An Infrastructure for Clinical Data Extraction, Medical Knowledge and Mining Services Sharing<br />Xiao-Ou Ping2, Mei-Shu ...
Introduction<br />More and more adoption of electronic health record (EHR) systems<br />The enormous healthcare records wi...
Infrastructure<br />3<br />Data Pre-processing Module<br />Clinical Data Warehouse<br />Analyzing Module<br />Aim:<br />Cl...
Result<br />The infrastructure is adopted in our three-year project relevant to liver cancer to assist domain experts for<...
Conclusion<br />The same work flow of this infrastructure can be reused to facilitate data gathering, information extracti...
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An infrastructure for clinical data extraction, medical knowledge and mining services sharing

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An infrastructure for clinical data extraction, medical knowledge and mining services sharing

  1. 1. An Infrastructure for Clinical Data Extraction, Medical Knowledge and Mining Services Sharing<br />Xiao-Ou Ping2, Mei-Shu Lai4, Ching-Ting Tan6, Ja-Der Liang5, Hung-Chang Lee7, Chi-Huang Chen3, Yi-Ju Tseng1, Zi-Jun Wang1, and Feipei Lai123<br />1. Graduate Institute of Biomedical Electronic and Bioinformatics &<br />2. Graduate Institute of Computer Science and Information Engineering &<br />3. Graduate Institute of Electronics Engineering<br />National Taiwan University<br />4. Institute of Preventive Medicine &<br />5. Department of Internal Medicine &<br />6. Department of Otolaryngology<br />National Taiwan University Hospital<br />7. Graduate Institute of Information Management<br />Tamkang University<br />
  2. 2. Introduction<br />More and more adoption of electronic health record (EHR) systems<br />The enormous healthcare records will be accumulated in the clinical data repository <br />We proposed an infrastructure for facilitating quality improvement of healthcare through<br />Gathering clinical data<br />Discovering knowledge from the gathered data<br />Sharing the knowledge and mining modules services<br />The ontology-based methodologies can be adopted for <br />Extracting and querying information <br />The guideline representation languages can be involved to present the discovered knowledge<br />e.g. GLIF, Arden syntax, Asbru, PROforma, EON, and PRODIGY<br />2<br />
  3. 3. Infrastructure<br />3<br />Data Pre-processing Module<br />Clinical Data Warehouse<br />Analyzing Module<br />Aim:<br />Clinical data extraction<br />Facilitate the process of clinical data extraction from clinical data repositories <br />Through clinical data handler<br />Medical knowledge and mining services sharing<br />Share the mining modules<br />Through data analyzing service provider<br />Share the standardized medical knowledge<br />Through knowledge sharing services provider<br />Web-based Service Module<br />Data Analyzing ServicesProvider<br />Clinical Data Handler<br />Specific Cases Filter<br />Knowledge Base Repository<br />Query Interface<br />Information Extracting Module<br />Representation Module<br />Data Gathering Module<br />Execution Engine<br />Web-based Service Module<br />Clinical Data Repository<br />Knowledge Sharing Services Provider<br />
  4. 4. Result<br />The infrastructure is adopted in our three-year project relevant to liver cancer to assist domain experts for<br />Gathering and extracting the structuralized and non-structuralized clinical data relevant to liver cancer<br />Gather data: e.g. Laboratory tests, medication records, administration summary, discharge summary, operation reports, and medical image reports<br />Extract information: e.g. X-Ray, CT, and MRI free text reports<br />Selecting specific cases and translating cases into specific format for mining modules <br />e.g. SVM (Support Vector Machine)<br />Comparing the treatment strategies of liver cancer between<br />Existed clinical practice guideline of liver cancer<br />Analyzed results from mining modules<br />4<br />
  5. 5. Conclusion<br />The same work flow of this infrastructure can be reused to facilitate data gathering, information extracting, and knowledge discovering on different research subjects<br />e.g. Comparing the clinical pathways, finding the impact factors and rules for disease diagnosis, or predicting the disease staging classification<br />The analyzing modules can be shared with other institutions for <br />Reusing the developed analyzing modules<br />Verifying the analyzing modules using different clinical data<br />The discovered knowledge can be shared with other institutions and individuals for<br />Reusing and verifying the clinical guidelines<br />Retrieving the medical related suggestions through the execution engine<br />5<br />

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