Recurrent neural networks for churn prediction

Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2018, Director: Jordi Vitrià i Marca i Salvador Torra Porras[en] This project is based on a probabilistic Deep learning model called WTTE-RNN that applies recurrent neural networks along with survival analysis in order to model the distribution of time between specific events. The main motivation of the application of survival analysis is its adjustment to recurrent events, unlike the basic hypothesis of this theory which assumes that the existence of one event implies the end of data entry. In order to understand the main parts that constitute the model, an extensive section of this project addresses Deep learning and Survival Analysis. The approach of the model as a business tool for churn prediction is also important, in order to show how the knowledge acquired during the Mathematics degree can serve as a tool in the business strategy direction and so as a link with the Business degree

Deep Landscape Forecasting for Real-time Bidding Advertising

The emergence of real-time auction in online advertising has drawn huge attention of modeling the market competition, i.e., bid landscape forecasting. The problem is formulated as to forecast the probability distribution of market price for each ad auction. With the consideration of the censorship issue which is caused by the second-price auction mechanism, many researchers have devoted their efforts on bid landscape forecasting by incorporating survival analysis from medical research field. However, most existing solutions mainly focus on either counting-based statistics of the segmented sample clusters, or learning a parameterized model based on some heuristic assumptions of distribution forms. Moreover, they neither consider the sequential patterns of the feature over the price space. In order to capture more sophisticated yet flexible patterns at fine-grained level of the data, we propose a Deep Landscape Forecasting (DLF) model which combines deep learning for probability distribution forecasting and survival analysis for censorship handling. Specifically, we utilize a recurrent neural network to flexibly model the conditional winning probability w.r.t. each bid price. Then we conduct the bid landscape forecasting through probability chain rule with strict mathematical derivations. And, in an end-to-end manner, we optimize the model by minimizing two negative likelihood losses with comprehensive motivations. Without any specific assumption for the distribution form of bid landscape, our model shows great advantages over previous works on fitting various sophisticated market price distributions. In the experiments over two large-scale real-world datasets, our model significantly outperforms the state-of-the-art solutions under various metrics.Comment: KDD 2019. The reproducible code and dataset link is https://github.com/rk2900/DL

A Recurrent Neural Network Survival Model: Predicting Web User Return Time

The size of a website's active user base directly affects its value. Thus, it is important to monitor and influence a user's likelihood to return to a site. Essential to this is predicting when a user will return. Current state of the art approaches to solve this problem come in two flavors: (1) Recurrent Neural Network (RNN) based solutions and (2) survival analysis methods. We observe that both techniques are severely limited when applied to this problem. Survival models can only incorporate aggregate representations of users instead of automatically learning a representation directly from a raw time series of user actions. RNNs can automatically learn features, but can not be directly trained with examples of non-returning users who have no target value for their return time. We develop a novel RNN survival model that removes the limitations of the state of the art methods. We demonstrate that this model can successfully be applied to return time prediction on a large e-commerce dataset with a superior ability to discriminate between returning and non-returning users than either method applied in isolation.Comment: Accepted into ECML PKDD 2018; 8 figures and 1 tabl

Long-Term Experience of Chemoradiotherapy Combined with Deep Regional Hyperthermia for Organ Preservation in High-Risk Bladder Cancer (Ta, Tis, T1, T2).

BackgroundThe aim of this study was to evaluate the efficacy and safety of chemoradiotherapy (RCT) combined with regional deep hyperthermia (RHT) of high-risk bladder cancer after transurethral resection of bladder tumor (TUR-BT).Materials and methodsBetween 1982 and 2016, 369 patients with pTa, pTis, pT1, and pT2 cN0-1 cM0 bladder cancer were treated with a multimodal treatment after TUR-BT. All patients received radiotherapy (RT) of the bladder and regional lymph nodes. RCT was administered to 215 patients, RCT + RHT was administered to 79 patients, and RT was used in 75 patients. Treatment response was evaluated 4-6 weeks after treatment with TUR-BT.ResultsComplete response (CR) overall was 83% (290/351), and in treatment groups was RT 68% (45/66), RCT 86% (178/208), and RCT + RHT 87% (67/77). CR was significantly improved by concurrent RCT compared with RT (odds ratio [OR], 2.32; 95% confidence interval [CI], 1.05-5.12; p = .037), less influenced by hyperthermia (OR, 2.56; 95% CI, 0.88-8.00; p = .092). Overall survival (OS) after RCT was superior to RT (hazard ratio [HR], 0.7; 95% CI, 0.50-0.99; p = .045). Five-year OS from unadjusted Kaplan-Meier estimates was RCT 64% versus RT 45%. Additional RHT increased 5-year OS to 87% (HR, 0.32; 95% CI, 0.18-0.58; p = .0001). RCT + RHT compared with RCT showed a significantly better bladder-preservation rate (HR, 0.13; 95% CI, 0.03-0.56; p = .006). Median follow-up was 71 months. The median number of RHT sessions was five.ConclusionThe multimodal treatment consisted of a maximal TUR-BT followed by RT; concomitant platinum-based chemotherapy combined with RHT in patients with high-grade bladder cancer improves local control, bladder-preservation rate, and OS. It offers a promising alternative to surgical therapies like radical cystectomy.Implications for practiceRadical cystectomy with appropriate lymph node dissection has long represented the standard of care for muscle-invasive bladder cancer in medically fit patients, despite many centers reporting excellent long-term results for bladder preserving strategies. This retrospective analysis compares different therapeutic modalities in bladder-preservation therapy. The results of this study show that multimodal treatment consisting of maximal transurethral resection of bladder tumor followed by radiotherapy, concomitant platinum-based chemotherapy combined with regional deep hyperthermia in patients with Ta, Tis, T1-2 bladder carcinomas improves local control, bladder-preservation rate, and survival. More importantly, these findings offer a promising alternative to surgical therapies like radical cystectomy. The authors hope that, in the future, closer collaboration between urologists and radiotherapists will further improve treatments and therapies for the benefit of patients

