HNF-4α controlling many genes involved in liver function such as the GLUT2 and L-PK genes. Evidence on the mode of action of metformin shows that it improves insulin sensitivity by increasing insulin receptor tyrosine kinase activity and enhancing glycogen synthesis in hepatocytes, and by increasing recruitment and transport of GLUT4 transporters to the plasma membrane in adipose tissue. In addition to its effects on hepatic glucose and lipid homeostasis and adipose tissue lipid homeostasis, metformin exerts effects in the pancreas, vascular endothelial cells, and in cancer cells.
The Glycemic Index Laboratories located in Toronto, Canada, performed tests on four different sweeteners to demonstrate the postprandial (after consumption) blood glucose and insulin responses. The study consisted of 15 healthy subjects between the ages of 18 and 75. Blood glucose levels after the non-nutritive sweeteners were significantly lower compared to sucrose at 15, 30 and 45 minutes. Postprandial incremental serum insulin measurements after four different sweeteners balanced for sweetness. Your child has been newly diagnosed with type 1 diabetes and we are aware that you may be feeling emotional, confused and shocked about the diagnosis and may have many questions about what is happening and where to go from here. Everything that you will need to know about diabetes and managing it will come in time but for now we will be teaching you the basics to manage your child’s diabetes in the next few days.
The rest of the information regarding the diagnosis will be done in follow up appointments in the next few days and weeks and you will be in daily telephonic contact with the doctor or your educator. There is a lot to learn about managing your child’s diabetes in the beginning and you cannot possibly learn it all in one day.
YOUR CHILD IS FIRST AND FOREMOST STILL A CHILD BEFORE THEY HAVE DIABETES AND DIABETES NEEDS TO FIT INTO THEIR LIFESTYLE NOT THE OTHER WAY ROUND.
You and your child are not alone in managing this condition, of course you have to do all the daily working in managing good blood glucose control, but our diabetes team will help you every step of the way. This manual is designed to help you with all the basic survival tools to look after your child, but each day will be a learning experience. Simply defined it is lack of insulin secreted from the pancreas, resulting in high levels of sugar (glucose) in the blood. When your child has diabetes it means that they have too much glucose (sugar) in their blood. Diabetes is not currently curable; however with proper management such as insulin injections and blood glucose testing, proper meal plans and regular exercise your child can have a normal long happy life, both physically and emotionally. Your child should be eating meals that are balanced with carbohydrates (sugars and starches), fats and protein (mostly meat). Carbohydrates are broken down in your child’s stomach, converted to glucose and absorbed into the blood stream as one of the major sources of energy for the body. The pancreas senses the rise in blood glucose levels and secretes the right amount of insulin to move the glucose out of the blood stream into their cells.
Quite simply insulin is secreted every time we eat and a slow release in the background between meals. As your child does not produce insulin anymore they rely on their insulin injections to provide meal time coverage and the background insulin needs.
This is a snap shot of your body during the night when your liver is releasing stored glucose back into your blood stream to supply a constant amount of glucose to the brain while you sleep.
When food enters your stomach, the carbohydrates get broken down into glucose and the glucose gets absorbed into your blood stream and your blood glucose level starts rising. If your pancreas is working, it samples the amount of glucose in the blood stream and produces the right amount of insulin and releases the insulin into the blood stream The insulin opens the cells and allows the sugar in and blood sugar will come down.
If you do not have insulin you have no problem getting the carbohydrate into the blood stream but have no way of opening up the cells to let the sugar in and your blood sugar levels stay high.
High blood sugar gets filtered by the kidneys and glucose ends up in the urine, therefore it causes increased urine flow and your child will urinate a lot and therefore drink a lot to catch up.
When your body cannot use glucose for energy it will find another source of energy and you will get these other sources of energy from your body breaking down your muscle and you will loose weight and feel weak and tired and you will also break down fat and produce ketones which makes you sick.
There are 5 main food groups that make up a balanced healthy diet in order for your child’s body to get all the vitamins, minerals and nutrients they require to function at its optimum. The main food groups can be divided into 3 nutrient groups which have individual effects on the body. Proteins – These nutrients are our bodies’ growth foods and have little effect on the blood glucose levels. Fats – These foods are also energy foods in the body; however they have twice the amount of calories than carbohydrates and therefore are to be kept to a minimum, in order to protect the heart and other vital organs. The rate of absorption of glucose in and out of the blood stream is called the glycaemia index (GI).
