[Summary of research in economic burden of hand, foot, and mouth disease in China].

Since 2010, the incidence of hand, foot, and mouth disease (HFMD) has ranked top in notifiable infectious disease in China, causing economic losses to many families and the society of China. This paper summarizes the related methods, results and problems systematically in the research of economic burden of HFMD in China to provide reference for the better estimation of the economic burden caused by HFMD. Many studies showed that HFMD, especially severe and fatal cases, had posed heavy economic burden on the society. To mitigate the burden caused by HFMD, it is necessary to decrease the risk of severe and fatal cases, as well as to reduce the incidence of mild cases.

Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Hemodynamic Management in Sepsis Patients.

The potential of Reinforcement Learning (RL) has been demonstrated through successful applications to games such as Go and Atari. However, while it is straightforward to evaluate the performance of an RL algorithm in a game setting by simply using it to play the game, evaluation is a major challenge in clinical settings where it could be unsafe to follow RL policies in practice. Thus, understanding sensitivity of RL policies to the host of decisions made during implementation is an important step toward building the type of trust in RL required for eventual clinical uptake. In this work, we perform a sensitivity analysis on a state-of-the-art RL algorithm (Dueling Double Deep Q-Networks) applied to hemodynamic stabilization treatment strategies for septic patients in the ICU. We consider sensitivity of learned policies to input features, embedding model architecture, time discretization, reward function, and random seeds. We find that varying these settings can significantly impact learned policies, which suggests a need for caution when interpreting RL agent output.

What information and the extent of information research participants need in informed consent forms: a multi-country survey.

BACKGROUND: The use of lengthy, detailed, and complex informed consent forms (ICFs) is of paramount concern in biomedical research as it may not truly promote the rights and interests of research participants. The extent of information in ICFs has been the subject of debates for decades; however, no clear guidance is given. Thus, the objective of this study was to determine the perspectives of research participants about the type and extent of information they need when they are invited to participate in biomedical research. METHODS: This multi-center, cross-sectional, descriptive survey was conducted at 54 study sites in seven Asia-Pacific countries. A modified Likert-scale questionnaire was used to determine the importance of each element in the ICF among research participants of a biomedical study, with an anchored rating scale from 1 (not important) to 5 (very important). RESULTS: Of the 2484 questionnaires distributed, 2113 (85.1%) were returned. The majority of respondents considered most elements required in the ICF to be 'moderately important' to 'very important' for their decision making (mean score, ranging from 3.58 to 4.47). Major foreseeable risk, direct benefit, and common adverse effects of the intervention were considered to be of most concerned elements in the ICF (mean score = 4.47, 4.47, and 4.45, respectively). CONCLUSIONS: Research participants would like to be informed of the ICF elements required by ethical guidelines and regulations; however, the importance of each element varied, e.g., risk and benefit associated with research participants were considered to be more important than the general nature or technical details of research. Using a participant-oriented approach by providing more details of the participant-interested elements while avoiding unnecessarily lengthy details of other less important elements would enhance the quality of the ICF.

A physiological time series dynamics-based approach to patient monitoring and outcome prediction.

Cardiovascular variables such as heart rate (HR) and blood pressure (BP) are regulated by an underlying control system, and therefore, the time series of these vital signs exhibit rich dynamical patterns of interaction in response to external perturbations (e.g., drug administration), as well as pathological states (e.g., onset of sepsis and hypotension). A question of interest is whether "similar" dynamical patterns can be identified across a heterogeneous patient cohort, and be used for prognosis of patients' health and progress. In this paper, we used a switching vector autoregressive framework to systematically learn and identify a collection of vital sign time series dynamics, which are possibly recurrent within the same patient and may be shared across the entire cohort. We show that these dynamical behaviors can be used to characterize the physiological "state" of a patient. We validate our technique using simulated time series of the cardiovascular system, and human recordings of HR and BP time series from an orthostatic stress study with known postural states. Using the HR and BP dynamics of an intensive care unit (ICU) cohort of over 450 patients from the MIMIC II database, we demonstrate that the discovered cardiovascular dynamics are significantly associated with hospital mortality (dynamic modes 3 and 9, p=0.001, p=0.006 from logistic regression after adjusting for the APACHE scores). Combining the dynamics of BP time series and SAPS-I or APACHE-III provided a more accurate assessment of patient survival/mortality in the hospital than using SAPS-I and APACHE-III alone (p=0.005 and p=0.045). Our results suggest that the discovered dynamics of vital sign time series may contain additional prognostic value beyond that of the baseline acuity measures, and can potentially be used as an independent predictor of outcomes in the ICU.

