Chih-Ming Cheng

Development of a portable device for telemonitoring of snoring and obstructive sleep apnea syndrome symptoms.

The First Intensive Balkan Telemedicine and e-Health Seminar was held in the war-ravaged capital of Kosova, Prishtina, in 2002. This event was created under the auspices of the International Virtual e Hospital (IVeH). This organization, the brain child of Rifat Latifi, M.D., was established with the intent to establish a medical capability using telemedicine in an environment where the health infrastructure had been totally destroyed by war. Recently, the IVeH opened six regional telemedicine centers in Kosova. These centers are in the cities of Gjilan, Prizren, Gjakove, Peja, Skenderaj, and Mitrovica. These centers cover the entire country through a telemedicine network. Recently, the Second Intensive Seminar was organized and held October 2123, 2007, in Tirana, Albania. It was organized to broaden the concept of telemedicine and e-health in the Balkans region, and to introduce telemedicine in Albania, which was an ideal choice for holding the seminar. The seminar represented a significant step for Albania as it embraces the concept of telemedicine. These important events have shaped telemedicine development in the Balkans and are serving as a model for the rest of the South Eastern European countries to embrace telemedicine and e-health. This paper summarizes the events of this second seminar and addresses the importance telemedicine has for the region.

Development of a portable device for home monitoring of snoring

Snoring analysis is important for the diagnosis and treatment of sleep-related breathing disorders (SRBD). Snoring has traditionally been assessed in clinical practice from subjective accounts by the snorer and his/her partner. The use of polysomnographic recording is a standard evaluation procedure for SRBD patients. However, it is expensive and is not suitable for long term monitoring. This paper describes the development of a portable microcontroller based device for long-term, home monitoring of snoring. By analyzing the temporal feature of the snoring sound, this device can output the total snoring count, average number of snores per hour, and the number of intermittent snoring. In our tests, the average success rate in identifying snores is over 85% in a lab environment and around 70% in a home.

Development of a Telehealthcare Decision Support System for Patients Discharged from the Hospital

OBJECTIVE This article presents the development of a telehealthcare decision support system (TDSS) for patients discharged from the hospital, where symptom data are important indications of the recovery progress for patients. Symptom data are difficult to quantify in a telehealthcare application scenario because the observations and perceptions on symptoms by the patient themselves are subjective. In the TDSS, both symptom data from patients and clinical histories from the hospital information system are collected. Machine learning algorithms are used to build a predictive model for classifying patients according to their symptom data and clinical histories, to provide a degree of urgency for the patient to return to the hospital. MATERIALS AND METHODS During a 1-year period, 1,467 patient cases were collected. Symptom data and clinical histories were preprocessed into 49 parameters for machine learning. The training data of patients were validated manually with their actual clinical histories of returning to the hospital. The performances of predictive models trained by five different machine learning algorithms were evaluated and compared. RESULTS The Bayesian network algorithm had the best performance among the machine learning algorithms tested in this application scenario and was selected to be implemented in the TDSS. On the 1,467 patient cases collected, its precision in 10-fold cross-validation was 79.3%. The most important six parameters were also selected from the 49 parameters by feature selection. The performance of correct prediction by the TDSS is comparable to that by the nursing team at the call center. CONCLUSIONS The TDSS provides a degree of urgency for patients to return to the hospital and thereby assists the telehealthcare nursing team in making such decisions. The performance of the TDSS is expected to improve as more cases of patient data are collected and input into the TDSS. The TDSS has been implemented in one of the largest commercialized telehealthcare practices in Taiwan administered by Min-Sheng General Hospital.