High GI (HGI) foods are absorbed quickly into the blood stream and tend to raise blood glucose levels quickly.
Intermediate GI (IGI) foods are absorbed at a gradual rate and have less of a rise on the blood glucose values. Low GI foods (LGI) are absorbed very slowly into the blood stream and tend to keep blood glucose values more stable for longer periods than HGI and IGI foods. There are different ways to measure carbohydrates and relate them to the effect they will have on your blood glucose level, this concept is called carbohydrate counting and is a very useful tool to use. Carbohydrates are divided in two main groups and the sugar group is further divided in 3 groups.
Most foods have nutritional information on the packaging, so these are easy to read and determine.
Diabetic products are not usually encouraged as they are poorly marketed, costly and they only remove sucrose form the product but still have other glucose products in them, therefore they are not completely free ( Unless the total carbs on the nutritional label says 0 grams) and will still raise the blood glucose levels.
Your child’s Diet needs to be individualised and you should see a dietician who understands children with diabetes to structure something for your child individually.
Sugar is allowed in small to moderate amounts and will make up part of your carb value at meals. Proteins get broken down into glucose very slowly in the blood stream and only 50-60% of protein gets converted to Glucose, making it an ideal food for your child to eat as a snack or as the main portion of a meal. Testing your child’s blood glucose levels regularly, will help you to achieve daily blood glucose targets and it is your only tool to help you administer the correct dose of insulin or see what changes need to made to food or exercise etc. Before each meal and at bedtime, and at any other time you think you may be low or very high. If you are having problems with higher or lower blood sugars email or fax through the last 3-4 days results and insulin doses being used and we can help you make adjustments.
The risk of long term diabetes complications is related to overall blood glucose control that is above ranges for many years.
Push the plunger all the way down –if using pens, hold for 10 sec and if using syringes hold for 2 sec. Let go of your pinch before pulling out the needle, this will prevent the insulin from leaking out.
If they are having lots of issues with injections or have a severe needle phobia we can use insulin ports to deliver insulin through. Most often when you have a low blood glucose value your body will give you warning signs – here are some symptoms, it’s also best that you recognise your individual symptoms and treat a low blood glucose values as soon as possible. Don’t get into the habit of over treating low blood sugars and getting a high blood sugar thereafter by feeding them too much. Give the above treatment and recheck blood glucose values again after 15-20 min if they still complain of feeling low. NB Insulin must be given before a meal if your child was low, you will fix the low blood glucose and then give the normal dose of insulin before that meal, you will need insulin to store that food eaten in the cells of the body otherwise the next blood sugar will be high – they will not go low again! If your child is confused and unable to swallow – rub condensed milk, syrup, honey or glucose syrup onto the gums if that does not raise the blood glucose levels Glucagon needs to be used. Blood glucose values need to be checked every 3 hour for next 6 hours after a severe episode. High blood glucose values above target along with high HbA1c’s over many years can lead to complications associated with diabetes. When a diabetic child is ill, it is a very unstable time as blood glucose values may fluctuate erratically.
Diabetic children need there insulin when they are sick, sometimes even larger doses, even if they do not want to eat, insulin must NEVER be skipped and the types of foods or liquids may need to be adjusted along with the amount of insulin.
Encourage your child to eat their usual meal or something from the list below if there stomach is upset or they are having difficulty eating. If your child is vomiting and there are no Ketones they need to have small sips of fluid every 15 minutes to avoid dehydration. Medicines for fever, pain, decongestants, runny tummy or nausea, coughs and colds may contain small amounts of sugar. In the absence of insulin your body cannot use your glucose for energy provided by the food you eat. When Ketones are detected you should contact your Dr, Educator or the 24 hour emergency hot line, so they can advise you on clearing the Ketones. Diabetes camps are one of the best experiences that a child with diabetes can have and it is advisable that you allow your child to be involved in this experience, especially when they are newly diagnosed. Camp is a place for your child to learn self-confidence, independence from mom and dad, to be with other kids with diabetes, and simply to have a great time as well as have the opportunity to make lifelong friends. We interact with each other, learn teamwork, make friends, survival skills and time to play, be creative and do some rhythm workshop and have lots of FUN, FUN, FUN!! The purpose of this note is to let you know the implications of diabetes and how it may affect me at school.
