DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach

Comparing High Dimensional Word Embeddings Trained on Medical Text to Bag-of-Words For Predicting Medical Codes

Word embeddings are a useful tool for extracting knowledge from the free-form text contained in electronic health records, but it has become commonplace to train such word embeddings on data that do not accurately reflect how language is used in a healthcare context. We use prediction of medical codes as an example application to compare the accuracy of word embeddings trained on health corpora to those trained on more general collections of text. It is shown that both an increase in embedding dimensionality and an increase in the volume of health-related training data improves prediction accuracy. We also present a comparison to the traditional bag-of-words feature representation, demonstrating that in many cases, this conceptually simple method for representing text results in superior accuracy to that of word embeddings

Predicting Multiple ICD-10 Codes from Brazilian-Portuguese Clinical Notes

ICD coding from electronic clinical records is a manual, time-consuming and expensive process. Code assignment is, however, an important task for billing purposes and database organization. While many works have studied the problem of automated ICD coding from free text using machine learning techniques, most use records in the English language, especially from the MIMIC-III public dataset. This work presents results for a dataset with Brazilian Portuguese clinical notes. We develop and optimize a Logistic Regression model, a Convolutional Neural Network (CNN), a Gated Recurrent Unit Neural Network and a CNN with Attention (CNN-Att) for prediction of diagnosis ICD codes. We also report our results for the MIMIC-III dataset, which outperform previous work among models of the same families, as well as the state of the art. Compared to MIMIC-III, the Brazilian Portuguese dataset contains far fewer words per document, when only discharge summaries are used. We experiment concatenating additional documents available in this dataset, achieving a great boost in performance. The CNN-Att model achieves the best results on both datasets, with micro-averaged F1 score of 0.537 on MIMIC-III and 0.485 on our dataset with additional documents.Comment: Accepted at BRACIS 202

Modelling diverse root density dynamics and deep nitrogen uptake — a simple approach

We present a 2-D model for simulation of root density and plant nitrogen (N) uptake for crops grown in agricultural systems, based on a modification of the root density equation originally proposed by Gerwitz and Page in J Appl Ecol 11:773–781, (1974). A root system form parameter was introduced to describe the distribution of root length vertically and horizontally in the soil profile. The form parameter can vary from 0 where root density is evenly distributed through the soil profile, to 8 where practically all roots are found near the surface. The root model has other components describing root features, such as specific root length and plant N uptake kinetics. The same approach is used to distribute root length horizontally, allowing simulation of root growth and plant N uptake in row crops. The rooting depth penetration rate and depth distribution of root density were found to be the most important parameters controlling crop N uptake from deeper soil layers. The validity of the root distribution model was tested with field data for white cabbage, red beet, and leek. The model was able to simulate very different root distributions, but it was not able to simulate increasing root density with depth as seen in the experimental results for white cabbage. The model was able to simulate N depletion in different soil layers in two field studies. One included vegetable crops with very different rooting depths and the other compared effects of spring wheat and winter wheat. In both experiments variation in spring soil N availability and depth distribution was varied by the use of cover crops. This shows the model sensitivity to the form parameter value and the ability of the model to reproduce N depletion in soil layers. This work shows that the relatively simple root model developed, driven by degree days and simulated crop growth, can be used to simulate crop soil N uptake and depletion appropriately in low N input crop production systems, with a requirement of few measured parameters

Topological crystalline insulator states in Pb(1-x)Sn(x)Se

Topological insulators are a novel class of quantum materials in which time-reversal symmetry, relativistic (spin-orbit) effects and an inverted band structure result in electronic metallic states on the surfaces of bulk crystals. These helical states exhibit a Dirac-like energy dispersion across the bulk bandgap, and they are topologically protected. Recent theoretical proposals have suggested the existence of topological crystalline insulators, a novel class of topological insulators in which crystalline symmetry replaces the role of time-reversal symmetry in topological protection [1,2]. In this study, we show that the narrow-gap semiconductor Pb(1-x)Sn(x)Se is a topological crystalline insulator for x=0.23. Temperature-dependent magnetotransport measurements and angle-resolved photoelectron spectroscopy demonstrate that the material undergoes a temperature-driven topological phase transition from a trivial insulator to a topological crystalline insulator. These experimental findings add a new class to the family of topological insulators. We expect these results to be the beginning of both a considerable body of additional research on topological crystalline insulators as well as detailed studies of topological phase transitions.Comment: v2: published revised manuscript (6 pages, 3 figures) and supplementary information (5 pages, 8 figures

