Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation

Machine learning methods play increasingly important roles in pre-procedural planning for complex surgeries and interventions. Very often, however, researchers find the historical records of emerging surgical techniques, such as the transcatheter aortic valve replacement (TAVR), are highly scarce in quantity. In this paper, we address this challenge by proposing novel generative invertible networks (GIN) to select features and generate high-quality virtual patients that may potentially serve as an additional data source for machine learning. Combining a convolutional neural network (CNN) and generative adversarial networks (GAN), GIN discovers the pathophysiologic meaning of the feature space. Moreover, a test of predicting the surgical outcome directly using the selected features results in a high accuracy of 81.55%, which suggests little pathophysiologic information has been lost while conducting the feature selection. This demonstrates GIN can generate virtual patients not only visually authentic but also pathophysiologically interpretable

Predictors of outcomes in diabetic foot osteomyelitis treated initially with conservative (nonsurgical) medical management: A retrospective study

The optimal way to manage diabetic foot osteomyelitis remains uncertain, with debate in the literature as to whether it should be managed conservatively (ie, nonsurgically) or surgically. We aimed to identify clinical variables that influence outcomes of nonsurgical management in diabetic foot osteomyelitis. We conducted a retrospective study of consecutive patients with diabetes presenting to a tertiary center between 2007 and 2011 with foot osteomyelitis initially treated with nonsurgical management. Remission was defined as wound healing with no clinical or radiological signs of osteomyelitis at the initial or contiguous sites 12 months after clinical and/or radiological resolution. Nine demographic and clinical variables including osteomyelitis site and presence of foot pulses were analyzed. We identified 100 cases, of which 85 fulfilled the criteria for analysis. After a 12-month follow-up period, 54 (63.5%) had achieved remission with nonsurgical management alone with a median (interquartile range) duration of antibiotic treatment of 10.8 (10.1) weeks. Of these, 14 (26%) were admitted for intravenous antibiotics. The absence of pedal pulses in the affected foot (n = 34) was associated with a significantly longer duration of antibiotic therapy to achieve remission, 8.7 (7.1) versus 15.9 (13.3) weeks (P = .003). Osteomyelitis affecting the metatarsal was more likely to be amputated than other sites of the foot (P = .016). In line with previous data, we have shown that almost two thirds of patients presenting with osteomyelitis healed without undergoing surgical bone resection

Predicting Pancreatic Cancer Using Support Vector Machine

This report presents an approach to predict pancreatic cancer using Support Vector Machine Classification algorithm. The research objective of this project it to predict pancreatic cancer on just genomic, just clinical and combination of genomic and clinical data. We have used real genomic data having 22,763 samples and 154 features per sample. We have also created Synthetic Clinical data having 400 samples and 7 features per sample in order to predict accuracy of just clinical data. To validate the hypothesis, we have combined synthetic clinical data with subset of features from real genomic data. In our results, we observed that prediction accuracy, precision, recall with just genomic data is 80.77%, 20%, 4%. Prediction accuracy, precision, recall with just synthetic clinical data is 93.33%, 95%, 30%. While prediction accuracy, precision, recall for combination of real genomic and synthetic clinical data is 90.83%, 10%, 5%. The combination of real genomic and synthetic clinical data decreased the accuracy since the genomic data is weakly correlated. Thus we conclude that the combination of genomic and clinical data does not improve pancreatic cancer prediction accuracy. A dataset with more significant genomic features might help to predict pancreatic cancer more accurately