The ITIM approach facilitates a collaborative environment in which patients and their family caregivers, physicians, nurses, pharmacists, case managers and others work and share in the process of care.

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Background Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach. Objective To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical entities based only on semantic graph pattern features extracted from biomedical knowledge graphs. Methods We used 7,000 treats and 2,918 causes hand-curated relations from the UMLS Metathesaurus to train and test our models. Our graph pattern features are extracted from simple paths connecting biomedical entities in the SemMedDB graph (based on the well-known SemMedDB database made available by the U.S. National Library of Medicine). Using these graph patterns connecting biomedical entities as features of logistic regression and decision tree models, we computed mean performance measures (precision, recall, F-score) over 100 distinct 80%–20% train-test splits of the datasets. For all experiments, we used a positive:negative class imbalance of 1:10 in the test set to model relatively more realistic scenarios. Results Our models predict treats and causes relations with high F-scores of 99% and 90% respectively. Logistic regression model coefficients also help us identify highly discriminative patterns that have an intuitive interpretation. We are also able to predict some new plausible relations based on false positives that our models scored highly based on our collaborations with two physician co-authors. Finally, our decision tree models are able to retrieve over 50% of treatment relations from a recently created external dataset. Conclusions We employed semantic graph patterns connecting pairs of candidate entities in a knowledge graph as features to predict treatment/causative relations between them. We provide what we believe is the first evidence in direct prediction of biomedical relations based on graph features. Our work complements lexical pattern based approaches in that the graph patterns can be used as additional features for weakly supervised relation prediction.

Spontaneous rectus sheath haematomas and cough secondary to losartan are individually rare conditions. Abdominal wall haematomas present with abdominal pain and abdominal mass. Most patients are managed conservatively; Surgery or embolisation is indicated for shock, infection, rupture into the peritoneum or intractable pain. This is a man aged 65 years presented with dry cough and right-sided abdominal pain. He started losartan a few weeks prior to the onset of cough and had been on rivaroxaban for prior deep venous thrombosis. The right side of his abdomen was distended, bruised and tender. His haemoglobin dropped from 13.3to 9.5 g/dL. CT abdomen/pelvis showed a large 14.5×9.1×4.5 cm haematoma within the right lateral rectus muscle. His only risk factor for developing rectus sheath haematoma was cough in the setting of anticoagulation. Dry cough due to angiotensin receptor blockers is rare, but can have very serious consequences.

Peripherally inserted central catheter (PICC) migration into azygos vein (AV) is a rare complication. It is recognised only when catheter malfunction occurs or when patients develop associated complications. PICC migration into AV has been reported to be associated with various complications such as catheter malfunction, perforation, haemorrhage, thrombosis, infection and stenosis of AV. Pleural effusion and trachea-azygos fistulas have also been reported. We present a patient with recurrent migration of PICC into AV after an initial corrective repositioning during the same hospital stay. In this case, PICC migration was possibly related to left-sided approach, use of smaller diameter PICC, severe congestive heart failure and her bedbound status. PICC migration should be considered when PICC found be malfunctioning, especially if associated with the above risk factors.