449 citations · 520 across the 22 of their papers we have counts for
10 papers · 1 filter
Hierarchical Target-Attentive Diagnosis Prediction in Heterogeneous Information Networks
Anahita Hosseini, Tyler Davis, Majid Sarrafzadeh
We introduce HTAD, a novel model for diagnosis prediction using Electronic Health Records (EHR) represented as Heterogeneous Information Networks. Recent studies on modeling EHR ha…
Cost-Sensitive Feature-Value Acquisition Using Feature Relevance
Kimmo Kärkkäinen, Mohammad Kachuee, Orpaz Goldstein +1
In many real-world machine learning problems, feature values are not readily available. To make predictions, some of the missing features have to be acquired, which can incur a cos…
Group-Connected Multilayer Perceptron Networks
Mohammad Kachuee, Sajad Darabi, Shayan Fazeli +1
Despite the success of deep learning in domains such as image, voice, and graphs, there has been little progress in deep representation learning for domains without a known structu…
Unsupervised Representation for EHR Signals and Codes as Patient Status Vector
Sajad Darabi, Mohammad Kachuee, Majid Sarrafzadeh
Effective modeling of electronic health records presents many challenges as they contain large amounts of irregularity most of which are due to the varying procedures and diagnosis…
Target-Focused Feature Selection Using a Bayesian Approach
Orpaz Goldstein, Mohammad Kachuee, Kimmo Karkkainen +1
In many real-world scenarios where data is high dimensional, test time acquisition of features is a non-trivial task due to costs associated with feature acquisition and evaluating…
TAPER: Time-Aware Patient EHR Representation
Sajad Darabi, Mohammad Kachuee, Shayan Fazeli +1
Effective representation learning of electronic health records is a challenging task and is becoming more important as the availability of such data is becoming pervasive. The data…
Generative Imputation and Stochastic Prediction
Mohammad Kachuee, Kimmo Karkkainen, Orpaz Goldstein +2
In many machine learning applications, we are faced with incomplete datasets. In the literature, missing data imputation techniques have been mostly concerned with filling missing…
Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams
Mohammad Kachuee, Orpaz Goldstein, Kimmo Karkkainen +2
In many real-world learning scenarios, features are only acquirable at a cost constrained under a budget. In this paper, we propose a novel approach for cost-sensitive feature acqu…
Cost-Sensitive Diagnosis and Learning Leveraging Public Health Data
Mohammad Kachuee, Kimmo Karkkainen, Orpaz Goldstein +2
Traditionally, machine learning algorithms rely on the assumption that all features of a given dataset are available for free. However, there are many concerns such as monetary dat…
Unsupervised Prediction of Negative Health Events Ahead of Time
Anahita Hosseini, Majid Sarrafzadeh
The emergence of continuous health monitoring and the availability of an enormous amount of time series data has provided a great opportunity for the advancement of personal health…