If there are any questions please write them down and I will get my parents or my doctor to answer them for you. The main dangers that arise from diabetes at school are low blood glucose values or hypoglycemia. If I experience a low blood glucose value during class I will need to test my blood glucose values and I will need to eat foods that have glucose in them to raise my blood glucose values again.
If I am unconscious or have a seizure from a low blood glucose value I will need to be injected with the following injection called glucagon that will increase my blood glucose values.
When I wake up please give me sips of juice or coke and check my blood glucose every 5-10min. There may be some side effects 30 min after the injection such as: Nausea,vomiting, bloating and headache.
Authors focus on ontology-based multidimensional data warehousing and mining methodologies, addressing various issues on organizing, reporting and documenting diabetic cases and their associated ailments, including causalities.
In order to handle multiple domains, heterogeneous and multidimensional data, a robust methodology is proposed to address the issues of documentation and organization of data, including public awareness through education.
The comprehensive education programs may include resolving various issues, such as educating the mass population, awareness and the consequences of this deadly disease, and including healthy lifestyles and food habits. As narrated in Table 1, these programs may be organized in the countries, where severe cases are reported on a priority basis. Multidimensional modeling of data instances relevant to antioxidants and their associated foods affecting the blood sugars. Each region, which is inherently a composite dimension, consists of several points and profile-lines. Another exenatide-related drug is Bydureon® which is a once-a-week injectable form of exenatide. A more recent addition to the GLP-1 receptor agonist family of diabetes drugs is Trulicity® (dulaglutide) manufactured by Eli Lilly and Co. Additionally, it has been shown that metformin affects mitochondrial activities dependent upon the model system studied. The latter effects of metformin were recognized in epidemiological studies of diabetic patients taking metformin versus those who were taking another anti-hyperglycemia drug.
As demonstrated in the chart below, Swerve is non-glycemic and does not raise blood glucose (blood sugar) levels. Glucose levels were also significantly lower after both Swerve products compared to high potency sweetener at 15 minutes. Results are expressed as Mean±SEM, and using ANOVA for main effects of time and test meal and the time?meal interaction. Consult with your doctor, dietician or nutritionist to know if the recipe is appropriate for a diabetes diet. Your child is unique and you will learn in time how diet, exercise and different life situations affect your child’s blood sugar levels. There is a “genetic predisposition” (inherited factor) that needs to be present for the process to start and a viral infection can be the external trigger required to start the immune attack.
The food your child eats’, especially carbohydrates are broken down into glucose and stored in their cells for energy now or later so that your child can perform their daily activities like, learning, running, swimming and playing and more importantly having fun.


This is usually achieved by using 2 or 3 kinds of insulin, usually a long acting (background insulin) and a rapid acting insulin (meal time insulin). Half of the protein we eat gets converted to glucose over a long period of time so it has a gradual rise on the blood glucose levels. Fats are needed for cell growth and protection for organs and are a very important part in children’s growth and development.
The following pictures are a few examples of the main carbohydrates consumed and they are all measured in 1 carbohydrate value.
Below are a few typical foods that have been measured in 1 carbohydrate value they may not necessarily have nutritional information on them and they may need to be learned with time. Complications are obviously one of your concerns as a parent, but if you and your child work hard at getting the blood glucose values as close to target as often as possible so complications can be prevented. Injecting in the same area too often will cause scar tissue and lumps in the area and the insulin absorption is then unpredictable and poor. Areas that can be damaged from high blood glucose levels are the: eyes, kidneys, heart and feet. Anti-nausea suppositories can be given every 6 hours, if your child needs a second suppository call your Dr.
I know this may make you feel scared or worried to have me in your class as there are a lot of misconceptions about my condition.
I would like to explain my diabetes and I am sure you will see that I can lead a normal life jut like any other child in your class. Multidimensional Data Warehousing and Mining of Diabetes and Food-domain Ontologies for e-Health Management.
