Feedbackdriven radiologyreportretrieval ichi2015-v2
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Feedback Drive Radiology Exam Report Retrieval with Semantics
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Feedbackdriven radiologyreportretrieval ichi2015-v2
- 1. Feedback-Driven Radiology Exam Report Retrieval with Semantics Sarasi Lalithsena Amit Sheth Kno.e.sis Center Wright State University Luis Tari Steven Gustafson GE Global Research Ann von Reden Benjamin Wilson Brian Kolowitz UPMC Enterprises John Kalafut GE Healthcare
- 2. 2 Motivation Reason for exam Secondary diagnoses related by anatomical region or disease type Follow up exam for an existing condition Specific hypothesis or inquiry mentioned A radiologists will be interested to know the background,
- 3. 3 Problem Reason for exam: Foot pain Vast amount of data EMR, Radiology report R1:………………………. R2:………………………. R3:History Diabetes, Smoker …………………………… …………………………… Rn:........................................... ….. R3:History Diabetes, Smoker ……
- 4. 4 Challenges • Capture the semantics to find relevant data Patient X : Current Study Reason for Exam: Foot Pain ………………………………………………………………………………… R1 : Lower Extremity Venous Insufficient Doppler Ultra sound R2 : History: Diabetes Former Smoker, History of Hypertension RX : Generalized Abdominal Pain …………………………………………………………………………………… …………………………………………………………………………………… Patient X : Prior Reports Lower Extremity Venous Insufficient Doppler Ultra sound History: Diabetes Former Smoker, History of Hypertension Generalized Abdominal Pain Foot partOf Lower Extremity Foot Pain associatedTo Diabetes • Lexical Similarity Measures falls behind when there is no lexical similarity • Knowledge-based similarity measures only uses taxonomical relationships
- 5. 5 Challenges • Personalize the relevancy based on radiologist's need Patient X : Current Study Reason for Exam: XR Chest, Rib Fracture Abdomen X-RAY MRI CHEST WALL Neuro Specialist Musculoskeletal Specialist More Relevant More Relevant
- 6. 6 Our Approach Capture the semantics Knowledgebase and Semantic Similarity Personalized the relevancy Explicit Relevant Feedback Semantic Vector Builder Similarity Calculator …………………… R1 : Lower Extremity R2 : History: Diabetes …………….. ……………… Prior Reports …………………… R1 R2 Prior Report Vectors Reason for exam vector Feedback Incorporator R1 : Lower Extremity R2 : History: Diabetes …………
- 7. 7 Our Approach – Semantic Vector Creation • Uses RadLex Ontology to capture semantic relationships with NLP techniques CT THORAX WITHOUT CONTRAST Reason for Exam: Chest pain Reason for Exam: Foot pain Lower Extremity Venous Insufficient Doppler Ultra sound
- 8. 8 Our Approach – Semantic Vector Creation • Creates the vector of concepts using semantic similarity methods for each relationship 𝑺𝒊𝒎 𝒄𝒊, 𝒄𝒎 = 𝟎, 𝒊𝒇 𝑷𝒂𝒕𝒉𝑳𝒆𝒏𝒈𝒕𝒉 𝒄𝒊, 𝒄𝒎 = 𝟎 𝟏 𝑷𝒂𝒕𝒉𝑳𝒆𝒏𝒈𝒕𝒉 𝒄𝒊, 𝒄𝒎 , 𝑶𝒕𝒉𝒆𝒓𝒘𝒊𝒔𝒆 Lower Extremity Venous Insufficient Doppler Ultra sound 1 0.5 bpi bpj bpi HasPart bpj bpi – Lower Extremity bpj - Foot
- 9. 9 Our Approach – Semantic Vector Creation • Handles multiple relation 𝒕𝒊 = 𝒎𝒂𝒙(𝑯𝒂𝒔𝑷𝒂𝒓𝒕𝒊, 𝑰𝒔𝒂𝒊, … . 𝑪𝒂𝒖𝒔𝒆𝒔𝒊) Lower Extremity Venous Insufficient Doppler Ultra sound 1 0.5 1 0 1 0 0.2 0.02 HasPart Isa Causes 1 0.5 0.020.2
- 10. 10 Our Approach: Explicit Relevance Feedback • Explicit relevance feedback is used in information retrieval to improve the relevance of the results • Useful when users have a general conception on what they are looking for • Adopted an existing algorithm “Rocchio Algorithm” to work with the ratings provided by the users * ! * * * ! * * * ! ! * Relevant Record ! Non-Relevant Record OO MO Original Query Vector Modified Query Vector
- 11. 11 Our Approach: Explicit Relevance Feedback Modified Rocchio query vector 𝒒 𝒎 = 𝒂𝒒 𝒐 + 𝒃 𝟏 𝑫 𝒓 𝒊=𝟏 𝟓 𝒘𝒊 𝒅 𝒋ε𝑫 𝒊 𝒅𝒋 + 𝒄 𝟏 𝑫 𝒏𝒓 𝒅 𝒌ε𝑫 𝒏𝒓 𝒅 𝒌 𝑨 = 𝑫 𝟏 𝑫 𝟐 𝑫 𝟑 𝑫 𝟒 𝑫 𝟓 qm - modified query vector qo - original query vector Dr - relevant patient records Dnr - non relevant patient records Di - patient records rated as i a = 1 b = 0.6 c = 0.1
- 12. 12 Evaluation
- 13. Thank You! Questions? http://knoesis.wright.edu/researchers/sarasi sarasi@knoesis.org Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing Wright State University, Dayton, Ohio, USA
Editor's Notes
- Short reason for exam, limited interaction with the patient [Add one sentence about the personalization aspect]
- Radiologists typically spend only (1 – 5 ) minutes for X-Ray and 20 – 30 minutes In MRI data The larger the time they spent on the EMR, they will have less in interpreting the results EMR tends to be oriented towards producing physician and specialty reports and not for radiology work flow needs
- [Some explanation]
- This simulates the process of user changing the original query by looking at the results. User's search query is revised to include an arbitrary percentage of relevant and non-relevant documents as a means of increasing the search engine's recall, and possibly the precision as well
- [Can we make this little more clear] modify the query vector in a direction closer to related document and farther away to non-related documents values for b and c should be incremented or decremented proportionally to the set of documents classified by the user If the user decides that the modified query should not contain terms from either the original query, related documents, or non-related documents, then the corresponding weight (a, b, c) value for the category should be set to 0. Similar to the nearest centroid classifier in classification models As the weights are increased or decreased for a particular category of documents, the coordinates for the modified vector begin to move either closer, or farther away, from the centroid of the document collection. Thus if the weight is increased for related documents, then the modified vectors coordinates will reflect being closer to the centroid of related documents. optimal query is the vector difference between the centroids of the relevant and nonrelevant documents Limitation - what it could not work because we do not the set of full relevant documents
- R-Precision is the precision after R documents have been retrieved, where R is the number of relevant documents for the topic. R-precision refers to the best precision on the precision/recall curve when considering a set of rel relevant documents and looking at the rel first answers. Rank cutoff R is the point at which precision and recall are equal, since at that point both are r/R