Bio-medical Ontologies Maintenance and Change Management

More documents

Recommendations

Info

Design of an Online Physician-Mediated Personal Health Record System 277 The Charting Engine can generate different types of charts. The charts may be viewed chronically by selecting the “yearly” option or in its entirety by selecting ‘All’. In addition, in the current implementation, by default, the patients grants their primary physicians automatic approval to view their health charts and comments made. 5.4 Annotate Personal Health Records There are two types of annotation we will demonstrate. In Fig. 7, panel a, we show a chronicle of blood sugar level changes for the patient. Note the data is integrated first with public database in which the normal range of blood sugar level may be looked up. This lookup content is used to automatically annotate each chronicle event regardless of the level “normal”, “high”, or “low”. When clicked on ‘Saved Notes’ link, the review comments saved by the patient will be displayed. The second type of annotation is provided by user as comments. Next to three blood sugar measurements, there are “Saved Notes” in the Patient’s Comments section. Clicking on the link on September 27, 2006, a patient will see or update his own notes, which can be saved again for future use. Fig. 7. A Health Event Chronicle from PHO the System Showing Blood Sugar Level (a) and Patient's Comments (b) 5.5 Sharing PHR with Physicians By default in this implementation, patients automatically grant their primary physicians permission to view their health charts and comments made in the PHO. Therefore, a physician may be able to view each of his patient’s review comments, in addition to easy-to-understand subsets of the EMR that the physician can already access. This feature facilitates an environment where the physician can (a) (b)
278 J.Y. Chen, S. Taduri, and F. Lloyd Fig. 8. User Interface for Physician to Choose Patients who Shares PHR with him/her study patient medical lab tests and take appropriate decisions on prescribing new tests for the disease that the patient is diagnosed for. Another component is to facilitate broadcast of clinical trial messages to all the patients of the physician. In Fig. 8, we show an example user interface to select a patient from a physician’s account. After the physician clicks on the “View Charts” button, the system displays the same sets of charts and tables, except that physician’s view is read-only. 6 Discussion The physician-mediated personal health office software system is a new method, which aims to benefit patients, physicians, and information technology administrators significantly with the help of PHO software agents. The PHO software agents can help integrate public health data with individual’s medical data from EMRs and personal health history data. The system has built-in security and access control, with robust relational database management system in the backend, and secure web server applications in the frontend. With physician as mediator, especially when future PHO applications are integrated with physician’s daily information management (including email), the small overhead can translate into potentially huge gains for patients, who are excited about managing PHR and EMR together, understanding their own health problems better, and being able to self-care outside of the hospital confinements. Adopting and enforcing accepted standards is an important topic not addressed here. This requires first and foremost the development of a complete data model for representing the structure of the domain covered in this applicaiton. For example, there should be a many-many relationship between patients and physicians as it exists in real world. With this feature, if a patient’s access request is delayed or unfairly denied by one physician, the patient would be able to request another physician to view health information pertaining to a different category. Also important is the mapping of medical terms from different hospitals, patients, and their EMRs/PHRs onto the presentation layer using standards such as the UMLS [8]. To make the prototype increasingly significant and useful, we would be transferring the concepts, design models, and prototype programming codes into the Open Source community. It is necessary that the community of developers are involved in this project. We believe the sharing and social networking potential of