Map and other diagnostic data views, depicting similarity and comparison of attributes, extracted from warehouses, are used for understanding the ailments, based on gender, age, geography, food-habits and other hereditary event attributes. This paper proposes an intelligent information management system that can store and integrate different domain ontologies, such as diabetes and cholesterol, food and anti-oxidants, in multidimensional schemas. An educational program that refers to professionals involved in the management of public health care system is included in the Table 1. As per statistics narrated in [5], the global health expenditure on diabetics reached in total USD 376 billion in 2010 and 490 billion USD is expected to be spent in 2030.
The point dimension connects different domains of data, such as diabetic, food related and navigational data (that represent a geographic region or location).
Metformin has a mild inhibitory effect on complex I of oxidative phosphorylation, has antioxidant properties, and activates both glucose-6-phosphate dehydrogenase, G6PDH and AMP-activated protein kinase, AMPK.
If the time?mean interaction was significant, then ANOVA was conducted for each time point using Tukey-Kramer method to adjust for multiple comparisons. Sometimes a viral infection can trigger diabetes in some children, but you could not have stopped diabetes from happening. Insulin directs the uptake of glucose into the cells either to meet immediate energy needs or to store it for later use.
These foods make excellent snacks between meals and will not require extra insulin, further more it can be eaten when blood glucose values are high in order to prevent them from raising further. They raise the blood glucose values and are the foods groups that need to be kept under control in order to manage diabetes. Unless you are sick, then it necessary for you to test more often, especially when you have ketones, then you will need to test hourly.
The HbA1c is a way to measure this; it looks at the amount of glucose attached to your red blood cells. The high blood glucose levels damage all the small nerves and arteries to the organs mentioned above, however if you and your child manage your diabetes effectively you will not develop diabetes complications. Cortisone and or steroid containing medications cannot be taken, they increase Blood glucose levels aggressively the list is below. HOWEVER once your child has the above symptoms but starts to vomit and have difficulty breathing it can no longer be managed at home - the Dr needs to be contacted and your child will need to be admitted. In addition to rigor on data mining and visualization, an added focus is on values of interpretation of data views, from processed full-bodied diagnosis, subsequent prescription and appropriate medications. Many healthcare institutions in many countries spend billions of dollars, although the quality of services to diabetic patients has improved in many rich countries.
The systems can create a prescription or meal plan according to a person’s lifestyle and particular health needs.
In addition, preventive care helps controlling the disease, especially of hereditary nature.
Globally, 12 percent of total expenditure is spent on diabetes, USD 1,330 per person in 2010. Ontology-based Warehouse Modelling of Multidimensional Diabetes Data InstancesThe proposed methodologies are comprehensively used in various business environments [8,9,10]. The importance of AMPK in the actions of metformin stems from the role of AMPK in the regulation of both lipid and carbohydrate metabolism (see AMPK: Master Metabolic Regulator for more details). If undetected or untreated your child could develop DIABETIC KETOACIDOSIS (DKA), this is a medical emergency and they will need to be hospitalised. The proposed methodology, is a robust back-end application, for web-based patient-doctor consultations and e-Health care management systems through which, billions of dollars spent on medical services, can be saved, in addition to improving quality of life and average life span of a person.
A renewed campaign has emphasized preventive care for diabetic health, especially on documenting, revitalizing and bringing awareness about healthy food habits to the massed population of diabetic patients worldwide. The required knowledge is stored in an ontologically structured metadata model, predefined by domain experts.
Regular doctor check ups, healthy meal plans and regular body exercise, maintaining and controlling the existing glucose levels, in essence, are to educate mass population worldwide.
Expenditure is variable, based on geographic region, age, gender and countries’ GDP attributes. They are further extended in other domains robustly, such as the human ecosystems and human anatomy domains [4,11] to test their validity and versatility.
As shown in Figure 6b, horizontally and vertically varying hierarchies and their associated data instances are grouped from gathered data volumes.