Learning outcome-discriminative dynamics in multivariate physiological cohort time series.

Model identification for physiological systems is complicated by changes between operating regimes and measurement artifacts. We present a solution to these problems by assuming that a cohort of physiological time series is generated by switching among a finite collection of physiologically-constrained dynamical models and artifactual segments. We model the resulting time series using the switching linear dynamical systems (SLDS) framework, and present a novel learning algorithm for the class of SLDS, with the objective of identifying time series dynamics that are predictive of physiological regimes or outcomes of interest. We present exploratory results based on a simulation study and a physiological classification example of decoding postural changes from heart rate and blood pressure. We demonstrate a significant improvement in classification over methods based on feature learning via expectation maximization. The proposed learning algorithm is general, and can be extended to other applications involving state-space formulations.

Reducing computational costs in large scale 3D EIT by using a sparse Jacobian matrix with block-wise CGLS reconstruction.

Electrical impedance tomography (EIT) is a fast and cost-effective technique to provide a tomographic conductivity image of a subject from boundary current-voltage data. This paper proposes a time and memory efficient method for solving a large scale 3D EIT inverse problem using a parallel conjugate gradient (CG) algorithm. The 3D EIT system with a large number of measurement data can produce a large size of Jacobian matrix; this could cause difficulties in computer storage and the inversion process. One of challenges in 3D EIT is to decrease the reconstruction time and memory usage, at the same time retaining the image quality. Firstly, a sparse matrix reduction technique is proposed using thresholding to set very small values of the Jacobian matrix to zero. By adjusting the Jacobian matrix into a sparse format, the element with zeros would be eliminated, which results in a saving of memory requirement. Secondly, a block-wise CG method for parallel reconstruction has been developed. The proposed method has been tested using simulated data as well as experimental test samples. Sparse Jacobian with a block-wise CG enables the large scale EIT problem to be solved efficiently. Image quality measures are presented to quantify the effect of sparse matrix reduction in reconstruction results.

Automated de-identification of free-text medical records.

BACKGROUND: Text-based patient medical records are a vital resource in medical research. In order to preserve patient confidentiality, however, the U.S. Health Insurance Portability and Accountability Act (HIPAA) requires that protected health information (PHI) be removed from medical records before they can be disseminated. Manual de-identification of large medical record databases is prohibitively expensive, time-consuming and prone to error, necessitating automatic methods for large-scale, automated de-identification. METHODS: We describe an automated Perl-based de-identification software package that is generally usable on most free-text medical records, e.g., nursing notes, discharge summaries, X-ray reports, etc. The software uses lexical look-up tables, regular expressions, and simple heuristics to locate both HIPAA PHI, and an extended PHI set that includes doctors' names and years of dates. To develop the de-identification approach, we assembled a gold standard corpus of re-identified nursing notes with real PHI replaced by realistic surrogate information. This corpus consists of 2,434 nursing notes containing 334,000 words and a total of 1,779 instances of PHI taken from 163 randomly selected patient records. This gold standard corpus was used to refine the algorithm and measure its sensitivity. To test the algorithm on data not used in its development, we constructed a second test corpus of 1,836 nursing notes containing 296,400 words. The algorithm's false negative rate was evaluated using this test corpus. RESULTS: Performance evaluation of the de-identification software on the development corpus yielded an overall recall of 0.967, precision value of 0.749, and fallout value of approximately 0.002. On the test corpus, a total of 90 instances of false negatives were found, or 27 per 100,000 word count, with an estimated recall of 0.943. Only one full date and one age over 89 were missed. No patient names were missed in either corpus. CONCLUSION: We have developed a pattern-matching de-identification system based on dictionary look-ups, regular expressions, and heuristics. Evaluation based on two different sets of nursing notes collected from a U.S. hospital suggests that, in terms of recall, the software out-performs a single human de-identifier (0.81) and performs at least as well as a consensus of two human de-identifiers (0.94). The system is currently tuned to de-identify PHI in nursing notes and discharge summaries but is sufficiently generalized and can be customized to handle text files of any format. Although the accuracy of the algorithm is high, it is probably insufficient to be used to publicly disseminate medical data. The open-source de-identification software and the gold standard re-identified corpus of medical records have therefore been made available to researchers via the PhysioNet website to encourage improvements in the algorithm.

