Temporal modeling of ALS using longitudinal data and long-short term memory-based algorithm
ALS is a neurodegenerative disease where factors such as disease progression rate and pattern var... more ALS is a neurodegenerative disease where factors such as disease progression rate and pattern vary greatly among patients. Since patient functionality deteriorates over time, we model ALS temporally to mimic the physician’s reasoning by incorporating old with new information using a long-short term memory (LSTM) network. We demonstrate that the LSTM achieves a higher accuracy than a random forest in disease state prediction, and improves accuracy with data from additional clinic visits. Being an anytime predictor, our model can help physicians and caregivers to adjust patients’ treatment and living environment along the disease period, improving patients’ life quality.
Human mobility patterns reflect many aspects of life, from the global spread of infectious diseas... more Human mobility patterns reflect many aspects of life, from the global spread of infectious diseases to urban planning and daily commute patterns. In recent years, the prevalence of positioning methods and technologies, such as the global positioning system, cellular radio tower geo-positioning, and WiFi positioning systems, has driven efforts to collect human mobility data and to mine patterns of interest within these data in order to promote the development of location-based services and applications. The efforts to mine significant patterns within large-scale, high-dimensional mobility data have solicited use of advanced analysis techniques, usually based on machine learning methods, and therefore, in this paper, we survey and assess different approaches and models that analyze and learn human mobility patterns using mainly machine learning methods. We categorize these approaches and models in a taxonomy based on their positioning characteristics, the scale of analysis, the properties of the modeling approach, and the class of applications they can serve. We find that these applications can be categorized into three classes: user modeling, place modeling, and trajectory modeling, each class with its characteristics. Finally, we analyze the short-term trends and future challenges of human mobility analysis. Keywords Human mobility patterns • Mobile phones • Machine learning • Data mining 1 Introduction According to a 2016 worldwide Pew report, around 90% of people have a mobile phone, and between 45 and 80% have a smartphone [71]. In recent years, we have witnessed a mobile computing revolution-mobile technology is now more connected, sophisticated,
Understanding land use in urban areas, from the perspective of social function, is beneficial for... more Understanding land use in urban areas, from the perspective of social function, is beneficial for a variety of fields, including urban and highway planning, advertising, and business. However, big cities with complex social dynamics and rapid development complicate the task of understanding these social functions. In this paper, we analyze and interpret human social function in urban areas as reflected in cellular communication usage patterns. We base our analysis on digital traces left by mobile phone users, and from this raw data, we derive a varied collection of features that illuminate the social behavior of each land use. We divide space and time into basic spatiotemporal units and classify them according to their land use. We categorize land uses with a leveled hierarchy of semantic categories that include different levels of detail resolution. We apply the above methodology to a dataset consisting of 62 days of cellular data recorded in nine cities in the Tel Aviv district. The methodology proved beneficial with an accuracy rate ranging from 84 to 91%, dependent on land use label resolution. In addition, analyzing the results sheds light on some of the limitations of relying solely on cellular communication as a data resource. We discuss some of these problems and offer applicable solutions.
We provide a novel algorithm for the discovery of mobility patterns and prediction of users' dest... more We provide a novel algorithm for the discovery of mobility patterns and prediction of users' destination locations, both in terms of geographic coordinates and semantic meaning. We did not use any semantic data voluntarily provided by a user, and there was no sharing of data among the users. An advantage of our algorithm is that it allows a trade-off between prediction accuracy and information. Experimental validation was conducted on a GPS dataset collected in the Microsoft Research Asia GeoLife project by 168 users in a period of over five years.
A Bayesian multinet classifier allows a different set of independence assertions among variables ... more A Bayesian multinet classifier allows a different set of independence assertions among variables in each of a set of local Bayesian networks composing the multinet. The structure of the local network is usually learned using a jointprobability-based score that is less specific to classification, i.e., classifiers based on structures providing high scores are not necessarily accurate. Moreover, this score is less discriminative for learning multinet classifiers because generally it is computed using only the class patterns and avoiding patterns of the other classes. We propose the Bayesian class-matched multinet (BCM 2) classifier to tackle both issues. The BCM 2 learns each local network using a detection-rejection measure, i.e., the accuracy in simultaneously detecting class patterns while rejecting patterns of the other classes. This classifier demonstrates superior accuracy to other state-of-the-art Bayesian network and multinet classifiers on 32 real-world databases.
In this paper, we modify the fuzzy ARTMAP (FA) neural network (NN) using the Bayesian framework i... more In this paper, we modify the fuzzy ARTMAP (FA) neural network (NN) using the Bayesian framework in order to improve its classification accuracy while simultaneously reduce its category proliferation. The proposed algorithm, called Bayesian ARTMAP (BA), preserves the FA advantages and also enhances its performance by the following: 1) representing a category using a multidimensional Gaussian distribution, 2) allowing a category to grow or shrink, 3) limiting a category hypervolume, 4) using Bayes' decision theory for learning and inference, and 5) employing the probabilistic association between every category and a class in order to predict the class. In addition, the BA estimates the class posterior probability and thereby enables the introduction of loss and classification according to the minimum expected loss. Based on these characteristics and using synthetic and 20 real-world databases, we show that the BA outperformes the FA, either trained for one epoch or until completion, with respect to classification accuracy, sensitivity to statistical overlapping, learning curves, expected loss, and category proliferation.