Cancer and thrombosis: Managing the risks and approaches to thromboprophylaxis

Patients with cancer are at increased risk of venous thromboembolism (VTE) compared with patients without cancer. This results from both the prothrombotic effects of the cancer itself and iatrogenic factors, such as chemotherapy, radiotherapy, indwelling central venous devices and surgery, that further increase the risk of VTE. Although cancer-associated thrombosis remains an important cause of morbidity and mortality, it is often underdiagnosed and undertreated. However, evidence is accumulating to support the use of low-molecular-weight heparins (LMWHs) in the secondary prevention of VTE in patients with cancer. Not only have LMWHs been shown to be at least as effective as coumarin derivatives in this setting, but they have a lower incidence of complications, including bleeding, and are not associated with the practical problems of warfarin therapy. Furthermore, a growing number of studies indicate that LMWHs may improve survival among patients with cancer due to a possible antitumor effect. Current evidence suggests that LMWHs should increasingly be considered for the long-term management of VTE in patients with cancer

SAFE: A Neural Survival Analysis Model for Fraud Early Detection

Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform. How to detect fraudsters in time is a challenging problem. Most of the existing approaches adopt classifiers to predict fraudsters given their activity sequences along time. The main drawback of classification models is that the prediction results between consecutive timestamps are often inconsistent. In this paper, we propose a survival analysis based fraud early detection model, SAFE, which maps dynamic user activities to survival probabilities that are guaranteed to be monotonically decreasing along time. SAFE adopts recurrent neural network (RNN) to handle user activity sequences and directly outputs hazard values at each timestamp, and then, survival probability derived from hazard values is deployed to achieve consistent predictions. Because we only observe the user suspended time instead of the fraudulent activity time in the training data, we revise the loss function of the regular survival model to achieve fraud early detection. Experimental results on two real world datasets demonstrate that SAFE outperforms both the survival analysis model and recurrent neural network model alone as well as state-of-the-art fraud early detection approaches.Comment: To appear in AAAI-201

Hyperthermia combined with chemotherapy - Biological rationale, clinical application, and treatment results

There is substantial evidence from preclinical data that the antitumor cytotoxicity of selected chemotherapeutic agents either alone or combined with radiation can be enhanced by appropriate heat exposure (40-44 degrees C) of cells or tumor tissues. Based upon these results the integration of hyperthermia as an additional treatment modality, given simultaneously with systemic chemotherapy or in combination with radiochemotherapy, is currently tested at the clinic. Regional hyperthermia combined with chemotherapy or radiochemotherapy showed impressive results (phase II studies) at clinical relevant temperatures in locally advanced tumors of different entities in terms of objective response rate, local tumor control and relapse-free survival. Clinical protocols of well-designed phase III trials on combined treatment modalities integrating hyperthermia are rather limited but for some tumors confirm its clinical benefit. In general, the clinical approach to use hyperthermia has gained much more interest within in the field of medical oncology. One of the major reason is the substantial technical improvements made with the available commercial equipment for local or regional heating, especially in case of deep-seated lesions or systemic heating. Further testing of the potential of hyperthermia combined with chemotherapy or radiochemotherapy in prospective randomized trials are warranted. At this time, hyperthermia as an adjunct to conventional treatment strategies is recommended in the setting of clinical protocols. The results of prospective trials should answer the question for which types of local advanced or metastatic tumors hyperthermia becomes standard as part of a multi-modal treatment strategy