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Health Informatics Assignment Week 7 to 9

Week 7 Question

  1. What are the five possible approaches to accelerating the creation of standards?


    The process of creation of health standards involve: Health informatics requirements help ensure that specific health systems used by physicians, patients and residents are safe, accessible and fit for the purpose. In the case of health care, the concept data quality includes processes, procedures, definitions and requirements for the compilation, distribution, processing and management of healthcare-related information include health history, medications, radiological images, payments and refunds. There were several facets to the production process. Task Force AAMSI was ASTM subcommittee E31.11 developed and launched ASTM standard 1238 for the interchange of clinical-laboratory knowledge. To establish standards, two other organizations, several of those had collaborated in the former AAMSI task force, were formed, albeit with a slightly specific focus. The objective of the ACR / NEMA specification was to advertise a standardized format for digital picture information exchange, to promote the development in different of image cataloging and communications networks. This RIM is a set of subjects, situations, categories, characteristics, application instances, performers, cause incidents, encounters, and so on represent the details required to define HL7 messages. RIM's specified aim is to have a framework for the development of communication requirements and HL7 communications.

  2. Define five health care standards, not mentioned in the chapter, that might also be needed?
  3. What role should the government play in the creation of standards?
  4. At what level might a standard interfere with a vendor’s ability to produce a unique product?
  5. Define a hypothetical standard for one of the areas mentioned in the text for which no current standard exists. Include the conceptualization and discussion points. Specifically state the scope of the standard.

Week 8 Question

Questions for Discussion

  1. Develop a regular expression to regularize the tokens in lines 4–9 of the cardiaccatheterization report shown in Figure 8.7 (Complications through Heart Rate).
  2. Create a lexicon for the last seven lines of the cardiac catheterization report shown in Figure 8.7 (Conclusions through the last sentence). For each word, determine all the parts of speech that apply, using the tags in Table 8.2. Which words have more than one part of speech? Choose eight clinically relevant words in that section of the report, and suggest appropriate semantic categories for them that would be consistent with the SNOMED axes and with the UMLS semantic network.
  3. Using the grammar in Figure 8.3, draw all possible parse trees for each of the sample sentences 1a, 2a, and 3a discussed in Section 8.4.3. For each sentence, indicate which parse represents the correct structure.
  4. Using the grammar in Figure 8.3, draw a parse tree for the last sentence of cardiaccatheterization report shown in Figure 8.7.
  5. Using the grammar in Figure 8.4, draw parse trees for the following sentences: no increase in temperature; low grade fever; marked improvement in pain; not breathing. (Hint: some lexemes have more than one word.)
  6. Identify all the referential expressions in the text below and determine the correct referent for each. Assume that the computer attempts to identify referents by finding the most recent noun phrase. How well does this resolution rule work? Suggest a more effective rule.

The patient went to receive the AV fistula on December 4. However, he refuses transfusion. In the operating room it was determined upon initial incision that there was too much edema to successfully complete the operation and the incision was closed with staples. It was well tolerated by the patient.

Week 9 Question

  1. What is the general principle that underlies computed axial tomography (CT)? What are the advantages of CT images over conventional X-ray images?
  2. Explain the general principle underlying magnetic resonance imaging (MRI)? What are the advantages of this method compared to older methods of imaging?
  3. Explain the differences among contrast, spatial, and temporal resolution.
  4. Describe the four standard image-processing steps, and suggest how these might be applied by an imageanalysis program looking for abnormal cells in a PAP smear.
  5. What is the segmentation step in image analysis? Why is it so difficult to perform? Give two examples of ways by which current systems avoid the problem of automatic seg-mentation. Give an example of how knowledge about the problem to be solved (e.g., local anatomy) could be used in future systems to aid in automatic segmentation.


