Analysis and Design of Inpatient Daily Cense Information System in Bhayangkara Bengkulu Hospital

Background: Progressing technology in the world need to fast and accurate information in the hospital agencies as the basis for appropriate making decision. The inpatient daily census reporting of system Bhayangkara Hospital Bengkulu don't have utilized the Inpatient Daily census system electronically and still uses a manual system, so that the processing of report data is less than optimal. There are still a lot of inputting errors, inaccurate data, and inefficient time and energy. This study to aim design system information inpatient daily census reporting application at the Bhayangkara hospital to existing problems solving.Methods: The method used in designing and making this application is by utilizing software development methods, namely the waterfall method which includes identification, analysis, design or design, implementation and maintenance of the system.Results: The results this study is creation of an application to facilitys the processing of data into an inpatient daily census report that is needed and to overcome the problems that arise because of the report processing system manually. Design and Creation of Inpatient Daily Census Applications with Visual Basic 6.0 Programming at Bhayangkara Bengkulu Hospital have been made with the results of an analysis of existing systems and according to the method used, and the design of the forms that have been made in accordance with the manual form or home party needs sick and can simplify filling out forms and processing the data.Conclusions: At Bhayangkara Bengkulu Hospital still uses a manual inpatient daily census system, and not on time for reporting daily cencus patient data. The data structure contained in the ledger consists of patient identity, patient diagnosis, and others. There are three processes in the stage of analyzing the needs of the inpatient daily census system, namely the data input process, data processing and data output processes. ledger, patient data consisting of patient identity, doctor's name, patient diagnosis, treatment room, and treatment class. In designing the daily inpatient census system at Bhayangkara Bengkulu Hospit consists of patient data forms, incoming patients, outgoing patients, and patients moving. The implementation of the daily inpatient census system at the Bhayangkara Bengkulu Hospital has carried out socialization and discussions about the user interface design to officers or users of the electronic daily census system. And the maintenance of the daily inpatient census system is carried out in several stages (1) corrective, by correcting design and errors in the program, (2) adaptive, by modifying the system according to user needs, (3) perfective, namely processing census data computerized.

A nonlinear population dynamics model of patient diagnosis and treatment involving in two level medical institutions and its qualitative analysis of positive singularity

<abstract><p>In this article, we firstly establish a nonlinear population dynamical model to describe the changes and interaction of the density of patient population of China's primary medical institutions (PHCIs) and hospitals in China's medical system. Next we get some sufficient conditions of existence of positive singularity by utilising homotopy invariance theorem of topological degree. Meanwhile, we study the qualitative properties of positive singularity based on Perron's first theorem. Furthermore, we briefly analyze the significance and function of the mathematical results obtained in this paper in practical application. As verifications, some numerical examples are ultimately exploited the correctness of our main results. Combined with the numerical simulation results and practical application, we give some corresponding suggestions. Our research can provide a certain theoretical basis for government departments to formulate relevant policies.</p></abstract>

Post-synaptic scaffold protein TANC2 in psychiatric and somatic disease risk

Understanding the shared genetic aetiology of psychiatric and medical comorbidity in neurodevelopmental disorders (NDDs) could improve patient diagnosis, stratification and treatment options. Rare TANC2 (Tetratricopeptide Repeat, Ankyrin Repeat and Coiled-Coil Containing 2) disrupting variants were disease-causing in NDD patients. This post-synaptic scaffold protein, essential for dendrite formation in synaptic plasticity, plays an unclarified but critical role in development. We here report a novel homozygous-viable Tanc2 disrupted function model where mutant mice were hyperactive and had impaired sensorimotor gating consistent with NDD patient psychiatric endophenotypes. Yet, a multi-systemic analysis revealed the pleiotropic effects of Tanc2 outside the brain such as growth failure and hepatocellular damage. This was associated with aberrant liver function including altered hepatocellular metabolism. Integrative analysis indicates that these disrupted Tanc2 systemic effects relate to interaction with Hippo developmental signalling pathway proteins and will increase the risk for comorbid somatic disease. This highlights how NDD gene pleiotropy can augment medical comorbidity susceptibility underscoring the benefit of holistic NDD patient diagnosis and treatment for which large-scale preclinical functional genomics can provide complementary pleiotropic gene function information.