i417 intraclass correlation coefficients (ICCs) and interpreted according to Landis and Koch benc... more i417 intraclass correlation coefficients (ICCs) and interpreted according to Landis and Koch benchmarks (slight, 0.00-0.20; fair, 0.21-0.40; moderate, 0.41-0.60; substantial, 0.61-0.80; almost perfect, 0.81-1.00). Responsiveness was evaluated using the area under the receiver operating characteristic curve (AUC). The API was developed using linear regression with a 100-mm visual analogue scale (VAS) of pouchitis activity as an anchor. Results: Among all measures assessed for reliability and responsiveness, the simple endoscopic score for Crohn's disease (SES-CD) and the Robarts histopathology index (RHI) were selected for inclusion in the API. The total API score ranges from 0 to 69, with higher scores indicating more severe disease activity, and can be calculated as API = (3 × SES-CD [0 to 12]) + (1 × RHI [0 to 33]). The API demonstrated almost perfect intra-rater reliability (ICC, 0.88 [95% CI: 0.81, 0.92]), substantial inter-rater reliability (ICC, 0.72 [95% CI: 0.60, 0.79]), and a high correlation with the VAS (r = 0.84 [95% CI: 0.76, 0.89]). Index responsiveness in detecting meaningful improvements in disease activity was higher for the API (AUC, 0.95 [95% CI: 0.89, 0.98]) than for existing endoscopic indices (∆AUC, 0.09-0.24; P≤0.005) (Table). Conclusion: The novel API is a composite index of endoscopic (SES-CD) and histologic disease activity (RHI) that is reliable and significantly more responsive than existing endoscopic pouchitis scoring systems. The API is a promising tool for use in clinical trials of novel therapies for pouchitis. Further validation is needed in independent datasets.
Learning a latent variable model (LVM) exploits values of the measured variables as manifested in... more Learning a latent variable model (LVM) exploits values of the measured variables as manifested in the data to causal discovery. Because the challenge in learning an LVM is similar to that faced in unsupervised learning, where the number of clusters and the classes that are represented by these clusters are unknown, we link causal discovery and clustering. We propose the concept of pairwise cluster comparison (PCC), by which clusters of data points are compared pairwise to associate changes in the observed variables with changes in their ancestor latent variables and thereby to reveal these latent variables and their causal paths of influence, and the learning PCC (LPCC) algorithm that identifies PCCs and uses them to learn an LVM. LPCC is not limited to linear or latent-tree models. It returns a pattern of the true graph or the true graph itself if the graph has serial connections or not, respectively. The complete theoretical foundation to PCC, the LPCC algorithm, and its experimental evaluation are given in [Asbeh and Lerner, 2016a,b], whereas, here, we only introduce and promote them. The LPCC code and evaluation results are available online.
Asian Conference on Machine Learning, Nov 17, 2012
Identification of latent variables that govern a problem and the relationships among them given m... more Identification of latent variables that govern a problem and the relationships among them given measurements in the observed world are important for causal discovery. This identification can be made by analyzing constraints imposed by the latents in the measurements. We introduce the concept of pairwise cluster comparison PCC to identify causal relationships from clusters and a two-stage algorithm, called LPCC, that learns a latent variable model (LVM) using PCC. First, LPCC learns the exogenous and the collider latents, as well as their observed descendants, by utilizing pairwise comparisons between clusters in the measurement space that may explain latent causes. Second, LPCC learns the non-collider endogenous latents and their children by splitting these latents from their previously learned latent ancestors. LPCC is not limited to linear or latent-tree models and does not make assumptions about the distribution. Using simulated and real-world datasets, we show that LPCC improves accuracy with the sample size, can learn large LVMs, and is accurate in learning compared to state-of-the-art algorithms.
International Joint Conference on Artificial Intelligence, Aug 3, 2013
Thresholding a measure in conditional independence (CI) tests using a fixed value enables learnin... more Thresholding a measure in conditional independence (CI) tests using a fixed value enables learning and removing edges as part of learning a Bayesian network structure. However, the learned structure is sensitive to the threshold that is commonly selected: 1) arbitrarily; 2) irrespective of characteristics of the domain; and 3) fixed for all CI tests. We analyze the impact on mutual information-a CI measure-of factors, such as sample size, degree of variable dependence, and variables' cardinalities. Following, we suggest to adaptively threshold individual tests based on the factors. We show that adaptive thresholds better distinguish between pairs of dependent variables and pairs of independent variables and enable learning structures more accurately and quickly than when using fixed thresholds.
International Conference on Artificial Intelligence and Statistics, 2005
We propose a constraint-based algorithm for Bayesian network structure learning called recursive ... more We propose a constraint-based algorithm for Bayesian network structure learning called recursive autonomy identification (RAI). The RAI algorithm learns the structure by recursive application of conditional independence (CI) tests of increasing orders, edge direction and structure decomposition into autonomous substructures. In comparison to other constraintbased algorithms d-separating structures and then directing the resulted undirected graph, the RAI algorithm combines the two processes from the outset and along the procedure. Learning using the RAI algorithm renders smaller condition sets thus requires a smaller number of high order CI tests. This reduces complexity and run-time as well as increases accuracy since diminishing the curse-of-dimensionality. When evaluated on synthetic and "real-world" databases as well as the ALARM network, the RAI algorithm shows better structural correctness, run-time reduction along with accuracy improvement compared to popular constraint-based structure learning algorithms. Accuracy improvement is also demonstrated when compared to a common search-and-score structure learning algorithm.
