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.

Development and practice of a Telehealthcare Expert System (TES).

OBJECTIVE: Expert systems have been widely used in medical and healthcare practice for various purposes. In addition to vital sign data, important concerns in telehealthcare include the compliance with the measurement prescription, the accuracy of vital sign measurements, and the functioning of vital sign meters and home gateways. However, few expert system applications are found in the telehealthcare domain to address these issues. MATERIALS AND METHODS: This article presents an expert system application for one of the largest commercialized telehealthcare practices in Taiwan by Min-Sheng General Hospital. The main function of the Telehealthcare Expert System (TES) developed in this research is to detect and classify events based on the measurement data transmitted to the database at the call center, including abnormality of vital signs, violation of vital sign measurement prescriptions, and malfunction of hardware devices (home gateway and vital sign meter). When the expert system detects an abnormal event, it assigns an "urgent degree" and alerts the nursing team in the call center to take action, such as phoning the patient for counseling or to urge the patient to return to the hospital for further tests. RESULTS: During 2 years of clinical practice, from 2009 to 2011, 19,182 patients were served by the expert system. The expert system detected 41,755 events, of which 22.9% indicated abnormality of vital signs, 75.2% indicated violation of measurement prescription, and 1.9% indicated malfunction of devices. On average, the expert system reduced by 76.5% the time that the nursing team in the call center spent in handling the events. CONCLUSIONS: The expert system helped to reduce cost and improve quality of the telehealthcare service.

Using n-gram analysis to cluster heartbeat signals.

BACKGROUND: Biological signals may carry specific characteristics that reflect basic dynamics of the body. In particular, heart beat signals carry specific signatures that are related to human physiologic mechanisms. In recent years, many researchers have shown that representations which used non-linear symbolic sequences can often reveal much hidden dynamic information. This kind of symbolization proved to be useful for predicting life-threatening cardiac diseases. METHODS: This paper presents an improved method called the "Adaptive Interbeat Interval Analysis (AIIA) method". The AIIA method uses the Simple K-Means algorithm for symbolization, which offers a new way to represent subtle variations between two interbeat intervals without human intervention. After symbolization, it uses the n-gram algorithm to generate different kinds of symbolic sequences. Each symbolic sequence stands for a variation phase. Finally, the symbolic sequences are categorized by classic classifiers. RESULTS: In the experiments presented in this paper, AIIA method achieved 91% (3-gram, 26 clusters) accuracy in successfully classifying between the patients with Atrial Fibrillation (AF), Congestive Heart Failure (CHF) and healthy people. It also achieved 87% (3-gram, 26 clusters) accuracy in classifying the patients with apnea. CONCLUSIONS: The two experiments presented in this paper demonstrate that AIIA method can categorize different heart diseases. Both experiments acquired the best category results when using the Bayesian Network. For future work, the concept of the AIIA method can be extended to the categorization of other physiological signals. More features can be added to improve the accuracy.

Drosophila eugracilis - Akt.

Gene Model for Akt in the D. eugracilis (DeugGB2) assembly (GCA_000236325.2).

[To explore the mechanisms of neurogenic inflammation and airway hyperresponsiveness of rat by inhaled sulfur].

AIM: To explore the physiopathological mechanisms of airway injury and the effect on the airway responsiveness of rat by inhaled sulfur dioxide(SO2). METHODS: Sixteen SD male rats were divided randomly into 2 groups (n = 8): the control group and SO2 group. The control group was exposed o pure air. SO2 group was exposed to SO2 of the content 1.0 mg/(m(3) x h) 6h daily for consecutive 3 d. At 4th day, we determined the airway responsiveness, collected the bronchoalveolar lavage fluid (BALF), plasma and lung tissue. Then we counted the total cellular score in BALF, measured the plasma SP content and made the immunohistochemistry staining on the lung tissue (HE and SP methods). RESULTS: Compared with the control group, the total cellular score in BALF and plasma SP content in SO2 group's increased significantly ( P < 0.01). HE staining showed there were a great deal of inflammatory cells infiltration under the tunica mucosa bronchiorum; and SP immunohistochemistry staining indicated there were significant changes in numbers of SP-IR positive fibers of SO2group. CONCLUSION: Exposure to low concentration of SO2 would injure healthy rat's airway, and induce airway hyperresponsiveness, neurogenic inflammation is one of its critical pathophysiological mechanisms.

[Respiratory syncytial virus infection enhances airway hyperresponsiveness in guinea pigs and the underlined mechanism].

AIM: To study the relation between Respiratory Syncytial Virus infection and asthma development by measuring airway responsiveness (AR) and M2R function. METHODS: Guinea pigs (n = 34) were randomly divided into 4 groups: Hep-2/NS group (group A, n = 9), RSV/NS group (group B, n =9), Hep-2/OVA group (group C, n = 8) and RSV/OVA group(group D, n = 8). On day 21 after infection we tested AR and M2R. Then counted eosinophils in BALF and observed pathological change. RESULTS: Intraairway pressure(IP mmH20) of group B had no significant difference with group A(P > 0.01), and the extent of IP decrease also had no difference between groups A and B (P > 0. 05), but IP of C group were much higher than group A (P<0.05), with extent of IP decrease lower than group A (P < 0.05). And IP of group D were higher than group C (P < 0.01), with the extent of IP decrease much lower than group C (P < 0.05). CONCLUSION: RSV infection could enhance OVA-induced M2R dysfunction, then develop AHR.