Churn prediction based on text mining and CRM data analysis

Within quantitative marketing, churn prediction on a single customer level has become a major issue. An extensive body of literature shows that, today, churn prediction is mainly based on structured CRM data. However, in the past years, more and more digitized customer text data has become available, originating from emails, surveys or scripts of phone calls. To date, this data source remains vastly untapped for churn prediction, and corresponding methods are rarely described in literature. Filling this gap, we present a method for estimating churn probabilities directly from text data, by adopting classical text mining methods and combining them with state-of-the-art statistical prediction modelling. We transform every customer text document into a vector in a high-dimensional word space, after applying text mining pre-processing steps such as removal of stop words, stemming and word selection. The churn probability is then estimated by statistical modelling, using random forest models. We applied these methods to customer text data of a major Swiss telecommunication provider, with data originating from transcripts of phone calls between customers and call-centre agents. In addition to the analysis of the text data, a similar churn prediction was performed for the same customers, based on structured CRM data. This second approach serves as a benchmark for the text data churn prediction, and is performed by using random forest on the structured CRM data which contains more than 300 variables. Comparing the churn prediction based on text data to classical churn prediction based on structured CRM data, we found that the churn prediction based on text data performs as well as the prediction using structured CRM data. Furthermore we found that by combining both structured and text data, the prediction accuracy can be increased up to 10%. These results show clearly that text data contains valuable information and should be considered for churn estimation

Digestive enzyme replacement relieves growth failure in preterm infants with poor exocrine pancreatic function: a retrospective case series

In orally fed preterm infants, poor weight gain may be linked to low fecal pancreatic elastase-1 (FPE-1) activity, indicative of exocrine pancreatic insufficiency. The objective of this study was the retrospective assessment of the effect of exogenous digestive enzyme replacement by gavage in preterm infants with growth failure and low FPE-1 (<200 mu g/g). We analyzed weight gain relative to baseline and caloric intake during 14-day periods before and after institution of digestive enzyme replacement containing 6000 U lipase and 240 U protease kg(-1) d(-1). Among 46 of 132 preterm infants < 1250g birth weight surviving to at least 14 days in whom FPE-1 was determined, 38 infants had low FPE-1 (< 200 mu g/g), and 33 infants received exogenous digestive enzyme replacement. Average daily weight gain significantly increased from 14.4 [range 2.6-22.4] g kg(-1) d(-1) to 17.4 [8.4-29.0] g kg(-1) d(-1) (P = 0.001), as did weight gain per kcal, from 0.08 [0.02-0.13] g kcal(-1) d(-1) to 0.11 [0.05-0.18] g kcal(-1) d(-1). Conclusion: In preterm infants with signs and symptoms of exocrine pancreatic insufficiency, exogenous digestive enzyme replacement is associated with improved growth. What is Known: center dot Very preterm infants on full enteral nutrition may display growth failure linked to transient poor exocrine pancreatic function. center dot Porcine pancreatic enzymes covered with an acid-resistant coating are too large to pass the internal diameter of most gavage tubes used in very preterm infants. What is New: center dot Administration of a liquid formulation of acid-resistant microbial digestive enzymes in preterm infants with growth failure and low fecal pancreatic elastase-1 values was associated with improved weight gain. center dot Response to exogenous digestive enzyme replacement was associated with the prior extent of growth failure

Conformational States of the CXCR4 Inhibitor Peptide EPI-X4—A Theoretical Analysis