Background: Previous systems for dot (signal) counting in fluorescence in situ hybridization (FIS... more Background: Previous systems for dot (signal) counting in fluorescence in situ hybridization (FISH) images have relied on an auto-focusing method for obtaining a clearly defined image. Because signals are distributed in three dimensions within the nucleus and artifacts such as debris and background fluorescence can attract the focusing method, valid signals can be left unfocused or unseen. This leads to dot counting errors, which increase with the number of probes. Methods: The approach described here dispenses with auto-focusing, and instead relies on a neural network (NN) classifier that discriminates between in and out-of-focus images taken at different focal planes of the same field of view. Discrimination is performed by the NN, which classifies signals of each image as valid data or artifacts (due to out of focusing). The image that contains no artifacts is the in-focus image selected for dot count proportion estimation. Results: Using an NN classifier and a set of features to represent signals improves upon previous discrimination schemes that are based on nonadaptable decision boundaries and single-feature signal representation. Moreover, the classifier is not limited by the number of probes. Three classification strategies, two of them hierarchical, have been examined and found to achieve each between 83% and 87% accuracy on unseen data. Screening, while performing dot counting, of in and out-of-focus images based on signal classification suggests an accurate and efficient alternative to that obtained using an auto-focusing mechanism. Cytometry 43:87-93, 2001.
We propose and investigate the fuzzy ARTMAP neural network in off and online classification of fl... more We propose and investigate the fuzzy ARTMAP neural network in off and online classification of fluorescence in situ hybridization image signals enabling clinical diagnosis of numerical genetic abnormalities. We evaluate the classification task (detecting a several abnormalities separately or simultaneously), classifier paradigm (monolithic or hierarchical), ordering strategy for the training patterns (averaging or voting), training mode (for one epoch, with validation or until completion) and model sensitivity to parameters. We find the fuzzy ARTMAP accurate in accomplishing both tasks requiring only very few training epochs. Also, selecting a training ordering by voting is more precise than if averaging over orderings. If trained for only one epoch, the fuzzy ARTMAP provides fast, yet stable and accurate learning as well as insensitivity to model complexity. Early stop of training using a validation set reduces the fuzzy ARTMAP complexity as for other machine learning models but cannot improve accuracy beyond that achieved when training is completed. Compared to other machine learning models, the fuzzy ARTMAP does not loose but gain accuracy when overtrained, although increasing its number of categories. Learned incrementally, the fuzzy ARTMAP reaches its ultimate accuracy very fast obtaining most of its data representation capability and accuracy by using only a few examples. Finally, the fuzzy ARTMAP accuracy for this domain is comparable with those of the multilayer perceptron and support vector machine and superior to those of the naive Bayesian and linear classifiers. Index Terms-Fluorescence in situ hybridization (FISH), fuzzy ARTMAP neural network (NN), genetic abnormality diagnosis, image classification, off-and online learning.
IEEE transactions on systems, man, and cybernetics, 1998
The application of neural networks (NN's) to automatic analysis of chromosome images is investiga... more The application of neural networks (NN's) to automatic analysis of chromosome images is investigated in this paper. All aspects of the analysis, namely segmentation, feature description, selection and extraction, and classification, are studied. As part of the segmentation process, the separation of clusters of partially occluded chromosomes, which is the critical stage that state-of-the-art chromosome analyzers usually fail to accomplish, is performed. First, a moment representation of the image pixels is clustered to create a binary image without a need for threshold selection. Based on the binary image, lines connecting cut points imply possible separations. These hypotheses are verified by a multilayer perceptron (MLP) NN that classifies the two segments created by each separating line. Use of a classification-driven segmentation process gives very promising results without a need for shape modeling or an excessive use of heuristics. In addition, an NN implementation of Sammon's mapping using principal component based initialization is applied to feature extraction, significantly reducing the dimensionality of the feature space and allowing high classification capability. Finally, by applying MLP based hierarchical classification strategies to a well-explored chromosome database, we achieve a classification performance of 83.6%. This is higher than ever published on this database and an improvement of more than 10% in the error rate. Therefore, basing a chromosome analysis on the NN-based techniques that are developed in this research leads toward a completely automatic human chromosome analysis.