In adipose tissue, metformin inhibits lipolysis while enhancing re-esterification of fatty acids. In between meals and during fasting low levels of insulin regulate the amount of glucose produced from the liver (your body’s main glucose store). Children, especially very young children have different targets to adults as they don’t always detect there low blood glucose values and very low HbA1c‘s are not suitable for them and they are individualized to the child. Government health departments and agencies, private and government medical practitioners including social welfare organizations are typical users of these systems. One of the main reasons are unhealthy life styles and food habits, which affect blood sugar levels, cholesterol and blood pressure. It contains diabetic ontology, including embedded set of personal and food-domain ontologies. Because of diabetic-related ailments, the losses in national incomes are enormous, for example, for years 2005–2015, the losses are estimated to reach USD 558 billion in China, USD 303 billion in Russia, and USD 237 billion in India. For this purpose, ontological descriptions written in the form of schemas in different domains, are integrated in a warehouse environment to make connectivity among domains. These grouped values are made interconnected through navigational data attributes, for which the coordinate data instances are described for all geometrical point dimensions of multiple regions.
The activation of AMPK by metformin is likely related to the inhibitory effects of the drug on complex I of oxidative phosphorylation.
Every year we are closer to a cure and the tools and knowledge we have to manage diabetes improve. One can imagine that because of unhealthy life styles, there are series of chain-linked chronic diseases.
The prescriptive medication of diabetes, is intelligently and effectively managed by ontology-based data mining, data visualization and interpretation. Surprisingly, for USA, in spite of an alarming diabetes situation in all age groups, because of the billions of dollars spent, diabetes and their associated ailments have been curtailed.
A metadata is constructed from which several data views are extracted for visualization and interpretation purposes.Similarity and comparison ontologies (Figure 1) are worth analyzing in understanding of similarity of patients’ ailments, in adition to comparing different domain data of similar and dissimilar data instances of patients, symptoms, food habits, medications and preventive care regimes. This would lead to a reduction in ATP production and, therefore, an increase in the level of AMP and as a result activation of AMPK. Medical professionals use these data views for interpretation of seriousness of ailments, their symptoms and appropriate prescriptions. Unfortunately, poorly populated countries are the real victims of diabetic-related diseases, because of very low expenditures per capita, suggesting urgent basic diabetes care needs.
For example, same gender persons may have similar food habits, with similar symptoms and or among age attribute groups.
In fact, since the cells of the gut will see the highest doses of metformin they will experience the greatest level of inhibited complex I which may explain the gastrointestinal side effects (nausea, diarrhea, anorexia) of the drug that limit its utility in many patients.
Recently, several contributions are made in documenting these cases and analyzing historical data for future forecasts [1,2,3].
An information system using a healthcare ontology provides a standardized representation of healthcare data documentation.
Even in rich countries, in North America and European countries, disadvantaged indigenous, ethnic minorities, recent migrants and slum dwellers suffer high rates of diabetes and its complications.
In this context the Authors have developed ontology-based multidimensional data warehousing and mining for organizing huge amounts of historical data [4]. One embodiment of the information system comprises a digital logic platform storing and using the healthcare ontology. The major challenge is to reduce social inequalities among and within countries that restrict opportunities for good health and access to healthcare. Another example is, preventive care of early symptoms for those patients, not yet diabetic nature (with symptoms persisting); medication and treatment phenomena may be in high risk and priority patients, in which, these ailments reported, are under medication for long periods of time. Even though multiple domains-research is ongoing, an integrated research effort on the connectivity of food-diabetic domains is lacking, especially using the existing information systems (IS) and the proposed IS solutions.
The healthcare ontology describes concepts and relationships among the concepts and contexts, derived from the corpus of domain-specific knowledge and linking it with standardized terminological systems. Economic implications and considerations on issues, associated with worldwide diabetes expenditure disparities, are beyond the scope of the current research.
Another example is, in such areas (geographically distributed, such as Africa, Asia, and Latin America), where people have similar lifestyles, with similar ailments and similar or dissimilar food-habits.
