Kamran Kowsari is a Scientist at the University of California, Los Angeles, Office of Health informatics, and analytics (OHIA). He earned his Ph.D. from the School of Engineering and Applied Science of the University of Virginia. His advisors are professor Laura E. Barnes and Donald E. Brown. He earned his Master of Science from The George Washington University in 2014. Phone: (202) 812-3013
Background Shareable e-scooters have become popular, but injuries to riders and bystanders have n... more Background Shareable e-scooters have become popular, but injuries to riders and bystanders have not been well characterized. The goal of this study was to describe e-scooter injuries and estimate the rate of injury per e-scooter trip. Methods and findings Retrospective review of patients presenting to 180 clinics and 2 hospitals in greater Los Angeles between January 1, 2014 and May 14, 2020. Injuries were identified using a natural language processing (NLP) algorithm not previously used to identify injuries, tallied, and described along with required healthcare resources. We combine these tallies with municipal data on scooter use to report a monthly utilization-corrected rate of e-scooter injuries. We searched 36 million clinical notes. Our NLP algorithm correctly classified 92% of notes in the testing set compared with the gold standard of investigator review. In total, we identified 1,354 people injured by e-scooters; 30% were seen in more than one clinical setting (e.g., emerge...
Copyright (c) 2017 Kamran Kowsari Permission is hereby granted, free of charge, to any person obt... more Copyright (c) 2017 Kamran Kowsari Permission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated documentation files (the "Dataset"), to deal in the dataset without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Dataset, and to permit persons to whom the dataset is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Dataset. If you use this dataset please cite: Referenced paper: HDLTex: Hierarchical Deep Learning for Text Classification Description of Dataset: Here is three datasets which include WOS-11967 , WOS-46985, and WOS-5736 Each folder contains: -X.txt -Y.txt -YL1.txt -YL2.txt X is input data that include text sequences Y is target value YL1 is target value of level one (parent label) YL2 is target value of l...
Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adve... more Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adversely impact normal childhood development. Both conditions require a tissue biopsy for diagnosis and a major challenge of interpreting clinical biopsy images to differentiate between these gastrointestinal diseases is striking histopathologic overlap between them. In the current study, we propose four diagnosis techniques for these diseases and address their limitations and advantages. First, the diagnosis between CD, EE, and Normal biopsies is considered, but the main challenge with this diagnosis technique is the staining problem. The dataset used in this research is collected from different centers with different staining standards. To solve this problem, we use color balancing in order to train our model with a varying range of colors. Random Multimodel Deep Learning (RMDL) architecture has been used as another approach to mitigate the effects of the staining problem. RMDL combines d...
Objective: Develop a deep learning-based methodology using the foundations of systems pathology t... more Objective: Develop a deep learning-based methodology using the foundations of systems pathology to generate highly accurate predictive tools for complex gastrointestinal diseases, using celiac disease (CD) as a prototype. Design: To predict the severity of CD, defined by Marsh-Oberhuber classification, we used deep learning to develop a model based on histopathologic features. Results: The study was based on a pediatric cohort of 124 patients identified with different classes of CD severity. The model predicted CD with an overall 88.7% accuracy with the highest for Marsh IIIc (91.0%; 95% sensitivity; 91% specificity). The model identified EECs as a defining feature of children with Marsh IIIc CD and endocrinopathies which was confirmed using immunohistochemistry. Conclusion: This deep learning image analysis platform has broad applications in disease treatment, management, and prognostication and paves the way for precision medicine.