Poverty relief and development by way of out-immigration: new opportunities for women's participation in development.

PIP: This article discusses patterns of female migration out of Gansu province in China and the causes of women's problems in migration. China is initiating a relocation project for moving 200,000 people from poverty areas in central south Gansu province to the Shule River Basin in Jiuquan Prefecture of Gansu. The study provides findings from a migrant survey. Destination and origin areas differed in educational attainment. Occupations varied by gender. The ratio of men to women in all salaried occupations varied between origin and destination areas. 96.41% in the origin areas and 55.31% in the destination areas were women farmers. During 1985-90, 50,902 persons moved to destination areas, of which 24,181 (47.51%) were female. Women's movements were related to marriage and family reunification. Men migrated due to job transfers or employment and business opportunities. About 610,000 people were interested in migrating to the Shule River Valley. 46.67% of female migrants in the destination area indicated that they had no say in decision making concerning the move; in the origin areas only 32.02% had no say. Female migrants in the destination area arrived 3-9 years ago. Women in the destination area had more skills than women in origin areas. "Finding a way out" was the major reason for migration in both destination and origin areas. Origin areas had more migrants who moved due to landlessness. 26.67% of women returned for visits to the origin areas. Few men or women participated in premigration programs; but, following migration, 63% of women and 86% of men were attracted to education programs. Most desired technical programs. Many women suffered from low educational status, low employment, premature marriage, and early childbearing. These problems were due to a backward economy, traditional values, women's personal characteristics, excessive childbearing, reforms, and the market economy.^ieng

Clinical significance of multiple hypothalamic-pituitary functions assessment in patients with Turner's syndrome.

Hypothalamic-pituitary functions in 26 cases of Turner syndrome were assessed with a combined stimulation test. The results showed that the peak GH levels of 12 cases were less than 10 micrograms/L; 3 patients were demonstrated as having an even TSH response, while another one with a delayed TSH peak, and other 4 had high basal values and consistent exaggerated TSH responses to TRH; all patients showed increased basal and peak LH and FSH levels but 5, whose LH and FSH secretion patterns were similar to normal. 12 cases have been treated with individualized protocols and followed up for 12 months or more, of them the growth velocity all increased, especially those with hypothyroidism or with a BA less than 13. It is suggested that multiple functions of hypothalamic-pituitary axis in Turner patients be evaluated as early as possible, in order that proper treatment could be adopted and their growth and development improved.

Hypothalamic-pituitary function assessment in children by a combined stimulation test.

We utilized a combined stimulation test using insulin, thyrotropin-releasing hormone, gonadotropin-releasing hormone and levodopa to assess multiple pituitary hormones including growth hormone, thyrotropin, prolactin and gonadotropins in 32 children of short stature and 18 girls with early appearance of puberty. It was found that this combined stimulation test can assess multiple hormone responses with satisfactory results in a single 90-min test. Compared with any of those laborious classic stimulation tests alone, it is easier to be carried out and willingly accepted by children.

Marriage practices in contemporary China: a comparative study of urban and rural marriages in Taiwan.

PIP: Results are presented from a field study carried out in 1978 to examine differences in marriage practices between rural and urban families in northern Taiwan. The data concern 1,228 couples and include information on attitudes regarding mate selection, parents' attitudes toward children's marriage, attitudes toward marriage in the family context, and age at first marriage. The continuation of more traditional attitudes toward marriage in rural areas is noted. The age at marriage is higher in urban areas, and a relationship between higher educational level and later marriage is observed.^ieng