Worldwide, governments spend billions of dollars on preventive health-care systems and high priority medications. Ontologies target modelling worldly events (Figure 1) and construct expressions of complex classes, including narrating data characteristics with and without ambiguities. In developing countries, connectivity, communication and interaction among patients, medical professionals, dieticians, druggists and social welfare organizations involved in diabetes control, are critical issues. Similar age groups (such as 45–55 age groups) have similar diabetic type symptoms and or prescriptions. Countrywise diabetic cases and associated diseases, have so far been difficult to document and or report, because of complications in organizing these data and information on geographic and periodic scales. Ontologies are used in applications that need focus of semantics, schematic and syntactic heterogeneities. Projects associated with preventive care, drug research, inadequate access to medical records and operational research are other challenges.
Social welfare organizations, medical institutions, clinical specialists, nutrition professionals and dieticians often encounter problems of data and information access on both these scales. Ontology describes the meaning of the content that articulated among data attributes, used in the current data modelling processes.
There could be several preventable and avoidable medical complications; patients may die because of lack of awareness and unhealthy lifestyles. Event Similarity Prediction (ESP) is an algorithm that can compare, compute attributes and narrate histories of ailments among mass populations. Vertically Varying DimensionsAs shown in Figure 6, data instances are collected from a profile that connects a geographic region, where several points are described in vertically varying dimensions. Awareness includes early preventive measures and already high-risk patients on high priority treatments, such as medication and implementing healthy food habits.
Frequent occurrence of diabetic symptoms in a period of time under analysis is also significant. As described in Figure 6b, vertically varying dimensions are grouped on all profiles and thus computed grids and surfaces. These computed grids and surfaces across profiles represent vertically varying ontological descriptions.


In developed countries, this is a common disease, though this trend is changing in recent years. Issues, Challenges and Problem StatementThis paper is intended to study the feasibility and applicability of robust database methodologies such as, an ontology-based multidimensional data warehousing and data mining for integrating multiple domains, such as diabetes and foodstuffs (and nutrients) that include antioxidants.
Poor data quality leads to practice variation, medical errors, out-of-control emergency room visits, and fraudulent claims. Each geometric dimension, either point, profile or region possess domain-data instances with hierarchically varying magnitudes in the vertical direction.
In developing countries such as, India, China and other Latin American countries, there is an increasing attention and awareness. This can evaluate the effectiveness of diabetic disease management, using both patient medical records and medicare claims that address clinical outcomes, costs and process measures. Providers increase prices, payers increase premiums, and patients lack quality access care. All these data instances are populated in dimension and fact tables, with details of patients, doctors’ notes and facilities, where preventive care and medication are in progress. When their instance values vary in this direction, the data structure changes into different relationship or as responses reported from conceptual domains, such as dips, slopes (dip and steepness are again dimensions) of the response curves measured from diabetic patients, as are discussed and interpreted in forthcoming Section 6, Section 7 and Section 8. In a mass population, initiatives must include electronic consultations, electronic prescriptions, greater reliance on evidence-based medication, care collaboration centers with easy access to diabetic patients’ medical records and improved inventory management. If the interventions can positively influence various measures taken, the project may been expanded to an advanced care stage. Besides these issues, data entry is prone to error, especially during claim filing, procedure coding and medical records. As described in [11], several models comprising of dimensions and their attributes in a warehouse environment, focus on rules and constraints, templates, mappings, domains, formats, measurement types, instruments, dimensions, attributes, fields, treatments, patients, medical practitioners, medical prescriptions, bench marks, test results, specimens, specimen types, specimen sizes, periods and units.
In summary, mapping is carried out by arranging multiple geometrical dimensions with hierarchical structural representations, as illustrated in Figure 6a, normalizing and denormalizing [11] data relationships (evolved through conceptualizations, specifications and contextualizations). These initiatives may complicate the design, development and implementation processes on a massive scale. Reduction in medicare payments suggest inpatient hospital services and emergency room visits, as well as increase in the percentage of patients, being tested annually for HbAlc and being referred for diabetic education. When there are changes in technology and disparate systems, the chance of introducing errors increases.
In addition, glucose levels, glucose type, ophthalmology, eye tests, daily food intake, types of food, food hygiene, normal daily routines, and amount of time spent in daily exercises, cholesterol level are other relevant dimensions. In this mapping process, diabetic clinical data, including results (data instances) are made connected through multiple domain ontologies in horizontally, laterally and vertically varying dimensions. For this purpose, ontology-based multidimensional data warehousing and mining are proposed for physical and logical organization of heterogeneous patients’ and medical professional’s data.