EPI-X4, an endogenous peptide inhibitor, has exhibited potential as a blocker of CXCR4—a G protein-coupled receptor. This unique inhibitor demonstrates the ability to impede HIV-1 infection and halt CXCR4-dependent processes such as tumor cell migration and invagination. Despite its promising effects, a comprehensive understanding of the interaction between EPI-X4 and CXCR4 under natural conditions remains elusive due to experimental limitations. To bridge this knowledge gap, a simulation approach was undertaken. Approximately 150,000 secondary structures of EPI-X4 were subjected to simulations to identify thermodynamically stable candidates. This simulation process harnessed a self-developed reactive force field operating within the ReaxFF framework. The application of the Two-Phase Thermodynamic methodology to ReaxFF facilitated the derivation of crucial thermodynamic attributes of the EPI-X4 conformers. To deepen insights, an ab initio density functional theory calculation method was employed to assess the electrostatic potentials of the most relevant (i.e., stable) EPI-X4 structures. This analytical endeavor aimed to enhance comprehension of the inhibitor’s structural characteristics. As a result of these investigations, predictions were made regarding how EPI-X4 interacts with CXCR4. Two pivotal requirements emerged. Firstly, the spatial conformation of EPI-X4 must align effectively with the CXCR4 receptor protein. Secondly, the functional groups present on the surface of the inhibitor’s structure must complement the corresponding features of CXCR4 to induce attraction between the two entities. These predictive outcomes were based on a meticulous analysis of the conformers, conducted in a gaseous environment. Ultimately, this rigorous exploration yielded a suitable EPI-X4 structure that fulfills the spatial and functional prerequisites for interacting with CXCR4, thus potentially shedding light on new avenues for therapeutic development

Data-Driven Supervised Learning for Life Science Data

Life science data are often encoded in a non-standard way by means of alpha-numeric sequences, graph representations, numerical vectors of variable length, or other formats. Domain-specific or data-driven similarity measures like alignment functions have been employed with great success. The vast majority of more complex data analysis algorithms require fixed-length vectorial input data, asking for substantial preprocessing of life science data. Data-driven measures are widely ignored in favor of simple encodings. These preprocessing steps are not always easy to perform nor particularly effective, with a potential loss of information and interpretability. We present some strategies and concepts of how to employ data-driven similarity measures in the life science context and other complex biological systems. In particular, we show how to use data-driven similarity measures effectively in standard learning algorithms

Structure Preserving Encoding of Non-euclidean Similarity Data

Domain-specific proximity measures, like divergence measures in signal processing or alignment scores in bioinformatics, often lead to non-metric, indefinite similarities or dissimilarities. However, many classical learning algorithms like kernel machines assume metric properties and struggle with such metric violations. For example, the classical support vector machine is no longer able to converge to an optimum. One possible direction to solve the indefiniteness problem is to transform the non-metric (dis-)similarity data into positive (semi-)definite matrices. For this purpose, many approaches have been proposed that adapt the eigenspectrum of the given data such that positive definiteness is ensured. Unfortunately, most of these approaches modify the eigenspectrum in such a strong manner that valuable information is removed or noise is added to the data. In particular, the shift operation has attracted a lot of interest in the past few years despite its frequently reoccurring disadvantages. In this work, we propose a modified advanced shift correction method that enables the preservation of the eigenspectrum structure of the data by means of a low-rank approximated nullspace correction. We compare our advanced shift to classical eigenvalue corrections like eigenvalue clipping, flipping, squaring, and shifting on several benchmark data. The impact of a low-rank approximation on the data’s eigenspectrum is analyzed.</p

Acquired heart block: A possible complication of patent ductus arteriosus in a preterm infant

A large patent ductus arteriosus (PDA) is a frequently encountered clinical problem in extremely low birth weight (ELBW) infants. It leads to an increased pulmonary blood flow and in a decreased or reversed diastolic flow in the systemic circulation, resulting in complications. Here we report a possible complication of PDA not previously published. On day 8 of life, a male ELBW infant (birth weight 650 g) born at a gestational age of 23 weeks and 3 days developed an atrioventricular block (AV block). The heart rate dropped from 168/min to 90/min, and the ECG showed a Wenckebach second-degree AV block and intraventricular conduction disturbances. Echocardiography demonstrated a PDA with a large left-to-right shunt and large left atrium and left ventricle with high contractility. Within several minutes after surgical closure of the PDA, the heart rate increased, and after 30 min the AV block had improved to a 1: 1 conduction ratio. Echocardiography after 2 h revealed a significant decrease of the left ventricular and atrial dimensions. Within 12 h, the AV block completely reversed together with the intraventricular conduction disturbances. We suggest that PDA with a large left-to-right shunt and left ventricular volume overload may lead to an AV block in an ELBW infant. Surgical closure of the PDA may be indicated. Copyright (C) 2007 S. Karger AG, Basel