In recent years, technological advances, specifically new sensing and communication technologies,... more In recent years, technological advances, specifically new sensing and communication technologies, have brought new opportunities for a less expensive, dynamic, and more accurate mapping of social land use in cities. However, most research has featured complex methodologies that integrate several data resources or require much prior knowledge about the examined city. We offer a methodology that requires little prior knowledge and mainly relies on call detail records, which is an inexpensive available data resource of mobile phone signals. We introduce the Semi-supervised Self-labeled K-nearest neighbor (SSK) algorithm that combines distance-weighted k-nearest neighbors (DKNN) with a self-labeled iterative technique designed for training classifiers with only a small number of labeled samples. In each iteration, the samples (small land units) that we are most confident of their classification by DKNN are added to the training set of the next iteration. We perform neighbor smoothing to the land-use classification by considering feature-space neighbors as in the regular KNN but also geographical space neighbors, and thereby leverage the tendency of approximate land areas to share similar social land use. Based only on a few labeled examples, the SSK algorithm achieves a high accuracy rate, between 74% without neighbor smoothing, and 80% with it.
Lipid profiles in biological fluids from patients with Parkinson's disease (PD) are increasingly ... more Lipid profiles in biological fluids from patients with Parkinson's disease (PD) are increasingly investigated in search of biomarkers. However, the lipid profiles in genetic PD remain to be determined, a gap of knowledge of particular interest in PD associated with mutant α-synuclein (SNCA), given the known relationship between this protein and lipids. The objective of this research is to identify serum lipid composition from SNCA A53T mutation carriers and to compare these alterations to those found in cells and transgenic mice carrying the same genetic mutation. We conducted an unbiased lipidomic analysis of 530 lipid species from 34 lipid classes in serum of 30 participants with SNCA mutation with and without PD and 30 healthy controls. The primary analysis was done between 22 PD patients with SNCA+ (SNCA+/PD+) and 30 controls using machine-learning algorithms and traditional statistics. We also analyzed the lipid composition of human clonal-cell lines and tissue from transgenic mice overexpressing the same SNCA mutation. We identified specific lipid classes that best discriminate between SNCA+/PD+ patients and healthy controls and found certain lipid species, mainly from the glycerophosphatidylcholine and triradylglycerol classes, that are most contributory to this discrimination. Most of these alterations were also present in human derived cells and transgenic mice carrying the same mutation. Our combination of lipidomic and machine learning analyses revealed alterations in glycerophosphatidylcholine and triradylglycerol in sera from PD patients as well as cells and tissues expressing mutant α-Syn. Further investigations are needed to establish the pathogenic significance of these α-Syn-associated lipid changes.
Domain adaptation from clinical trials data to the tertiary care clinic – Application to ALS
Amyotrophic lateral sclerosis (ALS) is a devastating and incurable disease affecting motor neuron... more Amyotrophic lateral sclerosis (ALS) is a devastating and incurable disease affecting motor neurons, leading to progressive paralysis and death on average within three to five years from onset. The disease is characterized by highly variable patterns and rates of progression, which pose challenges to developing reliable and accurate ALS disease state prediction models to be used on a daily basis in clinics with little data. To meet these challenges, we suggest domain adaptation from a large, but unfortunately biased, clinical trials database to that of a tertiary care ALS clinic. To evaluate the reliability and accuracy of the suggested paradigm, we examine a naïve approach by which training is based only on the clinical trials data compared with a domain adaptation approach of an initial training using this same data followed by fine-tuning training using the clinic data. We also allow summarization of the clinical longitudinal data to evaluate non-temporal models, e.g., random forest (RF), XGBoost (XGB), and multilayer perceptron (MLP), partially exploiting the dynamic information hidden in patient clinical records, in comparison to the long short-term memory (LSTM) recurrent neural network, fully exploiting the temporal information in the data. First, we notice the XGB outperformance in terms of the ALS disease state prediction error to the RF and MLP, but surprisingly also to the LSTM regardless of prediction time (up to 24 months ahead). We contribute the inferiority of the highly parametrized neural network to the impact of the curse of dimensionality. Second, we show that this error does not significantly increase when the model is trained using only the clinical trials data, especially for LSTM in long prediction times. Finally, we demonstrate that fine-tuning of the clinical trials-based pre-trained model using the clinic data improves the LSTM and MLP performance compared to using solely the clinical trials or clinic data.
Identification of latent variables that govern a problem and the relationships among them, given ... more Identification of latent variables that govern a problem and the relationships among them, given measurements in the observed world, are important for causal discovery. This identification can be accomplished by analyzing the constraints imposed by the latents in the measurements. We introduce the concept of pairwise cluster comparison (PCC) to identify causal relationships from clusters of data points and provide a two-stage algorithm called learning PCC (LPCC) that learns a latent variable model (LVM) using PCC. First, LPCC learns exogenous latents and latent colliders, as well as their observed descendants, by using pairwise comparisons between data clusters in the measurement space that may explain latent causes. Since in this first stage LPCC cannot distinguish endogenous latent non-colliders from their exogenous ancestors, a second stage is needed to extract the former, with their observed children, from the latter. If the true graph has no serial connections, LPCC returns the true graph, and if the true graph has a serial connection, LPCC returns a pattern of the true graph. LPCC's most important advantage is that it is not limited to linear or latent-tree models and makes only mild assumptions about the distribution. The paper is divided in two parts: Part I (this paper) provides the necessary preliminaries, theoretical foundation to PCC, and an overview of LPCC; Part II formally introduces the LPCC algorithm and experimentally evaluates its merit in different synthetic and real domains. The code for the LPCC algorithm and data sets used in the experiments reported in Part II are available online.
