IEEE International Conference on Healthcare Informatics (ICHI)
2020 IEEE International Conference on Healthcare Informatics (ICHI)
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Proceedings
The proceedings of this conference will be available for purchase through Curran Associates.
Healthcare Informatics (ICHI), 2020 IEEE International Conference on
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Predicting Clinical Decisions and Outcomes
A Deep Reinforcement Learning Approach for Type 2 Diabetes Mellitus Treatment
Zhuo Liu;Linong Ji;Xuehan Jiang;Wei Zhao;Xiyang Liao;Tingting Zhao;Siqi Liu;Xingzhi Sun;Gang Hu;Mengling Feng;Guotong Xie
Publication Year: 2020,Page(s):1 - 9
Cited by: Papers (14)
Type 2 diabetes mellitus (T2DM) is a chronic disease that requires continuous treatments. T2DM treatments aim to achieve not only short-term but, more importantly, long-term control of the patient’s glucose to a normal level. We believe Reinforcement Learning (RL) can be an effective approach to learn and further recommend the ideal sequence of treatments that optimize the patient’s long-term outc...Show More
Machine Learning and Deep Learning Approaches to Quantify Respiratory Distress Severity and Predict Critical Alarms
Rohit Pardasani;Rupanjali Chaudhuri;Navchetan Awasthi;Mansi Goel
Publication Year: 2020,Page(s):1 - 11
Cited by: Papers (2)
Respiratory distress (RD) is often the premonition and accompanying symptom of many critical conditions that may eventually lead to mortality among patients admitted in intensive care units (ICUs). The contemporary monitoring and alarm systems often fail to give timely and assertive alert of dangerously soaring RD, thus obscuring accurate prognosis. These systems do not capture the information pro...Show More
Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score
Zhiliang Wu;Yinchong Yang;Yunpu Ma;Yushan Liu;Rui Zhao;Michael Moor;Volker Tresp
Publication Year: 2020,Page(s):1 - 11
Randomized controlled trials typically analyze the effectiveness of treatments with the goal of making treatment recommendations for patient subgroups. With the advance of electronic health records, a great variety of data has been collected in clinical practice, enabling the evaluation of treatments and treatment policies based on observational data. In this paper, we focus on learning individual...Show More
Network Models: Diagnosis, Co-Expression and Disease Progression
Causal Bayesian Networks for Medical Diagnosis: A Case Study in Rheumatoid Arthritis
Ali Fahmi;Amy MacBrayne;Evangelia Kyrimi;Scott McLachlan;Frances Humby;William Marsh;Costantino Pitzalis
Publication Year: 2020,Page(s):1 - 7
Cited by: Papers (7)
Bayesian network (BN) models have been widely applied in medical diagnosis. These models can be built from different sources, including both data and domain knowledge in the form of expertise and literature. Although it might seem simple to depend only on data, this will not be the best approach unless a large dataset is available. In this study, we present a knowledge-based BN modelling approach ...Show More
Characterization of Analytic and Experimental Uncertainty of RNA-seq Co-expression Network Determination: Application to SCA2
Publication Year: 2020,Page(s):1 - 11
Bioinformatics sequencing pipelines produce results that contain a degree of uncertainty which stem from a variety of sources. For example, uncertainty can arise from sampling bias during sample preparation, sequencing platform bias, alignment errors and/or mathematical uncertainty from algorithmic assumptions. While these sources are well-known, few studies exist on the overall quantitative deter...Show More
Higher-order Networks of Diabetes Comorbidities: Disease Trajectories that Matter
Steven J. Krieg;Daniel H. Robertson;Meeta P. Pradhan;Nitesh V. Chawla
Publication Year: 2020,Page(s):1 - 11
Cited by: Papers (10)
Networks are powerful and flexible structures for modeling relationships in medical and biological systems, but in a traditional first-order network representation, an edge typically expresses a relationship between a single pair of nodes. In order to analyze complex relationships between groups of nodes, researchers rely on combined sets of these pairwise connections, which can misrepresent the t...Show More
Methodological Advances in Biomedical Data Analysis: Longitudinal, Hierarchical and Multi-Task Models
Heatmaps for Visual Explainability of CNN-Based Predictions for Multivariate Time Series with Application to Healthcare
Publication Year: 2020,Page(s):1 - 8
Cited by: Papers (20)
The need for explainable AI is becoming increasingly important for critical decision domains such as healthcare for example. In this context, this paper is concerned with explaining the predictions of Convolutional Neural Networks (CNNs) with particular focus on multivariate Time Series (TS) problems. The approach is based on heatmaps as a visual means to highlight the significant variables over t...Show More
Hierarchical Deep Convolutional Neural Networks for Multi-category Diagnosis of Gastrointestinal Disorders on Histopathological Images
Rasoul Sali;Sodiq Adewole;Lubaina Ehsan;Lee A. Denson;Paul Kelly;Beatrice C. Amadi;Lori Holtz;Syed Asad Ali;Sean R. Moore;Sana Syed;Donald E. Brown
Publication Year: 2020,Page(s):1 - 6
Cited by: Papers (15)
Deep convolutional neural networks (CNNs) have been successful for a wide range of computer vision tasks including image classification. A specific area of application lies in digital pathology for pattern recognition in tissue-based diagnosis of gastrointestinal (GI) diseases. This domain can utilize CNNs to translate histopathological images into precise diagnostics. This is challenging since th...Show More
