In this paper, we provide a real nursing data set for mobile activity recognition that can be used for supervised machine learning, and big data combined the patient medical records and sensors attempted for 2 years, and also propose a method for recognizing activities for a whole day utilizing prior knowledge about the activity segments in a day. Furthermore, we demonstrate data mining by applying our method to the bigger data with additional hospital data. In the proposed method, we 1) convert a set of segment timestamps into a prior probability of the activity segment by exploiting the concept of importance sampling, 2) obtain the likelihood of traditional recognition methods for each local time window within the segment range, and, 3) apply Bayesian estimation by marginalizing the conditional probability of estimating the activities for the segment samples. By evaluating with the dataset, the proposed method outperformed the traditional method without using the prior knowledge by 25.81% at maximum by balanced classification rate. Moreover, the proposed method significantly reduces duration errors of activity segments from 324.2 seconds of the traditional method to 74.6 seconds at maximum. We also demonstrate the data mining by applying our method to bigger data in a hospital.

Non-communicable diseases (NCDs) are no longer just a problem for high-income countries, but they are also a problem that affects developing countries. Preventive medicine is definitely the key to combat NCDs; however, the cost of preventive programs is a critical issue affecting the popularization of these medicine programs in developing countries. In this study, we investigate predictive modeling for providing a low-cost preventive medicine program. In our two-year-long field study in Bangladesh, we collected the health checkup results of 15,075 subjects, the data of 6,607 prescriptions, and the follow-up examination results of 2,109 subjects. We address three prediction problems, namely subject risk prediction, drug recommendation, and future risk prediction, by using machine learning techniques; our multiple-classifier approach successfully reduced the costs of health checkups, a multi-task learning method provided accurate recommendation for specific types of drugs, and an active learning method achieved an efficient assignment of healthcare workers for the follow-up care of subjects.

The secondary use of medical data to improve medical care is gaining much attention. We have analyzed electronic clinical pathways for improving the medical process. The analysis of clinical pathways so far has used statistics analysis models, however as issue remains that the order, and multistory spatial and time relations of the each factor could not be analyzed. We constructed an Outcome tree system that shows the greatest significant relation for each factor. The Hip replacement arthroplasty clinical pathway was analyzed by the system, and the outcome variance of the clinical pathway was visualized. The results indicate the path of patient's who have a long hospitalization stay and extracted four critical indicators.

We present a remote healthcare consultancy system that enables healthcare workers to identify noncommunicable diseases in unreached communities. The healthcare system combines medical sensors with mobile health and is called a Portable Health Clinic. The Portable Health Clinic fits into a briefcase and is operated by the healthcare worker. The goal of this research is to empower the healthcare worker further by allowing her to recognize spurious measurements and to make lifestyle recommendations. In this paper, we show how to process the data: combine, link and compare - captured in patient electronic health records stored in database. We applied association rule technique to find common set of rules in order to build a clinical decision support system. We also showed examples of the meaningful information from the analyzed data to build a better clinical decision support.

Blood Pressure readings are widely accepted as a measure to determine the risk of non-communicable diseases such as hypertension and stroke. Affordable healthcare devices and sensors allow individuals to monitor blood pressure at home or at a local service point on a daily basis. ICT based healthcare systems interpret the readings and give feedback to individuals or may trigger a telemedicine call to a remote doctor. This paper introduces case studies for ICT healthcare studies undertaken in Japan and Bangladesh. Blood Pressure data collected by the Omron WellnessLink (500,000 readings) and the Kyushu University/Grameen Portable Heath Clinic (21,252 readings) are examined for similarities and differences. The results show similarities in gender and temporal influences. Males have higher blood pressure and readings appear to be rhythmic according to day and month. The differences indicate that the mean Systolic Blood Pressure (SBP) for Japanese males is higher than Bangladesh males and SBP for Bangladesh females is higher than Japanese females. The impact of climate is stronger on Japanese SBP than Bangladesh SBP. The Bangladesh data shows progressive increase in SBP in each ten year age category until 80 years; this is also reflected by BMI categories. The study reveals that affordable devices connected to basic ICT based healthcare systems reveal underlying factors in the Blood Pressure variability.

Remote surgery is one of the most desired applications in the context of recent advanced medical technologies. For a future expansion of remote surgery, it is important to use conventional network infrastructures such as Internet. However, using such conventional network infrastructures, we are confronting time-delay problems of data transmission. In this paper, a remote surgery experiment between Japan and Thailand using a research and development Internet is presented. In the experiment, the image and audio information was transmitted by a newly developed low latency CODEC system to shorten the time-delay. By introducing the low latency CODEC system, the time-delay was shortened compared with the past remote surgery experiments despite the longer distance. We also conducted several network measurements such as a comparison between TCP/IP and UDP/IP about the control signal transmission.

Several robotic surgical systems have been developed for MIS (minimally invasive surgery) including commercialized products such as da Vinci and ZEUS. We have developed a minimally invasive surgical system, which have carried out remote surgery experiments for five times at the writing time. In this paper, a remote surgery experiment, which was conducted between Japan and Korea by using the developed minimally invasive surgical system is described. Research & Development (R & D) Internet testbet, APII (Asia-Pacific Information Infrastructure), which consists of an optical submarine cable network KJCN (Korea-Japan Cable Network), was used. In the experiment, a laparoscopic cholecystectomy was successfully carried out on a pig. The network time-delays of control signal and images were 6.5 msec and 435.5 msec respectively. A comparison of remote surgery experiments using ISDN and the Internet was studied

We have established a medical network in Asia-Pacific area with high-quality moving image on a super broad-banded Internet line since February 2003. Broadcasting real-time surgery and teleconference with medical-quality videos with digital video transport system (DVTS) were so useful to learn surgical techniques and other medical procedures beyond borders. We have conducted 20 events in categories of teleconference (8 times), live transmission of medical procedure (6 times), and remote attendance to scientific medical meetings (6 times) until September 2004. We had teleconferences among 3 countries in APAN-Hawaii in January 2004 (Japan, Korea and USA), and in APAN-Cairns in July 2004 (Japan, Korea and Australia). There have been no serious transmission/ethical problems so far. With these experiences and current achievements, we hope to extend this advanced network system to whole Asia-Pacific countries to share medical skills and knowledge.