Medical Data Files Research Articles

Very long-term outcomes of pediatric patients treated for optic pathway gliomas: A longitudinal cohort study.

Optic pathway gliomas (OPGs) represent 5% of childhood brain tumors. Successive relapses lead to multiple treatments exposing to late complications. We included patients treated at Gustave Roussy (GR) between January 1980 and December 2015 for OPG, before 18 years old and alive at 5 years from diagnosis. Mortality and physical health conditions data were extracted from medical data files and updated, thanks to the GR long-term follow-up program and French national mortality registry for patients included in the French Childhood Cancer Survivor Study. We included 182 5-year OPG-childhood survivors in the analysis (sex ratio M/F 0.8, 35% with neurofibromatosis type 1 [NF1]). With a median follow-up of 17.2 years (range = 5-41), we registered 82 relapses, 9 second malignancies, and 15 deaths as first events after 5 years, resulting in 20-year conditional overall survival (C-OS) and late events-free survival of 79.9% (95% confidence interval [CI] = 71-86) and 43.5% (95% CI = 36-51), respectively. Radiotherapy exposure in NF1 patients (hazard ratio [HR] = 6, 95% CI = 1.7-21.2) and hypothalamic involvement (HR = 3.2, 95% CI = 1.4-7.3) were significantly associated with C-OS in multivariable analyses. Ninety-five percent of 5-year OPG survivors suffered from any health condition, especially visual acuity "<1/10" (n = 109), pituitary deficiency (n = 106), and neurocognitive impairment (n = 89). NF1 (HR 2.1) was associated with precocious puberty. With a median time post-diagnosis of 4.2 years, 33 cerebrovascular events were observed in 21 patients. Late relapses, second malignancies, and cerebrovascular diseases are severe late events resulting in premature mortality. Morbidity is high and needs after-cancer care to improve quality of life. Risk factors could be considered to better stratify long-term follow-up.

Gaussian relevance vector MapReduce-based annealed Glowworm optimization for big medical data scheduling

Various big-data analytics tools and techniques have been developed for handling massive amounts of data in the healthcare sector. However, scheduling is a significant problem to be solved in smart healthcare applications to provide better quality healthcare services and improve the efficiency of related processes when considering large medical files. For this purpose, a new hybrid model called Gaussian Relevance Vector MapReduce-based Annealed Glowworm Optimization Scheduling (GRVM-AGS) was designed to improve the balancing of large medical data files between different physicians with higher scheduling efficiency and minimal time. First, a GRVM model was developed for the predictive analysis of input medical data. This model reduces the storage complexity of large medical data analysis by means of eliminating unwanted patient information and predicts the disease class with help of a Gaussian kernel function. Afterwards, GRVM performs AGS to schedule the efficient workloads among multiple datacenters based on the luciferin value in the smart healthcare environment with reduced scheduling time. Through computational experiments, we demonstrate that GRVM-AGS increases the scheduling efficiency and reduces the scheduling time of large medical data analysis compared to state-of-the-art approaches.

Efficient light-weight private auditing scheme for cloud-based wireless body area networks

With the rapid development of cloud computing, cloud-based wireless body area networks (WBANs) provide powerful storage services, and process massive medical data efficiently. As the medical data are exploited to perform the clinical diagnosis and other special medical treatments, the integrity of the medical data stored in the cloud server is increasingly important. In this paper, based on the elliptic curve digital signature algorithm (ECDSA), we propose an efficient light-weight cloud storage private auditing scheme for medical data in WBANs. Our scheme enables a data owner to check the medical data integrity effectively personally, and does not need to retrieve the entire medical dataset, thereby dramatically reducing the communication overhead. Moreover, we further extend our private auditing scheme to private batch auditing. Thus, the data owner can perform auditing task for multiple different medical data files simultaneously. The performance comparison demonstrates that our scheme is much more light-weight, and more practical in cloud-based wireless body area networks.

Efficient light-weight private auditing scheme for cloud-based wireless body area networks

With the rapid development of cloud computing, cloud-based wireless body area networks (WBANs) provide powerful storage services, and process massive medical data efficiently. As the medical data are exploited to perform the clinical diagnosis and other special medical treatments, the integrity of the medical data stored in the cloud server is increasingly important. In this paper, based on the elliptic curve digital signature algorithm (ECDSA), we propose an efficient light-weight cloud storage private auditing scheme for medical data in WBANs. Our scheme enables a data owner to check the medical data integrity effectively personally, and does not need to retrieve the entire medical dataset, thereby dramatically reducing the communication overhead. Moreover, we further extend our private auditing scheme to private batch auditing. Thus, the data owner can perform auditing task for multiple different medical data files simultaneously. The performance comparison demonstrates that our scheme is much more light-weight, and more practical in cloud-based wireless body area networks.

ECC Based Secure Sharing of Healthcare Data in the Health Cloud Environment

In today’s world, Cloud computing based health cloud is an effective platform for enhancing e-health productivity, i.e., healthcare professionals can provide medical service by accessing health information on any device in any location at any time. The major challenge faced by a healthcare organization is secure sharing of healthcare data in health cloud among general practitioner, medical services, and insurance firms. In order to overcome this issue, the security of sensitive healthcare data is accomplished with encryption mechanisms. In this research work, the medical data files are encrypted using elliptic curve cryptography algorithm to possess confidentiality and integrity in the healthcare data while sharing data via health cloud. The experimental results exhibits better performance in terms of key generation time, turn-around time, file encryption time, file decryption time, file upload time, file download time, file uploading speed, as well as security overhead. Furthermore, it also securely transmits the healthcare data over health cloud by defending against insider threats.