2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2018
Social anxiety disorder affects approximately 7% of the adult population in the U.S., yet a vast ... more Social anxiety disorder affects approximately 7% of the adult population in the U.S., yet a vast majority of these individuals do not seek treatment. Thus, it is critical to examine models that deliver treatment to them. Computerized Cognitive Bias Modification (CBM) training programs can be effective in targeting interpretation bias, a key cognitive mechanism underlying social anxiety, and have potential for widespread dissemination, especially if they can be delivered via smart phones, which are becoming ubiquitous. However, the efficacy of CBM interpretation training paradigms that are adapted to and delivered via smart phones remains unknown. We present a pilot study to investigate if physiologic data can be used to track the changes over a smartphone-based CBM intervention for social anxiety. In a 3-week open trial, pilot study involving 20 high socially anxious participants, self-report affect ratings, heart rate and accelerometer data were collected using a smartphone and smartwatch before, after, and during the CBM intervention. The study focused on the relationship between accelerometer and heart rate to track change following the intervention. Results provide preliminary evidence for the viability of using physiological data to identify the change in mental state influenced by CBM interventions.
Objectives: Striking histopathological overlap between distinct but related conditions poses a si... more Objectives: Striking histopathological overlap between distinct but related conditions poses a significant disease diagnostic challenge. There is a major clinical need to develop computational methods enabling clinicians to translate heterogeneous biomedical images into accurate and quantitative diagnostics. This need is particularly salient with small bowel enteropathies; Environmental Enteropathy (EE) and Celiac Disease (CD). We built upon our preliminary analysis by developing an artificial intelligence (AI)-based image analysis platform utilizing deep learning convolutional neural networks (CNNs) for these enteropathies. Methods: Data for secondary analysis was obtained from three primary studies at different sites. The image analysis platform for EE and CD was developed using convolutional neural networks (CNNs: ResNet and custom Shallow CNN). Gradient-weighted Class Activation Mappings (Grad-CAMs) were used to visualize the decision making process of the models. A team of medi...
Image classification is central to the big data revolution in medicine. Improved information proc... more Image classification is central to the big data revolution in medicine. Improved information processing methods for diagnosis and classification of digital medical images have shown to be successful via deep learning approaches. As this field is explored, there are limitations to the performance of traditional supervised classifiers. This paper outlines an approach that is different from the current medical image classification tasks that view the issue as multi-class classification. We performed a hierarchical classification using our Hierarchical Medical Image classification (HMIC) approach. HMIC uses stacks of deep learning models to give particular comprehension at each level of the clinical picture hierarchy. For testing our performance, we use biopsy of the small bowel images that contain three categories in the parent level (Celiac Disease, Environmental Enteropathy, and histologically normal controls). For the child level, Celiac Disease Severity is classified into 4 classes...
String Kernel (SK) techniques, especially those using gappedk-mers as features (gk), have obtaine... more String Kernel (SK) techniques, especially those using gappedk-mers as features (gk), have obtained great success in classifying sequences like DNA, protein, and text. However, the state-of-the-art gk-SK runs extremely slow when we increase the dictionary size (Σ) or allow more mismatches (M). This is because current gk-SK uses a trie-based algorithm to calculate co-occurrence of mismatched substrings resulting in a time cost proportional toO(ΣM). We propose afastalgorithm for calculatingGappedk-merKernel usingCounting (GaKCo). GaKCo uses associative arrays to calculate the co-occurrence of substrings using cumulative counting. This algorithm is fast, scalable to largerΣandM, and naturally parallelizable. We provide a rigorous asymptotic analysis that compares GaKCo with the state-of-the-art gk-SK. Theoretically, the time cost of GaKCo is independent of theΣMterm that slows down the trie-based approach. Experimentally, we observe that GaKCo achieves the same accuracy as the state-of-...
In recent years, there has been an exponential growth in the number of complex documentsand texts... more In recent years, there has been an exponential growth in the number of complex documentsand texts that require a deeper understanding of machine learning methods to be able to accuratelyclassify texts in many applications. Many machine learning approaches have achieved surpassingresults in natural language processing. The success of these learning algorithms relies on their capacityto understand complex models and non-linear relationships within data. However, finding suitablestructures, architectures, and techniques for text classification is a challenge for researchers. In thispaper, a brief overview of text classification algorithms is discussed. This overview covers differenttext feature extractions, dimensionality reduction methods, existing algorithms and techniques, andevaluations methods. Finally, the limitations of each technique and their application in real-worldproblems are discussed.
