Delayed hepatic rupture post ultrasound-guided percutaneous liver biopsy: A case report.

RATIONALE: Hemorrhage, one of complications after liver biopsy, is often identified immediately after the procedure while delayed liver rupture is relatively rare. PATIENT CONCERNS: A 45-year-old woman was diagnosed with undetermined liver cirrhosis and abnormal liver function. To determine the etiology and severity of liver cirrhosis, ultrasound-guided liver biopsy was arranged. The patients did not complain any pain during the procedure. Ultrasound examination on postoperative day1 (POD 1) and MRI on POD 3 showed no evidence of hematoma and ascites. On POD 7, however, the patient was taken to the hospital with a sudden onset of pain in the right upper quadrant of the abdomen. DIAGNOSES: Contrast-enhanced computed tomography revealed liver rupture of right inferior segment of the liver with subcapsular hematoma. INTERVENTIONS: Patient was treated with infusion of 2-unit red blood cell suspension, fluid and hemostatics. OUTCOMES: The vital signs of the patient were stabilized after the therapy. The follow-up ultrasound 1 month later showed a shrunken subcapsular hematoma measuring 4.2 × 2.1 cm at the right lobe. LESSONS: Whenever a liver biopsy procedure is performed, the care should be taken to avoid puncturing those areas that may have liver incisure. Moreover, the patient need to rest for several days and to avoid heavy activities, which is one of the major risk factors for post-procedure bleeding

Object-oriented Neural Programming (OONP) for Document Understanding

We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains. Basically, OONP reads a document and parses it into a predesigned object-oriented data structure (referred to as ontology in this paper) that reflects the domain-specific semantics of the document. An OONP parser models semantic parsing as a decision process: a neural net-based Reader sequentially goes through the document, and during the process it builds and updates an intermediate ontology to summarize its partial understanding of the text it covers. OONP supports a rich family of operations (both symbolic and differentiable) for composing the ontology, and a big variety of forms (both symbolic and differentiable) for representing the state and the document. An OONP parser can be trained with supervision of different forms and strength, including supervised learning (SL) , reinforcement learning (RL) and hybrid of the two. Our experiments on both synthetic and real-world document parsing tasks have shown that OONP can learn to handle fairly complicated ontology with training data of modest sizes.Comment: accepted by ACL 201