More importantly, HbAlc test results are expected to improve understaning of this ailment, if any. In addition, machine and equipment maintenance and their responses are significant and these data add value to the domain modelling process and to the ultimate metadata structure and its interpretation. These multidimensional schemas are integrated with other schemas, related to lab test data instances from several other dimensions and fact tables. In another procedure [12,11], such clinical tool measurements obtained, grouped across profiles (line and region-profiles) of patients (at both local and global perspectives) and their associated food domain ontologies are connected and integrated, as described and narrated in different schemas in the following sections.
Data integration through warehouse modeling, mining and visualization can facilitate an accurate interpretation that involves saving human lives.
Star schema models that can handle multidimensional data instances are represented in Figure 3. As per WHO statistics (Figure 2), the alarming situation is that people in India, China and USA are suffering from diabetes in numbers respectively 32, 21 and 18 million, and these figures are expected to reach 79, 42 and 30 million by 2030. Different domains, such as food and diabetes are described and their connectivity is intended to be established among common attributes of dimensions of different domains.
Other countries with millions of sufferers include Australia, Indonesia, Japan, Pakistan, Russia, Brazil, Italy and Bangladesh, which suggest the availability of enormous data sources worldwide. Awareness among the mass population leads to appropriate remedies for diabetes and healthy lifestyles. If the attributes narrated through conceptualization and contextualization among attributes of associative domains have an ambiguity in describing their naming conventions and vocabularies including schemas, they can be resolved by semantic and schematic ontologies. Diabetes education occurs in a variety of settings, depending upon needs of the patient and severity of the disease.
The data warehouse generates internal identifiers for all patients and responsible for linking all the other pertinent dimensions and fact tables, such as specimen test dimension and fact tables.
Even the data structures, while connecting between domains may have created problems, but are set to have these resolved through description of semantic and schematic based ontologies.
Inpatient, outpatient, home health and pharmacy settings, all provide avenues for effective individual and group education. The data warehouse considers the concatenation of patient name, date of birth and history number as primary keys used in the identification patient dimension fact tables. For example, the attributes described in diabetes and food domains, if their attribute strengths are exchanged in either domain, they are still positively correlatable. Proposed data modeling and warehousing concepts support the description and integration of multiple data sources within and outside the organizations. In a more conventional approach, several primary keys of other dimension and fact tables, including outpatient care systems of given patient with different patient’s history numbers of their dimension tables, are connected, including the correct spelling of patient’s name, units of history numbers and dates of birth. Repetitive or incompatible data from these systems can be a significant source of data quality problems. A variety of heuristics are used at query time to determine when it is safe to assume that different primary key instances are associated with a single patient. In addition, consistently standardized data formats and datasets for transactions, are crucial for maintaining good quality data and thus for improved health care system designs.
In most cases, data warehouse designers chase the records of the patients and check physically which records are kept or discarded in consultation with physician. Specimen Dimension (Spc Dim): This table organizes, storing all the specimen records which are collected from laboratory tests. Many patient records may have typographical errors, missing values or incorrect information regarding the patients.
This dimension table has typical attributes such as identification number, the specimen collection date and time, and hospital or outpatient collection location. Specimens are typically identified by the combination of specimen numbers and collection date and time.
Data cleaning takes enormous amount of time and many records collected are not in compatible formats for ontology descriptions and modeling, processing data in a warehouse environment and or for interpretation is time consuming.
Another issue is generation of too many mining rules from the data that can confuse interpretation by medical practiners and even patients. Doctors are too busy with patients; they cannot afford time to sieve large number of mining rules generated by the databases. Internal patient and specimen identifiers are included to link specimen tests of patients and specimens, respectively, linking dimension and fact tables.
It is important to present the data in an easy understandable way, interpretable by the medical practitioners.
Data trends, correlations and patterns are so critical in interpretation of classifications of patient and medical records.
Tests are identified by the combination of specimen identifier, test code and ordering physician instances. Special skills are needed to improve and understand the data mining, data visualization and interpretation tasks.
Data warehouse approach reconciles data organization that include format differences and encoding schemes during integration, including resolving semantic, schematic and syntactic heterogeneities.
