Alexander Kotov

Conversational Entity Retrieval from a Knowledge Graph using Aggregation of Fine-grained Relevance Signals with Graph Convolutions and Self-Attention

Mona Zamiri, Alexander Kotov

The recently introduced task of Conversational Entity Retrieval from a Knowledge Graph (CER-KG) presents unique research challenges due to the complexity of the queries along with the necessity to consider KG structure and the context of an information-seeking dialog. This paper proposes …

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The Data-Driven Definition of “Complication” in Medicine

Gleb Danilov, Alexander Kotov, Anton Gerasimovich Nazarenko, Yu. V. Strunina et autres

The concept of "complication" is widely used in the medical domain to designate unfavorable events in the course of medical care.However, the medical community has not yet established a strict and generally accepted definition of "complication".This makes it much harder to systematically …

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Neural Conversational Agent for Weight Loss Counseling: Protocol for an Implementation and Feasibility Study

Alexander Kotov, April Idalski Carcone, Elizabeth Towner

BACKGROUND: Obesity is a common, serious and costly chronic disease. Current clinical practice guidelines recommend that providers augment the longitudinal care of people living with obesity with consistent support for the development of self-efficacy and motivation to modify their lifestyle behaviors. Lifestyle …

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Neural Conversational Agent for Weight Loss Counseling: Protocol for an Implementation and Feasibility Study (Preprint)

Alexander Kotov, April Idalski Carcone, Elizabeth Towner

BACKGROUND Obesity is a common, serious and costly chronic disease. Current clinical practice guidelines recommend that providers augment the longitudinal care of people living with obesity with consistent support for the development of self-efficacy and motivation to modify their lifestyle behaviors. Lifestyle …

Benchmark and Neural Architecture for Conversational Entity Retrieval from a Knowledge Graph

Mona Zamiri, Yao Qiang, Fedor Nikolaev, Dongxiao Zhu et autres

This paper introduces a novel information retrieval (IR) task of Conversational Entity Retrieval from a Knowledge Graph (CER-KG), which extends non-conversational entity retrieval from a knowledge graph (KG) to the conversational scenario. The user queries in CER-KG dialog turns may rely on …

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Machine and deep learning methods for clinical outcome prediction based on physiological data of COVID-19 patients: a scoping review

Dmitriy Viderman, Alexander Kotov, Maxim N. Popov, Yerkin G. Abdildin

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Joint Word and Entity Embeddings for Entity Retrieval from a Knowledge Graph

Fedor Nikolaev, Alexander Kotov

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Tensor Decomposition for Sub-typing of Complex Diseases based on Clinical and Genomic Data

Diana Diaz, Aliccia B. Bollig-Fischer, Alexander Kotov

It has long been understood that stratification of patients into fine-grained cohorts is a foundation of accurate diagnosis and effective treatment of complex diseases, such as cancer. Nevertheless, cancer therapies still fail or cause unnecessary suffering to many patients, which suggests that …

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Bayesian approach to incorporating different types of biomedical knowledge bases into information retrieval systems for clinical decision support in precision medicine

Saeid Balaneshin-kordan, Alexander Kotov

By providing clinicians with information regarding treatment options for molecular sub-types of complex diseases with genetic origin, such as cancer, information retrieval (IR) systems play an important role in precision medicine. In this paper, we propose Bayesian Precision Medicine (BPM), a novel …

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Developing Machine Learning Models for Behavioral Coding

April Idalski Carcone, Mehedi Hasan, Gwen Alexander, Ming Dong et autres

OBJECTIVE: The goal of this research is to develop a machine learning supervised classification model to automatically code clinical encounter transcripts using a behavioral code scheme. METHODS: We first evaluated the efficacy of eight state-of-the-art machine learning classification models to recognize patient-provider …

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Deep Neural Architectures for Discourse Segmentation in E-Mail Based Behavioral Interventions.

Mehedi Hasan, Alexander Kotov, Sylvie Naar, Gwen Alexander et autres

Communication science approaches to develop effective behavior interventions, such as motivational interviewing (MI), are limited by traditional qualitative coding of communication exchanges, a very resource-intensive and time-consuming process. This study focuses on the analysis of e-Coaching sessions, behavior interventions delivered via email …

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Identifying Effective Motivational Interviewing Communication Sequences Using Automated Pattern Analysis

Mehedi Hasan, April Idalski Carcone, Sylvie Naar, Susan S Eggly et autres

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