic analysis and reasoning to historical clinical data. METHODS: A semantic data model was constructed to semantically store clinical data. After querying semantic clinical data, treatment procedures were extracted. Four properties were self-defined for local ontology construction and semantic transformation, and three Jena rules were proposed to achieve error correction and pathway order recognition. Semantic reasoning was utilized to establish the relationship between data orders and pathway orders. RESULTS: A clinical pathway for deviated nasal septum was used as an example to illustrate how to combine standard care plans and practical treatment procedures. A group of 224 patients with 11,473 orders was transformed to a semantic data model, which was stored in RDF format. Long term order processing and error correction made the treatment procedures more consistent with clinical practice. The percentage of each pathway order with different probabilities was calculated to declare the commonality between the standard care plans and practical treatment procedures. Detailed treatment procedures with pathway orders, deduced pathway orders, and orders with probability greater than 80% were provided to efficiently customize the CPs. CONCLUSIONS: This study contributes to the practical application of pathway specifications recommended by the Ministry of Health of China and provides a generic framework for the hospital-specific customization of standard care plans defined by CPs or clinical guidelines.

Use of an agent-based simulation model to evaluate a mobile-based system for supporting emergency evacuation decision making.

Recently, mass casualty incidents (MCIs) have been occurring frequently and have gained international attention. There is an urgent need for scientifically proven and effective emergency responses to MCIs, particularly as the severity of incidents is continuously increasing. The emergency response to MCIs is a multi-dimensional and multi-participant dynamic process that changes in real-time. The evacuation decisions that assign casualties to different hospitals in a region are very important and impact both the results of emergency treatment and the efficiency of medical resource utilization. Previously, decisions related to casualty evacuation were made by an incident commander with emergency experience and in accordance with macro emergency guidelines. There are few decision-supporting tools available to reduce the difficulty and psychological pressure associated with the evacuation decisions an incident commander must make. In this study, we have designed a mobile-based system to collect medical and temporal data produced during an emergency response to an MCI. Using this information, our system's decision-making model can provide personal evacuation suggestions that improve the overall outcome of an emergency response. The effectiveness of our system in reducing overall mortality has been validated by an agent-based simulation model established to simulate an emergency response to an MCI.

Design and development of a mobile-based system for supporting emergency triage decision making.

Emergency care for mass casualty incidents is a sophisticated multi-participant process. To manage this process effectively, many information systems have been proposed. However, their performance in improving the efficiency and accuracy of patient triage is not satisfactory. This paper is concerned with the development of a mobile-based system for supporting emergency triage in the emergency care process for mass casualty incidents. This system collects the patient's emergency data throughout the whole emergency care process through a mobile application and data transfer mechanism. Using a Cox proportional hazard model, the system has the capacity to present the survival curve to the triage officer, helping him/her to make triage and transportation decisions. This system offers an alternative injury assessment tool based on the vital signs data of the injury patient. With the help of this system, the triage officer can more directly and comprehensively learn about each patient's situation and deterioration without additional operations at the incident site.

[Forebrain NMDA receptor 2B subunit over-expression has no influence on anxiety behaviors of mice.].

It has been known that the glutamate transmission system and N-methyl-D-aspartate receptor (NMDA-R) were possibly related to anxiety processes. Although anxiety symptom can be relieved by NMDA-R antagonists and partial agonists treatment, the functions of NMDA-R and its subunits in anxiety behaviors remain unclear. We used forebrain specific NR2B over-expression mice to examine whether the increase of NR2B subunit level would induce anxiety behaviors. The results indicated that the juvenile (3-5 months old), middle-aged (8-10 months old) and old (19-22 months old ) NR2B transgenic mice showed no significant difference in open field test and elevated plus maze test as compared with the control mice. Capillary electrophoresis of monoamine neurotransmitter in subregions of forebrain revealed no significant difference between transgenic and control mice of 16-18 months age. These results suggest that the increase of NR2B expression and followed NR1 and NR2A expression augmentations in the forebrain have no significant effect on anxiety-related behaviors in mice.

An Automatic User-Adapted Physical Activity Classification Method Using Smartphones.