Transaction dimension provides a mechanism by which the progressions of lab tests are documented.
Data visualization is used to enhance an understanding of the pictorial representation of diabetic records and their clinical interpretations.
In the fact table, for each transaction, there is an internal identifier created, by the warehouse. Provisional test results may be generated before the final results are made available, to the patient or consulting physician. The Transaction Dimension Identified is connected to the preliminary and final test result dimension tables. Since the entire lab tests are tagged with transaction identifiers, lab results can easily be sorted out by transaction identifiers, such as transaction fact table identification, which can reveal the progression of the lab results from preliminary to final data instances. Motivation and Need of OntologiesOur intention is to design and describe an integrated framework work, which can effectively lessen the research effort of managing medications of diabetic patients.
Component Dimension (Comp Dim): The composite dimension and lab test results are stored in these dimension and fact tables.
Keeping in view the variety of users, doctors, dieticians and professionals involved in preventive care, a systematic approach involving ontology-based data integration is needed. Typically they consist of component code, component name, reference ranges, units of measure and results.
Archetype patterns [6] used with high level ontology abstraction, at global scale are analyzed, keeping in view bio-informatics and health-care domains on a geographic scale [7]. As narrated before, a transaction identification id, from the transaction dimension and fact tables is linked with all components to track the progression of component data instances.The component dimension and fact tables are the finest level of granularity for a majority of lab tests (dimension tables). Ontology deals with queries on what entities and or dimensions exist in a given domain and how such entities or dimensions can be grouped, related within hierarchical, relational and networked data structures. There could be some other lab tests for which the value of the result column is just a reference (through attributes) into other dimension and fact tables.
Ontology Web Language (OWL) is a knowledge representation, and in our case, knowledge is expressed in the form of various fine-grained data schemas as represented in Figure 3, Figure 4 and Figure 5, which explore connections from multiple domains, which can make data mining effective, including visualization and interpretation. For this purpose, the component dimension table that contain a result type column, records the method of interpretation including diagnostic notes in the result column. Multidimensional data relationship diagrams drawn, are modelled in Oracle-driven, Windows-based high speed computing workstations. Thus at places, the result column appears as the root of a hierarchical structure for specified lab tests. Availability of heterogeneity and multidimensionality nature of data sources on food-diabetes has, in fact, motivated us the necessicity of developing an ontology-based warehouse for accommodating multidimensional healthcare metadata structures.
The component result is stored as reference data instances for lab result reports as blocks of test and microbiology culture results. This is supported by creating a text dimension (and corresponding fact tables) identifier for the block of text, storing the identifier in the component result field and finally storing the block of text in the free text dimension table (fact tables). To store other forms of complex lab results, a table is created with a new data type value to store the component result type column. When new data are made available, a unique identifier for the data is generated, the identifier is stored in the component result column and filled the component result type column appropriately. The lab results are added to the new table along with the data identifier that was added to the component result column. Isolate Dimension (ISL): Portions of culture results from tests performed on specimens, are stored in tables, associated with these dimensions. This table stores the organism or organism class that was identified, the identifier for the isolate, and the transaction identifier that is stored in the component result column. Typically, the table contains antibiotic name, antibiotic code, sensitivity results, and isolate identifiers.
The component table references it to store component comments and test results that are reported as multiple lines of text. This table stores the machine identifier, date and time, hospital1, location within the hospital, and a transaction identifier for each POCT device uploaded. The upload frequency can be sorted by device identifier, hospital, or location within a hospital.The transaction identifier is used in the GTS and HEM tables, connecting the glucose and hemoglobin data, respectively, to several other transactions.
These tables contain patient identifiers, operator identifiers, QC results, and test results. The patient identifiers can be used to link patients to lab results by cross-referencing with the patient table.




Glucose blood sugar range levels
Fasting blood sugar level 6.5 solution


Comments

  1. 02.10.2014 at 15:25:53


    Before you drank the glucose fasting plasma glucose concentration of 6.1 mmol?l-1.

    Author: Stilni_Qiz
  2. 02.10.2014 at 15:48:31


    That you don't drive your blood glucose too low.

    Author: Prinsesa_Wostoka