    Segmentation requires the selection from either the current appearance of the regions of interest (ROIs). The ROIs quick preview to objects which are physiologically important, like organs or sections of organs. The frameworks can be defined by their edges, whereby edge-detection technologies have been used or through their edges picture construction, where in scenario region-detection techniques are employed. None of these methodologies has indeed been totally successful; parts of the country sometimes have discrete boundaries or institutional concentration that is non-distinctive. Additionally, adjacent areas frequently overlap. These and many other complications end up making segmentation the hardest subtask to the dilemma of image compression-analysis. Since differentiation is complicated for a machine, this is often done by manual process with a mechanical device or by a combined effect of computerized and responsive methodologies between operators. But it continues to be a significant constraint that prohibits further extensive use of image processing methods. That several authors have investigated techniques of artificial intelligence quantum computing mimic such engagement between the subtasks. Some of the higher-level anatomical knowledge which radiologists use it when interpreting pictures is configured to desktop. High ranking organ models therefore provide information to support the segmentation technique at the reduced scale. The essence of the specification defines which one of these subprojects should be carried out, the preference of methodology within each subtask, and indeed the relative order of subprojects. Even though makes learning is an unresolved issue and as many frameworks are feasible, there seem to be an income of methods of image encoding that could be implemented to photographic files. During radiotherapy scheduling accurate segmentation of patient data is a major element in contouring. Computed topography (CT) and magnetic resonance imaging (MR) seem to be the most popularly utilized radiographic methodologies in diagnostic test, clinical trials, and scheduling of treatments. The demarcation of anatomical structures as well as other ROIs ensures adequate datasets. Mathematical models have been used to assist in photo-analysis subtasks productivity. The subtasks of international analysis, segmentation, object identification, and grouping are typically carried out consecutively in classic pattern-recognition technologies. Nevertheless, people tend to do changes due to particular pattern-recognition. Radiologists, for instance, can interpret barely perceptible pictures and detect discrete boundaries, in portion even though they recognize which characteristics individuals are looking for. Many authors have investigated methodologies of artificial intelligence to mimic those very communication between the subtasks. Most of the higher-level anatomical knowledge which radiologists use it when interpreting pictures is configured to the computer. High level tissue frameworks therefore provide feedback to support the image segmentation also at lesser level. The complexity of the requirement defines which one of these subprojects should be carried out, the selection of methodology by each subtask, or the implement change of the subtasks. Even though making learning is indeed an unanswered question as many implementations are conceivable, a prosperity of image analysis techniques is available which could further be allowed to interpret the digital photos that have been analyzed.

  6. What additional informatics issues arise when going from two-dimensional to three-dimensional image processing? What are the three-dimensional versions of two-dimensional image-processing operations such as region growing and edge finding?


    Global processing, segmentation, character recognition, and categorization's fundamental two-dimensional image processing activities make generalizations to extra dimensionality, and therefore are probably part of every image enhancement techniques. Three-dimensional as well as higher-dimensionality pictures, however, give lead to rising information systems problems, including image processing, spatial anatomy interpretation, symbolic morphology recognition, incorporation of virtual and conceptual anatomical depictions in atlases, anatomical variability, and anatomical characterization. All but the first of all these questions deal mainly with anatomic structures, and structural information technology could perhaps be counted as part of the sector. They could also have been supposed to be a part of computer image analysis as well as neuroinformatics.

    Voxels are categorized into contiguous regions throughout region-based segmentation based on the attributes including such brightness frequencies as well as resemblance to adjoining voxels. A widely accepted immediate response to region-based individuals find first classifying voxels into something like a tiny proportion of collagen courses, like gray matter, white matter, cerebrospinal fluid and background, and then using these categories as a premise for further segmentation. Some other region-based method is called growing area, where provinces are manual or automatic positioned throughout selected features through seed voxels. Template matching technicians often further method the regions discovered by these kinds of strategies to eliminate unnecessary linkages and openings.

    Edge-based segmentation is indeed a supplement to regional differentiation; frequency gradients have been used to check for limits of organs and to connect them. Throughout the two-dimensional scenario, adjoining positions on the border are connected by curvature-following methods. In the three-dimensional scenario, methodologies of isosurface-following or marching-cubes integrate boundary voxels into a three-dimensional surface mesh within a province.

  7. What is a three-dimensional brain atlas? What are the methods for registering a patient image volume to that atlas? What is the use of a brain atlas?
  8. Give some example techniques for imaging the function of the brain.
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