In recent years, an increasing number of people have become concerned about their health. Most chronic diseases are related to lifestyle, and daily activity records can be used as an important indicator of health. Specifically, using advanced technology to automatically monitor actual activities can effectively prevent and manage chronic diseases. The data used in this paper were obtained from acceleration sensors and gyroscopes integrated in smartphones. We designed an efficient Adaboost-Stump running on a smartphone to classify five common activities: cycling, running, sitting, standing, and walking and achieved a satisfactory classification accuracy of 98%. We designed an online learning method, and the classification model requires continuous training with actual data. The parameters in the model then become increasingly fitted to the specific user, which allows the classification accuracy to reach 95% under different use environments. In addition, this paper also utilized the OpenCL framework to design the program in parallel. This process can enhance the computing efficiency approximately ninefold.

High-Performance Personalized Heartbeat Classification Model for Long-Term ECG Signal.

Long-term electrocardiogram (ECG) has become one of the important diagnostic assist methods in clinical cardiovascular domain. Long-term ECG is primarily used for the detection of various cardiovascular diseases that are caused by various cardiac arrhythmia such as myocardial infarction, cardiomyopathy, and myocarditis. In the past few years, the development of an automatic heartbeat classification method has been a challenge. With the accumulation of medical data, personalized heartbeat classification of a patient has become possible. For the long-term data accumulation method, such as the holter, it is difficult to obtain the analysis results in a short time using the original method of serial design. The pressure to develop a personalized automatic classification model is high. To solve these challenges, this paper implemented a parallel general regression neural network (GRNN) to classify the heartbeat, and achieved a 95% accuracy according to the Association for the Advancement of Medical Instrumentation. We designed an online learning program to form a personalized classification model for patients. The achieved accuracy of the model is 88% compared to the specific ECG data of the patients. The efficiency of the parallel GRNN with GTX780Ti can improve by 450 times.

Integrating HL7 RIM and ontology for unified knowledge and data representation in clinical decision support systems.

BACKGROUND AND OBJECTIVES: The broad adoption of clinical decision support systems within clinical practice has been hampered mainly by the difficulty in expressing domain knowledge and patient data in a unified formalism. This paper presents a semantic-based approach to the unified representation of healthcare domain knowledge and patient data for practical clinical decision making applications. METHODS: A four-phase knowledge engineering cycle is implemented to develop a semantic healthcare knowledge base based on an HL7 reference information model, including an ontology to model domain knowledge and patient data and an expression repository to encode clinical decision making rules and queries. A semantic clinical decision support system is designed to provide patient-specific healthcare recommendations based on the knowledge base and patient data. RESULTS: The proposed solution is evaluated in the case study of type 2 diabetes mellitus inpatient management. The knowledge base is successfully instantiated with relevant domain knowledge and testing patient data. Ontology-level evaluation confirms model validity. Application-level evaluation of diagnostic accuracy reaches a sensitivity of 97.5%, a specificity of 100%, and a precision of 98%; an acceptance rate of 97.3% is given by domain experts for the recommended care plan orders. CONCLUSIONS: The proposed solution has been successfully validated in the case study as providing clinical decision support at a high accuracy and acceptance rate. The evaluation results demonstrate the technical feasibility and application prospect of our approach.

Ontology-based clinical pathways with semantic rules.

Clinical Pathways (CP) enhance the quality of patient care, and are thus important in health management. However, there is a need to address the challenge of adaptation of treatment procedures in CP-that is, the treatment schemes must be re-modified once the clinical status and other care conditions of patients in the healthcare setting change, which happen frequently. In addition, the widespread and frequent use of Electronic Medical Records (EMR) implies an increasing need to combine CP with other healthcare information systems, especially EMR, in order to greatly improve healthcare quality and efficiency. This study proposed an ontology-based method to model CP: ontology was used to model CP domain terms; Semantic Web Rule language was used to model domain rules. In this way, the CP could reason over the rules, knowledge, and information collected, and provides automated error checking for the next steps of the treatment in runtime, which is adaptive to treatment procedures. To evaluate our method, we built a Lobectomia Pulmonalis CP and realized it based on an EMR system.