Non‐invasive and tracheostomy invasive ventilation in amyotrophic lateral sclerosis: Utilization and survival rates in a cohort study over 12 years in Germany

Background and purpose: The aim of this study was to investigate utilization rates, treatment pathways and survival prognosis in patients with amyotrophic lateral sclerosis (ALS) undergoing non-invasive (NIV) and tracheostomy invasive ventilation (TIV) in a real-world setting. Methods: A prospective cohort study using a single-centre register of 2702 ALS patients (2007 to 2019) was conducted. Utilization of NIV/TIV and survival data were analysed in three cohorts: (i) non-NIV; (ii) NIV (NIV without subsequent TIV); and (iii) TIV (including TIV preceded by NIV). Results: A total of 1720 patients with available data were identified, 72.0% of whom (n = 1238) did not receive ventilation therapy. NIV was performed in 20.8% of patients (n = 358). TIV was performed in 9.5% of patients (n = 164), encompassing both primary TIV (7.2%, n = 124) and TIV with preceding NIV (2.3%, n = 40). TIV was more often utilized without previous NIV (25.7% vs. 8.3% of all ventilated patients), demonstrating that primary TIV was the prevailing pathway for invasive ventilation. The median (range) survival was significantly longer in the NIV cohort (40.8 [37.2–44.3] months) and the TIV cohort (82.1 [68.7–95.6] months) as compared to the non-NIV cohort (33.6 [31.6–35.7] months). Conclusions: Although NIV represents the standard of care, its utilization rate was low. TIV was mainly started without preceding NIV, suggesting that TIV may not be confined to NIV treatment escalation. However, TIV was pursued in a minority of patients who had previously undergone NIV. The survival benefit observed in the patients with NIV was equal to that reported in a controlled pivotal trial, but the prognosis with TIV is highly variable. The determinants of utilization of NIV/TIV and of survival (bulbar syndrome, availability of ventilation-related home nursing, cultural factors) warrant further investigation

Morphological and physiological characterization of filamentous Lentzea aerocolonigenes: Comparison of biopellets by microscopy and flow cytometry

Cell morphology of filamentous microorganisms is highly interesting during cultivations as it is often linked to productivity and can be influenced by process conditions. Hence, the characterization of cell morphology is of major importance to improve the understanding of industrial processes with filamentous microorganisms. For this purpose, reliable and robust methods are necessary. In this study, pellet morphology and physiology of the rebeccamycin producing filamentous actinomycete Lentzea aerocolonigenes were investigated by microscopy and flow cytometry. Both methods were compared regarding their applicability. To achieve different morphologies, a cultivation with glass bead addition (Ø = 969 μm, 100 g L-1) was compared to an unsupplemented cultivation. This led to two different macro-morphologies. Furthermore, glass bead addition increased rebeccamycin titers after 10 days of cultivation (95 mg L-1 with glass beads, 38 mg L-1 without glass beads). Macro-morphology and viability were investigated through microscopy and flow cytometry. For viability assessment fluorescent staining was used additionally. Smaller, more regular pellets were found for glass bead addition. Pellet diameters resulting from microscopy followed by image analysis were 172 μm without and 106 μm with glass beads, diameters from flow cytometry were 170 and 100 μm, respectively. These results show excellent agreement of both methods, each considering several thousand pellets. Furthermore, the pellet viability obtained from both methods suggested an enhanced metabolic activity in glass bead treated pellets during the exponential production phase. However, total viability values differ for flow cytometry (0.32 without and 0.41 with glass beads) and confocal laser scanning microscopy of single stained pellet slices (life ratio in production phase of 0.10 without and 0.22 with glass beads), which is probably caused by the different numbers of investigated pellets. In confocal laser scanning microscopy only one pellet per sample could be investigated while flow cytometry considered at least 50 pellets per sample, resulting in an increased statistical reliability

Acceptance of Enhanced Robotic Assistance Systems in People With Amyotrophic Lateral Sclerosis–Associated Motor Impairment: Observational Online Study