Temporal modeling of ALS using longitudinal data and long-short term memory-based algorithm
ALS is a neurodegenerative disease where factors such as disease progression rate and pattern var... more ALS is a neurodegenerative disease where factors such as disease progression rate and pattern vary greatly among patients. Since patient functionality deteriorates over time, we model ALS temporally to mimic the physician’s reasoning by incorporating old with new information using a long-short term memory (LSTM) network. We demonstrate that the LSTM achieves a higher accuracy than a random forest in disease state prediction, and improves accuracy with data from additional clinic visits. Being an anytime predictor, our model can help physicians and caregivers to adjust patients’ treatment and living environment along the disease period, improving patients’ life quality.
Human mobility patterns reflect many aspects of life, from the global spread of infectious diseas... more Human mobility patterns reflect many aspects of life, from the global spread of infectious diseases to urban planning and daily commute patterns. In recent years, the prevalence of positioning methods and technologies, such as the global positioning system, cellular radio tower geo-positioning, and WiFi positioning systems, has driven efforts to collect human mobility data and to mine patterns of interest within these data in order to promote the development of location-based services and applications. The efforts to mine significant patterns within large-scale, high-dimensional mobility data have solicited use of advanced analysis techniques, usually based on machine learning methods, and therefore, in this paper, we survey and assess different approaches and models that analyze and learn human mobility patterns using mainly machine learning methods. We categorize these approaches and models in a taxonomy based on their positioning characteristics, the scale of analysis, the properties of the modeling approach, and the class of applications they can serve. We find that these applications can be categorized into three classes: user modeling, place modeling, and trajectory modeling, each class with its characteristics. Finally, we analyze the short-term trends and future challenges of human mobility analysis. Keywords Human mobility patterns • Mobile phones • Machine learning • Data mining 1 Introduction According to a 2016 worldwide Pew report, around 90% of people have a mobile phone, and between 45 and 80% have a smartphone [71]. In recent years, we have witnessed a mobile computing revolution-mobile technology is now more connected, sophisticated,
Understanding land use in urban areas, from the perspective of social function, is beneficial for... more Understanding land use in urban areas, from the perspective of social function, is beneficial for a variety of fields, including urban and highway planning, advertising, and business. However, big cities with complex social dynamics and rapid development complicate the task of understanding these social functions. In this paper, we analyze and interpret human social function in urban areas as reflected in cellular communication usage patterns. We base our analysis on digital traces left by mobile phone users, and from this raw data, we derive a varied collection of features that illuminate the social behavior of each land use. We divide space and time into basic spatiotemporal units and classify them according to their land use. We categorize land uses with a leveled hierarchy of semantic categories that include different levels of detail resolution. We apply the above methodology to a dataset consisting of 62 days of cellular data recorded in nine cities in the Tel Aviv district. The methodology proved beneficial with an accuracy rate ranging from 84 to 91%, dependent on land use label resolution. In addition, analyzing the results sheds light on some of the limitations of relying solely on cellular communication as a data resource. We discuss some of these problems and offer applicable solutions.
We provide a novel algorithm for the discovery of mobility patterns and prediction of users' dest... more We provide a novel algorithm for the discovery of mobility patterns and prediction of users' destination locations, both in terms of geographic coordinates and semantic meaning. We did not use any semantic data voluntarily provided by a user, and there was no sharing of data among the users. An advantage of our algorithm is that it allows a trade-off between prediction accuracy and information. Experimental validation was conducted on a GPS dataset collected in the Microsoft Research Asia GeoLife project by 168 users in a period of over five years.
A Bayesian multinet classifier allows a different set of independence assertions among variables ... more A Bayesian multinet classifier allows a different set of independence assertions among variables in each of a set of local Bayesian networks composing the multinet. The structure of the local network is usually learned using a jointprobability-based score that is less specific to classification, i.e., classifiers based on structures providing high scores are not necessarily accurate. Moreover, this score is less discriminative for learning multinet classifiers because generally it is computed using only the class patterns and avoiding patterns of the other classes. We propose the Bayesian class-matched multinet (BCM 2) classifier to tackle both issues. The BCM 2 learns each local network using a detection-rejection measure, i.e., the accuracy in simultaneously detecting class patterns while rejecting patterns of the other classes. This classifier demonstrates superior accuracy to other state-of-the-art Bayesian network and multinet classifiers on 32 real-world databases.
In this paper, we modify the fuzzy ARTMAP (FA) neural network (NN) using the Bayesian framework i... more In this paper, we modify the fuzzy ARTMAP (FA) neural network (NN) using the Bayesian framework in order to improve its classification accuracy while simultaneously reduce its category proliferation. The proposed algorithm, called Bayesian ARTMAP (BA), preserves the FA advantages and also enhances its performance by the following: 1) representing a category using a multidimensional Gaussian distribution, 2) allowing a category to grow or shrink, 3) limiting a category hypervolume, 4) using Bayes' decision theory for learning and inference, and 5) employing the probabilistic association between every category and a class in order to predict the class. In addition, the BA estimates the class posterior probability and thereby enables the introduction of loss and classification according to the minimum expected loss. Based on these characteristics and using synthetic and 20 real-world databases, we show that the BA outperformes the FA, either trained for one epoch or until completion, with respect to classification accuracy, sensitivity to statistical overlapping, learning curves, expected loss, and category proliferation.