Design and development of an international clinical data exchange system: the international layer function of the Dolphin Project.

OBJECTIVE: At present, most clinical data are exchanged between organizations within a regional system. However, people traveling abroad may need to visit a hospital, which would make international exchange of clinical data very useful. BACKGROUND: Since 2007, a collaborative effort to achieve clinical data sharing has been carried out at Zhejiang University in China and Kyoto University and Miyazaki University in Japan; each is running a regional clinical information center. Methods An international layer system named Global Dolphin was constructed with several key services, sharing patients' health information between countries using a medical markup language (MML). The system was piloted with 39 test patients. RESULTS: The three regions above have records for 966,000 unique patients, which are available through Global Dolphin. Data exchanged successfully from Japan to China for the 39 study patients include 1001 MML files and 152 images. The MML files contained 197 free text-type paragraphs that needed human translation. Discussion The pilot test in Global Dolphin demonstrates that patient information can be shared across countries through international health data exchange. To achieve cross-border sharing of clinical data, some key issues had to be addressed: establishment of a super directory service across countries; data transformation; and unique one-language translation. Privacy protection was also taken into account. The system is now ready for live use. CONCLUSION: The project demonstrates a means of achieving worldwide accessibility of medical data, by which the integrity and continuity of patients' health information can be maintained.

Relationship between different sources of drinking water, water quality improvement and gastric cancer mortality in Changle County-A retrospective-cohort study in high incidence area.

AIM:To investigate the relationship between different sources of drinking water supply, water quality improvement and gastric cancer mortality rate in a high risk area.METHODS: A retrospective-cohort survey was carried out in all towns of this county to study the effect of different sources of drinking water supply and water quality improvement on gastric cancer mortality rate.RESULTS: The gastric cancer mortality rate among the population 124.05/10(5) drinking river water was obviously higher than that of drinking shallow well water (74.85/10(5)) (P < 0.01) according to the Zhanggang Town 16 years accumulated data. The same pattern was presented in 7 towns after balancing the confounders. The gastric cancer mortality rate of population drinking river water was 86.03/10(5), which was higher than those drinking shallow well water (62.03/10(5)) and tap water (29.78/10(5))(P < 0.01). When the drinking water switched from river and well water to tap water, the gastric cancer incidence decreased to 30.33/10(5) and 26.10/10(5), and the gastric cancer mortality decreased by 59% and 57% respectively.CONCLUSION: The quality of drinking water is one of the important factors of increased incidence of gastric cancer in Changle County, and water quality improvement has a beneficial effect, but the cause of high gastric cancer incidence may be multi-factorial in this area.

Diet and gastric cancer: a casecontrol study in Fujian Province, China.

AIM:To explore the relationship between consumption of fish sauce, other dietary factors, living habits and the rish kf gastric cancer.METHODS:From May 1994 to July 1995, a population-based 1 2 case-control study was in Carried out inhigh-risk areas of gastric cancer, Changle and Fuqing cities, Fujian Province. Totally 272 cases and 544 age, gender-matched controls were included. Risk state analyses were made by ASRS package.RESULTS:Risk state single-factor analysis indicated that gastric cancer risk rose with high intake of fish sauce(OR =2.57), salted vegetables (OR =1.41), salted/fried fish and small shrimps (OR =1.57), low consumption of fresh vegetables (OR =1.95), fresh citrus fruits (OR =1.41), other fresh fruits (OR =1.31), green tea (OR =1.72), exposure to moldy foods (OR =2.32), irregular dinners (OR =5.47) and familial history of malignancy (OR =3.27).No significant relationship was observed between smoking, drinking, salt intake, use of refrigerator and gastric cancer rish. The results of rish state conditional Logistic regression showed that fish sauce, salted/dried fish and small shrimps, irregular dinners, familial history of malignancy were included in the best rish set. The summary ARS for the four factors was 75.49%.CONCLUSION:High intake of fish sauce, salted foods, moldy foods, irregular dinners and familial history of malignancy were possible risk factors for gastric cancer, whereas fresh vegetables and fruits.and green tea might have protective effects for gastric cancer.