Page 1 and 2:
Amandeep S. Sidhu and Tharam S. Dil
Page 3 and 4:
Amandeep S. Sidhu and Tharam S. Dil
Page 5 and 6:
Preface The molecular biology commu
Page 7 and 8:
Preface VII biomedical systems, and
Page 9 and 10:
X Contents Mining Clinical, Immunol
Page 11 and 12:
2 A.S. Sidhu, M. Bellgard, and T.S.
Page 13 and 14:
Page 15 and 16:
Page 17 and 18:
Page 19 and 20:
Towards Bioinformatics Resourceomes
Page 21 and 22:
Page 23 and 24:
Page 25 and 26:
2 The New Waves Towards Bioinformat
Page 27 and 28:
2.4 Semantic Web Towards Bioinforma
Page 29 and 30:
Page 31 and 32:
Page 33 and 34:
Page 35 and 36:
Page 37 and 38:
Page 39 and 40:
Page 41 and 42:
Page 43 and 44:
A Summary of Genomic Databases: Ove
Page 45 and 46:
Page 47 and 48:
Page 49 and 50:
Page 51 and 52:
Page 53 and 54:
Page 55 and 56:
Page 57 and 58:
Page 59 and 60:
Appendix A Summary of Genomic Datab
Page 61 and 62:
Protein Data Integration Problem Am
Page 63 and 64:
Protein Data Integration Problem 57
Page 65 and 66:
Page 67 and 68:
Page 69 and 70:
Fig. 3. Class Hierarchy of Protein
Page 71 and 72:
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
72 L. Stanescu, D. Dan Burdescu, an
Page 79 and 80:
Page 81 and 82:
Page 83 and 84:
Page 85 and 86:
Page 87 and 88:
Page 89 and 90:
Page 91 and 92:
Page 93 and 94:
Page 95 and 96:
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
Page 103 and 104:
Page 105 and 106:
100 L. Stanescu, D. Dan Burdescu, a
Page 107 and 108:
Page 109 and 110:
Page 111 and 112:
Page 113 and 114:
Page 115 and 116:
Page 117 and 118:
Page 119 and 120:
Page 121 and 122:
Page 123 and 124:
Page 125 and 126:
Page 127 and 128:
Page 129 and 130:
Page 131 and 132:
Page 133 and 134:
Page 135 and 136:
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Bio-medical Ontologies Maintenance
Page 149 and 150:
Page 151 and 152:
Page 153 and 154:
Page 155 and 156:
Page 157 and 158:
Page 159 and 160:
TextToOnto (Maedche and Volz 2001)
Page 161 and 162:
Page 163 and 164:
Page 165 and 166:
Page 167 and 168:
Page 169 and 170:
Page 171 and 172:
Page 173 and 174:
Extraction of Constraints from Biol
Page 175 and 176:
Page 177 and 178:
Page 179 and 180:
Page 181 and 182:
Page 183 and 184:
Page 185 and 186:
Page 187 and 188:
Page 189 and 190:
Page 191 and 192:
Classifying Patterns in Bioinformat
Page 193 and 194:
Page 195 and 196:
Page 197 and 198:
Page 199 and 200:
Page 201 and 202:
Page 203 and 204:
Page 205 and 206:
Page 207 and 208:
Page 209 and 210:
Page 211 and 212:
Page 213 and 214:
Page 215 and 216:
Mining Clinical, Immunological, and
Page 217 and 218:
Page 219 and 220:
Page 221 and 222:
Page 223 and 224:
Page 225 and 226:
Page 227 and 228: Mining Clinical, Immunological, and
Page 241 and 242: Substructure Analysis of Metabolic
Page 253 and 254: entry compound relation subtype com
Page 257 and 258: Running Time 4500 4000 3500 3000 25
Page 263 and 264: 6 Conclusion Substructure Analysis
Page 267 and 268: 266 J.Y. Chen, S. Taduri, and F. Ll
Page 277: 276 J.Y. Chen, S. Taduri, and F. Ll
Page 281 and 282: Completing the Total Wellbeing Puzz
Page 295 and 296: 296 M. Fernandez, M. Villasana, and
Page 315 and 316: Genetic Algorithm in Ab Initio Prot
Page 329 and 330:
Genetic Algorithm in Ab Initio Prot
Page 331 and 332:
Page 333 and 334:
Page 335 and 336:
Page 337 and 338:
Page 339 and 340:
Page 341:
Author Index Apiletti, Daniele 169
show all

Bio-medical Ontologies Maintenance and Change Management

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?