A Dynamic Heuristic Algorithm for Management of Public Health Emergencies in Unreliable Settings
Sampson E. Akwafuo;Christopher Ihinegbu;Joshua Urbanovsky;Armin R. Mikler
Publication Year: 2020,Page(s):1 - 11
Cited by: Papers (4)
Prompt and efficient intervention is vital in reducing the impacts of sudden disease outbreaks, natural disasters, or terrorist attacks. This can only be achieved if there is a fit-for-purpose logistics plan in place, incorporating geographical, time, location demand, and vehicular capacity constraints. This response plan will be immediately activated at the onset of such events. Our algorithm inv...Show More
Detecting Potential Adverse Drug Reactions of Preschool ADHD Treatment Using Health Consumer-Generated Content
Publication Year: 2020,Page(s):1 - 6
Cited by: Papers (6)
Post-marketing surveillance of drugs is important because many adverse drug reactions (ADRs) cannot be detected in the pre-marketing clinical trials due to their limited scale and duration. This is especially true for pediatric patients who are often excluded from clinical trials out of ethical considerations. As a result, drug safety and efficacy data on children are largely missing, and pediatri...Show More
A Multi-Task Learning Approach to Personalized Progression Modeling
Publication Year: 2020,Page(s):1 - 9
Modeling disease progression is an active area of research. Many computational methods for progression modeling have been developed but mostly at population levels. In this paper, we formulate a personalized disease progression modeling problem as a multi-task regression problem where the estimation of progression scores at different time points is defined as a learning task. We introduce a Person...Show More
GRU-DF: A Temporal Model with Dynamic Imputation for Missing Target Values in Longitudinal Patient Data
Publication Year: 2020,Page(s):1 - 7
Cited by: Papers (1)
Temporal models are desirable in studying progressive diseases because the data are typically collected at regular time intervals. However, such clinical data often contain many missing entries, including those from the target variable that we are interested in predicting. Standard imputation techniques (e.g., linear interpolation) are inappropriate in treating missing target observations because ...Show More
Learning to Treat, Stratify and Categorize Individuals
Safe Reinforcement Learning for Sepsis Treatment
Publication Year: 2020,Page(s):1 - 7
Cited by: Papers (11)
Sepsis, a life-threatening illness, is estimated to be the primary cause of death for 50,000 people a year in the UK and many more worldwide. Managing the treatment of sepsis is very challenging as it is frequently missed at an early stage and the optimal treatment is not yet clear. There are promising attempts to use Reinforcement Learning (RL) to learn optimal strategies to treat sepsis patients...Show More
Stratification of patients with Alzheimer’s disease based on longitudinal neuropsychological tests
Publication Year: 2020,Page(s):1 - 7
Cited by: Papers (1)
Alzheimer's disease represents a heterogeneous neurodegenerative disorder that affects millions of people worldwide. The heterogeneity of the disorder slows research on the physiological underpinnings of the disorder and impedes development of treatments. We modeled for 329 patients of the ADNI database the progression of their detailed cognitive abilities over a 2-year period and found three dist...Show More
Underlying Cause of Death Identification from Death Certificates via Categorical Embeddings and Convolutional Neural Networks
Publication Year: 2020,Page(s):1 - 6
Cited by: Papers (4)
Information about causes of death are collected by filling death certificates according to a standard defined by WHO. Conditions are expressed by means of the International Statistical Classification of Diseases and Related Health Problems, revision 10. Starting from such information, the so-called Underlying Cause of Death is selected with statistical and epidemiological aims. This task is usuall...Show More
Automated Classification of Depression Severity Using Speech - A Comparison of Two Machine Learning Architectures
Publication Year: 2020,Page(s):1 - 4
Cited by: Papers (7)
Depression affects approximately 300 million people worldwide, resulting in significant suffering and economic costs. Millions of sufferers remain undiagnosed and untreated due to a shortage of trained personnel, social stigma, and expensive treatments. Two novel machine learning architectures, used to predict depression severity from audio recordings, are presented and compared in this study. The...Show More
Automated Assessment of Activities and Cognition
Personalized Assessment of Arousal and Valence from Videos
Matthew Pediaditis;Anca-Nicoleta Ciubotaru;Thomas Brunschwiler;Maria Gabrani
Publication Year: 2020,Page(s):1 - 8
Human behavior is influenced by numerous subjective factors such as the environment, culture, hormones, genes etc. This makes the development of a one-size-fits-all behavioral model for emotion recognition challenging, especially in the domain of affect recognition. In this paper we present a method to classify and assess arousal and valence from video in a personalized way. We represent the inher...Show More
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The proceedings of this conference will be available for purchase through Curran Associates.
Healthcare Informatics (ICHI), 2020 IEEE International Conference on
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