Background Shareable e-scooters have become popular, but injuries to riders and bystanders have n... more Background Shareable e-scooters have become popular, but injuries to riders and bystanders have not been well characterized. The goal of this study was to describe e-scooter injuries and estimate the rate of injury per e-scooter trip. Methods and findings Retrospective review of patients presenting to 180 clinics and 2 hospitals in greater Los Angeles between January 1, 2014 and May 14, 2020. Injuries were identified using a natural language processing (NLP) algorithm not previously used to identify injuries, tallied, and described along with required healthcare resources. We combine these tallies with municipal data on scooter use to report a monthly utilization-corrected rate of e-scooter injuries. We searched 36 million clinical notes. Our NLP algorithm correctly classified 92% of notes in the testing set compared with the gold standard of investigator review. In total, we identified 1,354 people injured by e-scooters; 30% were seen in more than one clinical setting (e.g., emerge...
Copyright (c) 2017 Kamran Kowsari Permission is hereby granted, free of charge, to any person obt... more Copyright (c) 2017 Kamran Kowsari Permission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated documentation files (the "Dataset"), to deal in the dataset without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Dataset, and to permit persons to whom the dataset is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Dataset. If you use this dataset please cite: Referenced paper: HDLTex: Hierarchical Deep Learning for Text Classification Description of Dataset: Here is three datasets which include WOS-11967 , WOS-46985, and WOS-5736 Each folder contains: -X.txt -Y.txt -YL1.txt -YL2.txt X is input data that include text sequences Y is target value YL1 is target value of level one (parent label) YL2 is target value of l...
Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adve... more Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adversely impact normal childhood development. Both conditions require a tissue biopsy for diagnosis and a major challenge of interpreting clinical biopsy images to differentiate between these gastrointestinal diseases is striking histopathologic overlap between them. In the current study, we propose four diagnosis techniques for these diseases and address their limitations and advantages. First, the diagnosis between CD, EE, and Normal biopsies is considered, but the main challenge with this diagnosis technique is the staining problem. The dataset used in this research is collected from different centers with different staining standards. To solve this problem, we use color balancing in order to train our model with a varying range of colors. Random Multimodel Deep Learning (RMDL) architecture has been used as another approach to mitigate the effects of the staining problem. RMDL combines d...
Objective: Develop a deep learning-based methodology using the foundations of systems pathology t... more Objective: Develop a deep learning-based methodology using the foundations of systems pathology to generate highly accurate predictive tools for complex gastrointestinal diseases, using celiac disease (CD) as a prototype. Design: To predict the severity of CD, defined by Marsh-Oberhuber classification, we used deep learning to develop a model based on histopathologic features. Results: The study was based on a pediatric cohort of 124 patients identified with different classes of CD severity. The model predicted CD with an overall 88.7% accuracy with the highest for Marsh IIIc (91.0%; 95% sensitivity; 91% specificity). The model identified EECs as a defining feature of children with Marsh IIIc CD and endocrinopathies which was confirmed using immunohistochemistry. Conclusion: This deep learning image analysis platform has broad applications in disease treatment, management, and prognostication and paves the way for precision medicine.
2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2018
Social anxiety disorder affects approximately 7% of the adult population in the U.S., yet a vast ... more Social anxiety disorder affects approximately 7% of the adult population in the U.S., yet a vast majority of these individuals do not seek treatment. Thus, it is critical to examine models that deliver treatment to them. Computerized Cognitive Bias Modification (CBM) training programs can be effective in targeting interpretation bias, a key cognitive mechanism underlying social anxiety, and have potential for widespread dissemination, especially if they can be delivered via smart phones, which are becoming ubiquitous. However, the efficacy of CBM interpretation training paradigms that are adapted to and delivered via smart phones remains unknown. We present a pilot study to investigate if physiologic data can be used to track the changes over a smartphone-based CBM intervention for social anxiety. In a 3-week open trial, pilot study involving 20 high socially anxious participants, self-report affect ratings, heart rate and accelerometer data were collected using a smartphone and smartwatch before, after, and during the CBM intervention. The study focused on the relationship between accelerometer and heart rate to track change following the intervention. Results provide preliminary evidence for the viability of using physiological data to identify the change in mental state influenced by CBM interventions.