i417 intraclass correlation coefficients (ICCs) and interpreted according to Landis and Koch benc... more i417 intraclass correlation coefficients (ICCs) and interpreted according to Landis and Koch benchmarks (slight, 0.00-0.20; fair, 0.21-0.40; moderate, 0.41-0.60; substantial, 0.61-0.80; almost perfect, 0.81-1.00). Responsiveness was evaluated using the area under the receiver operating characteristic curve (AUC). The API was developed using linear regression with a 100-mm visual analogue scale (VAS) of pouchitis activity as an anchor. Results: Among all measures assessed for reliability and responsiveness, the simple endoscopic score for Crohn's disease (SES-CD) and the Robarts histopathology index (RHI) were selected for inclusion in the API. The total API score ranges from 0 to 69, with higher scores indicating more severe disease activity, and can be calculated as API = (3 × SES-CD [0 to 12]) + (1 × RHI [0 to 33]). The API demonstrated almost perfect intra-rater reliability (ICC, 0.88 [95% CI: 0.81, 0.92]), substantial inter-rater reliability (ICC, 0.72 [95% CI: 0.60, 0.79]), and a high correlation with the VAS (r = 0.84 [95% CI: 0.76, 0.89]). Index responsiveness in detecting meaningful improvements in disease activity was higher for the API (AUC, 0.95 [95% CI: 0.89, 0.98]) than for existing endoscopic indices (∆AUC, 0.09-0.24; P≤0.005) (Table). Conclusion: The novel API is a composite index of endoscopic (SES-CD) and histologic disease activity (RHI) that is reliable and significantly more responsive than existing endoscopic pouchitis scoring systems. The API is a promising tool for use in clinical trials of novel therapies for pouchitis. Further validation is needed in independent datasets.
Learning a latent variable model (LVM) exploits values of the measured variables as manifested in... more Learning a latent variable model (LVM) exploits values of the measured variables as manifested in the data to causal discovery. Because the challenge in learning an LVM is similar to that faced in unsupervised learning, where the number of clusters and the classes that are represented by these clusters are unknown, we link causal discovery and clustering. We propose the concept of pairwise cluster comparison (PCC), by which clusters of data points are compared pairwise to associate changes in the observed variables with changes in their ancestor latent variables and thereby to reveal these latent variables and their causal paths of influence, and the learning PCC (LPCC) algorithm that identifies PCCs and uses them to learn an LVM. LPCC is not limited to linear or latent-tree models. It returns a pattern of the true graph or the true graph itself if the graph has serial connections or not, respectively. The complete theoretical foundation to PCC, the LPCC algorithm, and its experimental evaluation are given in [Asbeh and Lerner, 2016a,b], whereas, here, we only introduce and promote them. The LPCC code and evaluation results are available online.
Asian Conference on Machine Learning, Nov 17, 2012
Identification of latent variables that govern a problem and the relationships among them given m... more Identification of latent variables that govern a problem and the relationships among them given measurements in the observed world are important for causal discovery. This identification can be made by analyzing constraints imposed by the latents in the measurements. We introduce the concept of pairwise cluster comparison PCC to identify causal relationships from clusters and a two-stage algorithm, called LPCC, that learns a latent variable model (LVM) using PCC. First, LPCC learns the exogenous and the collider latents, as well as their observed descendants, by utilizing pairwise comparisons between clusters in the measurement space that may explain latent causes. Second, LPCC learns the non-collider endogenous latents and their children by splitting these latents from their previously learned latent ancestors. LPCC is not limited to linear or latent-tree models and does not make assumptions about the distribution. Using simulated and real-world datasets, we show that LPCC improves accuracy with the sample size, can learn large LVMs, and is accurate in learning compared to state-of-the-art algorithms.
International Joint Conference on Artificial Intelligence, Aug 3, 2013
Thresholding a measure in conditional independence (CI) tests using a fixed value enables learnin... more Thresholding a measure in conditional independence (CI) tests using a fixed value enables learning and removing edges as part of learning a Bayesian network structure. However, the learned structure is sensitive to the threshold that is commonly selected: 1) arbitrarily; 2) irrespective of characteristics of the domain; and 3) fixed for all CI tests. We analyze the impact on mutual information-a CI measure-of factors, such as sample size, degree of variable dependence, and variables' cardinalities. Following, we suggest to adaptively threshold individual tests based on the factors. We show that adaptive thresholds better distinguish between pairs of dependent variables and pairs of independent variables and enable learning structures more accurately and quickly than when using fixed thresholds.
International Conference on Artificial Intelligence and Statistics, 2005
We propose a constraint-based algorithm for Bayesian network structure learning called recursive ... more We propose a constraint-based algorithm for Bayesian network structure learning called recursive autonomy identification (RAI). The RAI algorithm learns the structure by recursive application of conditional independence (CI) tests of increasing orders, edge direction and structure decomposition into autonomous substructures. In comparison to other constraintbased algorithms d-separating structures and then directing the resulted undirected graph, the RAI algorithm combines the two processes from the outset and along the procedure. Learning using the RAI algorithm renders smaller condition sets thus requires a smaller number of high order CI tests. This reduces complexity and run-time as well as increases accuracy since diminishing the curse-of-dimensionality. When evaluated on synthetic and "real-world" databases as well as the ALARM network, the RAI algorithm shows better structural correctness, run-time reduction along with accuracy improvement compared to popular constraint-based structure learning algorithms. Accuracy improvement is also demonstrated when compared to a common search-and-score structure learning algorithm.