Noise-tolerance Investigation into Dual-kNN Pattern Classification

The performance of an algorithm is usually measured in three dimensions (simplicity, processing time, and prediction power). In addition, we should take into account the noise resistance level in those measures. For this reason, this paper focuses on investigating the noisetolerance level of dual k-nearest neighbors (dual-kNN) primarily based on five noisy medical diagnosis problems. Literally, dual-kNN is a reborn version of the k-nearest neighbors (k-NN) algorithm with a new observation idea in the classification process with a collaborative effort between the first and second nearest neighbors of an observed instance. It was recently proven that dual-kNN has high prediction accuracy for a variety of real-world data sets, especially so in unbiased data sets. Thus, in this report, not only the prediction accuracy of dual-kNN is compared with normal k-NN, logistic regression, and the neural network, but we additionally investigate the noise tolerance in the aforementioned approaches. The practical data sets applied in this paper are medical data files from the University of California, Irvine, Machine Learning Repository. In this report, the new approach to dual-kNN commences with better prediction accuracy, and higher noise resistance is presented, in comparison with normal k-NN, logistic regression, and neural networks.

Virtual Dental Patient: How Long Until It’s Here?

Recent developments in digital technology to be generated by superimposing different 3D imaging datasets. The aim of this literature review was to provide an update on the current status of knowledge, to report on the progress in virtual patient science, and to identify further research needed to facilitate the translation to clinical practice. Case reports were identified describing different dental patient simulation techniques. Publications focused mainly on methodological feasibility of combining skeletal, facial, and dental imaging to produce virtual simulations. Clinical randomized controlled trials were scarce. Superimposition of 3D medical data files is a feasible technique to create a virtual patient under static conditions. 3D image fusion is of interest and importance in all fields of dental medicine including diagnostics, treatment, science, and education. Future research should focus on simplifying the technology, to enable virtual patient simulation to be generated in a single-step approach.

ЕЛЕМЕНТИ ІНТЕРФЕЙСУ НАУКОВО-ДОСЛІДНОГО WEB-РЕСУРСУ PHYSIONET ТА ІМПОРТ ДАНИХ У СИСТЕМУ КОМП'ЮТЕРНОЇ МАТЕМАТИКИ MAPLE 17

Since 1999, PhysioNet (http://physionet.org/) has offered free access via the web to large collections of recorded physiologic signals and medical databases as well as associated open-source software. The intention of this scientific resource is to stimulate current research and new investigations in the study of cardiovascular and other complex biomedical signals. PhysioBank archives include today the records obtained from healthy individuals and from patients with different diagnoses obtained under various conditions. It includes sudden cardiac death, congestive heart failure, neurological disorders, epilepsy and many others. Software packages PhysioToolkit is valuable for physiological signal processing and analysis, for creation of new databases, the interactive display and characterization of signals, the simulation of physiological and other signals. Nonetheless, a researcher should have skills to work with the operating system Unix and be knowledgeable in special commands to successful use software PhysioToolkit. Therefore, it makes sense to convert the necessary signals to a user-friendly computer algebra system. This paper describes interface elements of scientific web-resource PhysioNet, the simple methods of converting from binary medical data files to the text format and import of received digital signals into computer mathematics system Maple 17.

Recensement rétrospectif des cas de cancers de 1994 à 2002 autour de l’usine d’incinération d’ordures ménagères de Gilly-sur-Isère

Background The retrospective cancer incidence study carried out around the municipal solid waste incinerator of Gilly-sur-Isère (Savoie, France) was ordered in a context of crisis during its closing in the late 2001. Its purpose was to determine whether or not there was an excessive number of cancers around the incinerator. In the absence of cancer registry in Savoie, this study consisted in counting as exhaustively as possible the cancers that occurred between 1994 and 2002 in the study area, which was exposed to the atmospheric fallouts from the incinerator. Thus, it was planned to compare the observed cancer incidence to the French cancer registries’. This work describes the main difficulties encountered as well as the solutions found during the census of cancer cases; the results of the incidence study are not included. Methods The collection of medical data was carried out thanks to multiple sources of information: pathology and hematology laboratories, hospitals’ and clinics’ departments of medical information, health insurance funds, liberal practitioners or specialised cancer registries. The collected medical data files were dealt with: looking for the missing addresses, selecting patients from the study area, homogenizing cancers coding, merging files into a single database, analysing available information on each cancer and de-duplicating the database. Most cancers were validated by consulting medical folders so as to exclude the false cases like metastasises of a known primary cancer or recurrences. Results Two thousand eight hundred and forty-five cancers were initially collected, and 28% of them were excluded because they did not correspond to the case definition (no proof of cancer, diagnosis date before the study period…); the final database was made of 2055 cancer cases. Quality indicators showed that the database could be considered as exhaustive and valid as a registry's. Three types of sources allowed to identify 94% of cases: laboratories, hospitals’ departments of medical information and health insurance funds. Conclusion Using administrative data and consulting medical folders turned out to be necessary considering uncertainties about: the patients’ residence at the time of the diagnosis, errors in coding cancers in some databases that were collected and difficulties to identify false cases. This census required very important means.