Random walk with barriers: Diffusion restricted by permeable membranes

Restrictions to molecular motion by barriers (membranes) are ubiquitous in biological tissues, porous media and composite materials. A major challenge is to characterize the microstructure of a material or an organism nondestructively using a bulk transport measurement. Here we demonstrate how the long-range structural correlations introduced by permeable membranes give rise to distinct features of transport. We consider Brownian motion restricted by randomly placed and oriented permeable membranes and focus on the disorder-averaged diffusion propagator using a scattering approach. The renormalization group solution reveals a scaling behavior of the diffusion coefficient for large times, with a characteristically slow inverse square root time dependence. The predicted time dependence of the diffusion coefficient agrees well with Monte Carlo simulations in two dimensions. Our results can be used to identify permeable membranes as restrictions to transport in disordered materials and in biological tissues, and to quantify their permeability and surface area.Comment: 8 pages, 3 figures; origin of dispersion clarified, refs adde

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Reproducibility of microvessel counts in breast cancer specimens

Assessment of tumour vascularity in core biopsy specimens may be a useful predictor of response to primary therapy. This study addresses practical methodological issues regarding accuracy of tumour vascularity assessments in different breast cancer specimens. Issues addressed in the study are variation caused by (i) inherent observer variation in the method, (ii) tumour heterogeneity and (iii) previous surgical manipulation of tumours. Microvessel counts were performed by two observers on separate occasions and by two different observers. Counts were performed on core biopsies and tumour sections taken simultaneously (n = 16) and with an intervening time interval (n = 21). In addition core biopsies were obtained from the same tumour on two separate occasions (n = 10). A highly significant correlation was found in counts performed by the same observers at different times and between two different observers. No significant correlation was found in counts of core biopsies and tumour sections taken either simultaneously or subsequently. No correlation was found between counts of sequential core biopsies. Study findings suggest that, although microvessel counts may be assessed reproducibly by the same and different observers, counts performed in core biopsies do not accurately reflect those of overall tumour, limiting their potential as predictive or prognostic markers. © 1999 Cancer Research Campaig

The differential effects of core stabilization exercise regime and conventional physiotherapy regime on postural control parameters during perturbation in patients with movement and control impairment chronic low back pain

<p>Abstract</p> <p>Background</p> <p>The purpose of the present study was to examine the differential effect of core stability exercise training and conventional physiotherapy regime on altered postural control parameters in patients with chronic low back pain (CLBP). As heterogeneity in CLBP population moderates the effect of intervention on outcomes, in this study, interventions approaches were used based on sub-groups of CLBP.</p> <p>Methods</p> <p>This was an allocation concealed, blinded, sequential and pragmatic control trial. Three groups of participants were investigated during postural perturbations: 1) CLBP patients with movement impairment (n = 15, MI group) randomized to conventional physiotherapy regime 2) fifteen CLBP patients with control impairment randomized to core stability group (CI group) and 3) fifteen healthy controls (HC).</p> <p>Results</p> <p>The MI group did not show any significant changes in postural control parameters after the intervention period however they improved significantly in disability scores and fear avoidance belief questionnaire work score (P < 0.05). The CI group showed significant improvements in Fx, Fz, and My variables (p < 0.013, p < 0.006, and p < 0.002 respectively with larger effect sizes: Hedges's g > 0.8) after 8 weeks of core stability exercises for the adjusted p values. Postural control parameters of HC group were analyzed independently with pre and post postural control parameters of CI and MI group. This revealed the significant improvements in postural control parameters in CI group compared to MI group indicating the specific adaptation to the core stability exercises in CI group. Though the disability scores were reduced significantly in CI and MI groups (p < 0.001), the post intervention scores between groups were not found significant (p < 0.288). Twenty percentage absolute risk reduction in flare-up rates during intervention was found in CI group (95% CI: 0.69-0.98).</p> <p>Conclusions</p> <p>In this study core stability exercise group demonstrated significant improvements after intervention in ground reaction forces (Fz, Mz; g > 0.8) indicating changes in load transfer patterns during perturbation similar to HC group.</p> <p>Trial registration</p> <p>UTRN095032158-06012009423714</p