Background: Previous systems for dot (signal) counting in fluorescence in situ hybridization (FIS... more Background: Previous systems for dot (signal) counting in fluorescence in situ hybridization (FISH) images have relied on an auto-focusing method for obtaining a clearly defined image. Because signals are distributed in three dimensions within the nucleus and artifacts such as debris and background fluorescence can attract the focusing method, valid signals can be left unfocused or unseen. This leads to dot counting errors, which increase with the number of probes. Methods: The approach described here dispenses with auto-focusing, and instead relies on a neural network (NN) classifier that discriminates between in and out-of-focus images taken at different focal planes of the same field of view. Discrimination is performed by the NN, which classifies signals of each image as valid data or artifacts (due to out of focusing). The image that contains no artifacts is the in-focus image selected for dot count proportion estimation. Results: Using an NN classifier and a set of features to represent signals improves upon previous discrimination schemes that are based on nonadaptable decision boundaries and single-feature signal representation. Moreover, the classifier is not limited by the number of probes. Three classification strategies, two of them hierarchical, have been examined and found to achieve each between 83% and 87% accuracy on unseen data. Screening, while performing dot counting, of in and out-of-focus images based on signal classification suggests an accurate and efficient alternative to that obtained using an auto-focusing mechanism. Cytometry 43:87-93, 2001.
We propose and investigate the fuzzy ARTMAP neural network in off and online classification of fl... more We propose and investigate the fuzzy ARTMAP neural network in off and online classification of fluorescence in situ hybridization image signals enabling clinical diagnosis of numerical genetic abnormalities. We evaluate the classification task (detecting a several abnormalities separately or simultaneously), classifier paradigm (monolithic or hierarchical), ordering strategy for the training patterns (averaging or voting), training mode (for one epoch, with validation or until completion) and model sensitivity to parameters. We find the fuzzy ARTMAP accurate in accomplishing both tasks requiring only very few training epochs. Also, selecting a training ordering by voting is more precise than if averaging over orderings. If trained for only one epoch, the fuzzy ARTMAP provides fast, yet stable and accurate learning as well as insensitivity to model complexity. Early stop of training using a validation set reduces the fuzzy ARTMAP complexity as for other machine learning models but cannot improve accuracy beyond that achieved when training is completed. Compared to other machine learning models, the fuzzy ARTMAP does not loose but gain accuracy when overtrained, although increasing its number of categories. Learned incrementally, the fuzzy ARTMAP reaches its ultimate accuracy very fast obtaining most of its data representation capability and accuracy by using only a few examples. Finally, the fuzzy ARTMAP accuracy for this domain is comparable with those of the multilayer perceptron and support vector machine and superior to those of the naive Bayesian and linear classifiers. Index Terms-Fluorescence in situ hybridization (FISH), fuzzy ARTMAP neural network (NN), genetic abnormality diagnosis, image classification, off-and online learning.
IEEE transactions on systems, man, and cybernetics, 1998
The application of neural networks (NN's) to automatic analysis of chromosome images is investiga... more The application of neural networks (NN's) to automatic analysis of chromosome images is investigated in this paper. All aspects of the analysis, namely segmentation, feature description, selection and extraction, and classification, are studied. As part of the segmentation process, the separation of clusters of partially occluded chromosomes, which is the critical stage that state-of-the-art chromosome analyzers usually fail to accomplish, is performed. First, a moment representation of the image pixels is clustered to create a binary image without a need for threshold selection. Based on the binary image, lines connecting cut points imply possible separations. These hypotheses are verified by a multilayer perceptron (MLP) NN that classifies the two segments created by each separating line. Use of a classification-driven segmentation process gives very promising results without a need for shape modeling or an excessive use of heuristics. In addition, an NN implementation of Sammon's mapping using principal component based initialization is applied to feature extraction, significantly reducing the dimensionality of the feature space and allowing high classification capability. Finally, by applying MLP based hierarchical classification strategies to a well-explored chromosome database, we achieve a classification performance of 83.6%. This is higher than ever published on this database and an improvement of more than 10% in the error rate. Therefore, basing a chromosome analysis on the NN-based techniques that are developed in this research leads toward a completely automatic human chromosome analysis.