Objectives: Striking histopathological overlap between distinct but related conditions poses a si... more Objectives: Striking histopathological overlap between distinct but related conditions poses a significant disease diagnostic challenge. There is a major clinical need to develop computational methods enabling clinicians to translate heterogeneous biomedical images into accurate and quantitative diagnostics. This need is particularly salient with small bowel enteropathies; Environmental Enteropathy (EE) and Celiac Disease (CD). We built upon our preliminary analysis by developing an artificial intelligence (AI)-based image analysis platform utilizing deep learning convolutional neural networks (CNNs) for these enteropathies. Methods: Data for secondary analysis was obtained from three primary studies at different sites. The image analysis platform for EE and CD was developed using convolutional neural networks (CNNs: ResNet and custom Shallow CNN). Gradient-weighted Class Activation Mappings (Grad-CAMs) were used to visualize the decision making process of the models. A team of medi...
Image classification is central to the big data revolution in medicine. Improved information proc... more Image classification is central to the big data revolution in medicine. Improved information processing methods for diagnosis and classification of digital medical images have shown to be successful via deep learning approaches. As this field is explored, there are limitations to the performance of traditional supervised classifiers. This paper outlines an approach that is different from the current medical image classification tasks that view the issue as multi-class classification. We performed a hierarchical classification using our Hierarchical Medical Image classification (HMIC) approach. HMIC uses stacks of deep learning models to give particular comprehension at each level of the clinical picture hierarchy. For testing our performance, we use biopsy of the small bowel images that contain three categories in the parent level (Celiac Disease, Environmental Enteropathy, and histologically normal controls). For the child level, Celiac Disease Severity is classified into 4 classes...
String Kernel (SK) techniques, especially those using gappedk-mers as features (gk), have obtaine... more String Kernel (SK) techniques, especially those using gappedk-mers as features (gk), have obtained great success in classifying sequences like DNA, protein, and text. However, the state-of-the-art gk-SK runs extremely slow when we increase the dictionary size (Σ) or allow more mismatches (M). This is because current gk-SK uses a trie-based algorithm to calculate co-occurrence of mismatched substrings resulting in a time cost proportional toO(ΣM). We propose afastalgorithm for calculatingGappedk-merKernel usingCounting (GaKCo). GaKCo uses associative arrays to calculate the co-occurrence of substrings using cumulative counting. This algorithm is fast, scalable to largerΣandM, and naturally parallelizable. We provide a rigorous asymptotic analysis that compares GaKCo with the state-of-the-art gk-SK. Theoretically, the time cost of GaKCo is independent of theΣMterm that slows down the trie-based approach. Experimentally, we observe that GaKCo achieves the same accuracy as the state-of-...
In recent years, there has been an exponential growth in the number of complex documentsand texts... more In recent years, there has been an exponential growth in the number of complex documentsand texts that require a deeper understanding of machine learning methods to be able to accuratelyclassify texts in many applications. Many machine learning approaches have achieved surpassingresults in natural language processing. The success of these learning algorithms relies on their capacityto understand complex models and non-linear relationships within data. However, finding suitablestructures, architectures, and techniques for text classification is a challenge for researchers. In thispaper, a brief overview of text classification algorithms is discussed. This overview covers differenttext feature extractions, dimensionality reduction methods, existing algorithms and techniques, andevaluations methods. Finally, the limitations of each technique and their application in real-worldproblems are discussed.
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