Mining Medical Data: Bridging the Knowledge Divide

Due to the signi¯cant amount of data generated by modern medicine there is a growing reliance on tools such as data mining and knowledge discovery to help make sense and comprehend such data. The success of this process requires collaboration and interaction between such methods and medical professionals. Therefore an important question is: How can we strengthen the relationship between two traditionally separate fields (technology and medicine) in order to work simultaneously towards enhancing knowledge in modern medicine. To address this question, this study examines the application of data mining techniques to a large asthma medical dataset. A discussion introducing various methods for a smooth approach, straying from the `jack of all trades, master of none' to a modular cooperative approach for a successful outcome is pro-posed. The results of this study support the use of data mining as a useful tool and highlight the advantages on a global scale of closer relations between the two distinct fields. The exploration of CRISP methodology suggests that a `one methodology fits all approach' is not appropriate, but rather combines to create a hybrid holistic approach to data mining

Two-Stage Fine-Tuning: A Novel Strategy for Learning Class-Imbalanced Data

Classification on long-tailed distributed data is a challenging problem, which suffers from serious class-imbalance and hence poor performance on tail classes with only a few samples. Owing to this paucity of samples, learning on the tail classes is especially challenging for the fine-tuning when transferring a pretrained model to a downstream task. In this work, we present a simple modification of standard fine-tuning to cope with these challenges. Specifically, we propose a two-stage fine-tuning: we first fine-tune the final layer of the pretrained model with class-balanced reweighting loss, and then we perform the standard fine-tuning. Our modification has several benefits: (1) it leverages pretrained representations by only fine-tuning a small portion of the model parameters while keeping the rest untouched; (2) it allows the model to learn an initial representation of the specific task; and importantly (3) it protects the learning of tail classes from being at a disadvantage during the model updating. We conduct extensive experiments on synthetic datasets of both two-class and multi-class tasks of text classification as well as a real-world application to ADME (i.e., absorption, distribution, metabolism, and excretion) semantic labeling. The experimental results show that the proposed two-stage fine-tuning outperforms both fine-tuning with conventional loss and fine-tuning with a reweighting loss on the above datasets.Comment: 20 pages, 6 figure

Short-term surgical and long-term survival outcomes after laparoscopic distal gastrectomy with D2 lymphadenectomy for gastric cancer

BACKGROUND: Laparoscopic distal gastrectomy (LDG) for gastric cancer has gradually gained popularity. However, the long-term oncological outcomes of LDG have rarely been reported. This study aimed to investigate the survival outcomes of LDG, and evaluate the early surgical outcomes of laparoscopy-assisted distal gastrectomy (LADG) and totally laparoscopic distal gastrectomy (TLDG). METHODS: Clinical outcomes of 240 consecutive patients with gastric cancer who underwent LDG at our institution between October 2004 and April 2013 were analyzed. Early surgical outcomes of LADG and TLDG were compared and operative experiences were evaluated. RESULTS: Of the 240 patients, 93 underwent LADG and 147 underwent TLDG. There were 109 T1, 36 T2, 31 T3, and 64 T4a lesions. The median follow-up period was 31.5 months (range: 4–106 months). Tumor recurrence was observed in 40 patients and peritoneal recurrence was observed most commonly. The 5-year disease-free survival (DFS) and overall survival (OS) rates according to tumor stage were 90.3% and 93.1% in stage I, 72.7% and 67.6% in stage II, and 34.8% and 41.5% in stage III, respectively. No significant differences in early surgical outcomes were noted such as operation time, blood loss and postoperative recovery between LADG and TLDG (P >0.05). CONCLUSIONS: LDG for gastric cancer had acceptable long-term oncologic outcomes. The early surgical outcomes of the two commonly used LDG methods were similar

The Dynamical Effects of a Large-Scale Ordered Magnetic Field on Slim Disks

National Basic Research Program of China [2009CB824800]; National Natural Science Foundation of China [11143003, 11233006, 11073015]; Natural Science Foundation of Fujian Province [2011J01023]The dynamics of slim disk under the influence of a large-scale ordered magnetic field is investigated. The global solutions show that the radial velocity increases and the disk temperature decreases with enhancing magnetic field. The fraction of mass loss becomes smaller when the accretion rate is higher. The ratio of the jet kinetic power to disk luminosity is less than 0.1, which indirectly supports the argument that radio-loud narrow-line Seyfert 1 galaxies share similarities with blazars

Ample Pairs

We show that the ample degree of a stable theory with trivial forking is preserved when we consider the corresponding theory of belles paires, if it exists. This result also applies to the theory of $H$-structures of a trivial theory of rank $1$.Comment: Research partially supported by the program MTM2014-59178-P. The second author conducted research with support of the programme ANR-13-BS01-0006 Valcomo. The third author would like to thank the European Research Council grant 33882