In recent years, technological advances, specifically new sensing and communication technologies,... more In recent years, technological advances, specifically new sensing and communication technologies, have brought new opportunities for a less expensive, dynamic, and more accurate mapping of social land use in cities. However, most research has featured complex methodologies that integrate several data resources or require much prior knowledge about the examined city. We offer a methodology that requires little prior knowledge and mainly relies on call detail records, which is an inexpensive available data resource of mobile phone signals. We introduce the Semi-supervised Self-labeled K-nearest neighbor (SSK) algorithm that combines distance-weighted k-nearest neighbors (DKNN) with a self-labeled iterative technique designed for training classifiers with only a small number of labeled samples. In each iteration, the samples (small land units) that we are most confident of their classification by DKNN are added to the training set of the next iteration. We perform neighbor smoothing to the land-use classification by considering feature-space neighbors as in the regular KNN but also geographical space neighbors, and thereby leverage the tendency of approximate land areas to share similar social land use. Based only on a few labeled examples, the SSK algorithm achieves a high accuracy rate, between 74% without neighbor smoothing, and 80% with it.
Lipid profiles in biological fluids from patients with Parkinson's disease (PD) are increasingly ... more Lipid profiles in biological fluids from patients with Parkinson's disease (PD) are increasingly investigated in search of biomarkers. However, the lipid profiles in genetic PD remain to be determined, a gap of knowledge of particular interest in PD associated with mutant α-synuclein (SNCA), given the known relationship between this protein and lipids. The objective of this research is to identify serum lipid composition from SNCA A53T mutation carriers and to compare these alterations to those found in cells and transgenic mice carrying the same genetic mutation. We conducted an unbiased lipidomic analysis of 530 lipid species from 34 lipid classes in serum of 30 participants with SNCA mutation with and without PD and 30 healthy controls. The primary analysis was done between 22 PD patients with SNCA+ (SNCA+/PD+) and 30 controls using machine-learning algorithms and traditional statistics. We also analyzed the lipid composition of human clonal-cell lines and tissue from transgenic mice overexpressing the same SNCA mutation. We identified specific lipid classes that best discriminate between SNCA+/PD+ patients and healthy controls and found certain lipid species, mainly from the glycerophosphatidylcholine and triradylglycerol classes, that are most contributory to this discrimination. Most of these alterations were also present in human derived cells and transgenic mice carrying the same mutation. Our combination of lipidomic and machine learning analyses revealed alterations in glycerophosphatidylcholine and triradylglycerol in sera from PD patients as well as cells and tissues expressing mutant α-Syn. Further investigations are needed to establish the pathogenic significance of these α-Syn-associated lipid changes.
Domain adaptation from clinical trials data to the tertiary care clinic – Application to ALS
Amyotrophic lateral sclerosis (ALS) is a devastating and incurable disease affecting motor neuron... more Amyotrophic lateral sclerosis (ALS) is a devastating and incurable disease affecting motor neurons, leading to progressive paralysis and death on average within three to five years from onset. The disease is characterized by highly variable patterns and rates of progression, which pose challenges to developing reliable and accurate ALS disease state prediction models to be used on a daily basis in clinics with little data. To meet these challenges, we suggest domain adaptation from a large, but unfortunately biased, clinical trials database to that of a tertiary care ALS clinic. To evaluate the reliability and accuracy of the suggested paradigm, we examine a naïve approach by which training is based only on the clinical trials data compared with a domain adaptation approach of an initial training using this same data followed by fine-tuning training using the clinic data. We also allow summarization of the clinical longitudinal data to evaluate non-temporal models, e.g., random forest (RF), XGBoost (XGB), and multilayer perceptron (MLP), partially exploiting the dynamic information hidden in patient clinical records, in comparison to the long short-term memory (LSTM) recurrent neural network, fully exploiting the temporal information in the data. First, we notice the XGB outperformance in terms of the ALS disease state prediction error to the RF and MLP, but surprisingly also to the LSTM regardless of prediction time (up to 24 months ahead). We contribute the inferiority of the highly parametrized neural network to the impact of the curse of dimensionality. Second, we show that this error does not significantly increase when the model is trained using only the clinical trials data, especially for LSTM in long prediction times. Finally, we demonstrate that fine-tuning of the clinical trials-based pre-trained model using the clinic data improves the LSTM and MLP performance compared to using solely the clinical trials or clinic data.
Identification of latent variables that govern a problem and the relationships among them, given ... more Identification of latent variables that govern a problem and the relationships among them, given measurements in the observed world, are important for causal discovery. This identification can be accomplished by analyzing the constraints imposed by the latents in the measurements. We introduce the concept of pairwise cluster comparison (PCC) to identify causal relationships from clusters of data points and provide a two-stage algorithm called learning PCC (LPCC) that learns a latent variable model (LVM) using PCC. First, LPCC learns exogenous latents and latent colliders, as well as their observed descendants, by using pairwise comparisons between data clusters in the measurement space that may explain latent causes. Since in this first stage LPCC cannot distinguish endogenous latent non-colliders from their exogenous ancestors, a second stage is needed to extract the former, with their observed children, from the latter. If the true graph has no serial connections, LPCC returns the true graph, and if the true graph has a serial connection, LPCC returns a pattern of the true graph. LPCC's most important advantage is that it is not limited to linear or latent-tree models and makes only mild assumptions about the distribution. The paper is divided in two parts: Part I (this paper) provides the necessary preliminaries, theoretical foundation to PCC, and an overview of LPCC; Part II formally introduces the LPCC algorithm and experimentally evaluates its merit in different synthetic and real domains. The code for the LPCC algorithm and data sets used in the experiments reported in Part II are available online.
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Papers by Boaz Lerner