Background: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by a progressive paresis of the extremities and the loss of manual functioning. Due to the severe functional impairment that the disease entails, ALS requires the provision of comprehensive nursing care and a complex set of assistive technology devices. To relieve caregivers and promote autonomy of people with ALS, robotic assistance systems are being developed. This trial aims to evaluate the acceptance of technology, in general, and of robotic arm assistance among people with ALS in order to lay the groundwork for the development of a semiautomatic robotic arm that can be controlled by humans via a multimodal user interface and that will allow users to handle objects and attend to their own bodies. Objective: The aim of this study was to perform a systematic analysis of technology commitment and acceptance of robotic assistance systems from the perspective of physically limited people living with ALS. Methods: The investigation was conducted as a study of a prospective cohort. Participants were only included if they had received a medical diagnosis of ALS. Data collection took place via an online questionnaire on the Ambulanzpartner Soziotechnologie internet platform. Technological commitment was measured using the Neyer short scale. Furthermore, a multidimensional questionnaire was specially developed to analyze participant acceptance of robotic arm assistance: the Acceptance Measure of Robotic Arm Assistance (AMRAA). This questionnaire was accompanied by a video introducing the robot arm. ALS severity was ascertained using the ALS Functional Rating Scale–Extended (ALSFRS-EX). Results: A total of 268 people with ALS participated in the survey. Two-thirds of the participants were male. The overall mean ALS severity score was 42.9 (SD 11.7) points out of 60 on the ALSFRS-EX, with the most relevant restrictions on arms and legs (<60% of normal functioning). Technological commitment ranked high, with the top third scoring 47.2 points out of 60. Younger participants and males showed significantly higher values. The AMRAA score was, again, significantly higher among younger participants. However, the gender difference within the overall cohort was not significant. The more limited the arm functioning of participants according to the ALSFRS-EX subscale, the higher the acceptance rate of robotic assistance. This relationship proved significant. Conclusions: People with ALS display high technological commitment and feel positive about using technological assistance systems. In our study, younger participants were more open to technology use, in general, and robotic assistance, in particular. Self-appraisal of technology acceptance, competence, and control conviction were generally higher among men. However, any presumed gender difference vanished when users were asked to rate the anticipated usefulness of the technology, in particular the robotic arm. The acceptance was also reflected in users’ increased willingness to use a robotic arm as the functionality of their own arms decreased. From the perspective of people with ALS, robotic assistance systems are critical to promoting individual autonomy. Another key consideration in the development of future assistive technologies should be the reduction of caregiver burden. Trial Registration: German Clinical Trials Register DRKS00012803; https://tinyurl.com/w9yzduh

Dosimetric comparison of different radiation techniques (IMRT vs. 3-dimensional) of the “true” (deep) ano-inguinal lymphatic drainage of anal cancer patients

Introduction: The ano-inguinal lymphatic drainage (AILD) is located in the subcutaneous adipose tissue of the proximal medial thigh. Currently, there are no recommendations for an inclusion of the ‘true’ AILD in the clinical target volume (CTV) of definitive chemoradiation for anal cancer patients. To estimate the relevance of inguinal recurrence, we compared the incidental dose to the AILD in anal cancer (AC) patients who were treated either with Volumetric Arc Therapy – Intensity Modulated Radiation Therapy (VMAT-IMRT) or conventional 3D-radiation technique. Methods: One VMAT-IMRT-plans and one 3D-plans were calculated on the same target volumes and identical dose prescription in ten patients. We defined the volume of the AILD on the planning CT-scans based on the information of new fluorescence methods. Furthermore, we defined several anatomical subvolumes of interest inside the AILD. We examined and compared absolute and relative dosimetric parameters of the AILD and different anatomical subunits. Results: The Dmean of the AILD was 40 Gy in the 3D-group and 38 Gy in the IMRT-group. Dmean and Dmedian as well as the V30Gy of the AILD and all subvolumes of the caudal AILD were significant higher using 3D-RT compared to IMRT. Even though the absolute differences were small, in the caudal aspect of the ano-inguinal lymphatic drainage the V30Gy could be more than 10% less with VMAT-IMRT. Conclusions: 3D-RT was slightly superior to IMRT in terms of dose coverage of the AILD. However, the absolute differences were very small. Some relevant caudal parts of the AILD received an insufficient dose for treating potential micrometastases. Particularly in high-risk situations, this may lead to inguinal recurrence and therefore the true deep AILD should be included into the target volume in high risk patients