Deep Learning Applied to Intracranial Hemorrhage Detection

Intracranial hemorrhage is a serious health problem requiring rapid and often intensive medical care. Identifying the location and type of any hemorrhage present is a critical step in treating the patient. Diagnosis requires an urgent procedure and the detection of the hemorrhage is a hard and time-consuming process for human experts. In this paper, we propose a novel method based on Deep Learning techniques which can be useful as decision support system. Our proposal is two-folded. On the one hand, the proposed technique classifies slices of computed tomography scans for hemorrhage existence or not, achieving 92.7% accuracy and 0.978 ROC-AUC. On the other hand, our method provides visual explanation to the chosen classification by using the so-called Grad-CAM method. TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back

Encoder-decoder models for chest X-ray report generation perform no better than unconditioned baselines

High quality radiology reporting of chest X-ray images is of core importance for high-quality patient diagnosis and care. Automatically generated reports can assist radiologists by reducing their workload and even may prevent errors. Machine Learning (ML) models for this task take an X-ray image as input and output a sequence of words. In this work, we show that ML models for this task based on the popular encoder-decoder approach, like ‘Show, Attend and Tell’ (SA&T) have similar or worse performance than models that do not use the input image, called unconditioned baseline. An unconditioned model achieved diagnostic accuracy of 0.91 on the IU chest X-ray dataset, and significantly outperformed SA&T (0.877) and other popular ML models (p-value < 0.001). This unconditioned model also outperformed SA&T and similar ML methods on the BLEU-4 and METEOR metrics. Also, an unconditioned version of SA&T obtained by permuting the reports generated from images of the test set, achieved diagnostic accuracy of 0.862, comparable to that of SA&T (p-value ≥ 0.05).

The evaluation of psychogenic non-epileptic seizures (PNES) cases with saline injection method in video-EEG monitorization unit

ABSTRACT Background: It has been reported that 10 to 30% of patients sent to epilepsy centers with a diagnosis of refractory epilepsy are diagnosed with psychogenic non-epileptic seizure (PNES). A wide variety of provocative methods are used to assist PNES diagnosis. Objective: To investigate the effect of seizure induction on the diagnosis and prognosis of PNES. Methods: We retrospectively examined 91 patients with PNES complaints in our video-EEG laboratory. Intravenous saline was administered to all patients for induction of seizures. Results: Saline injection was performed in 91 patients referred to our EEG lab with PNES initial diagnosis, 57 of whom were female and 34 male. Saline injection triggered an attack in 82 patients (90%). Conclusions: In this study we have concluded that provocative methods are practical, cheap and, most of all, effective for patient diagnosis. In clinical practice, explaining the diagnosis is the first and most important step of the treatment, and careful patient-doctor communication has a positive impact on patient prognosis.

Diagnosis and management of neutropenia in children: consensus audit of the “Associazione Italiana Ematologia Oncologia Pediatrica” (AIEOP) experts

SUMMARY Neutropenia is a generic term that indicates a reduction of neutrophils below the threshold for age and race. This condition encompasses a number of diseases with a wide range of duration and severity. In the present paper, the approach to diagnosis and treatment of neutropenia has been reviewed and implemented with the knowledge acquired during the last decade, by a group of experts, 10 years after the first publication. The diagnostic itinerary highlights the most important tools available to define the type of neutropenia and updates the list of genes causative of the disease. In addition, the present paper underlines the progresses towards a better definition of “primary autoimmune neutropenia” without remission which often hides different diseases. Moreover, indications on how to speed up neutropenia diagnosis and the indications to perform the bone marrow examination in the genetic forms, are given. The management and treatment of the “well-known” diseases and “special situations” are also reviewed giving literature derived and expert opinion-based suggestions tailored on the single patient/diagnosis.