Discriminative and Distinct Phenotyping by Constrained Tensor Factorization

By Yejin Kim, Robert E. El-Kareh, Jimeng Sun, Hwanjo Yu, Xiaoqian Jiang. 2017

40 citations recorded by the source corpus.

Adoption of Electronic Health Record (EHR) systems has led to collection of massive healthcare data, which creates oppor- tunities and challenges to study them. Computational phenotyping offers a promising way to convert the sparse and complex data into meaningful concepts that are interpretable to healthcare givers to make use of them. We propose a novel su- pervised nonnegative tensor factorization methodology that derives discriminative and distinct phenotypes. We represented co-occurrence of diagnoses and prescriptions in EHRs as a third-order tensor, and decomposed it using the CP algorithm. We evaluated discriminative power of our models with an Intensive Care Unit database (MIMIC-III) and demonstrated superior performance than state-of-the-art ICU mortality calculators (e.g., APACHE II, SAPS II). Example of the resulted phenotypes are sepsis with acute kidney injury, cardiac surgery, anemia, respiratory failure, heart failure, cardiac arrest, metastatic cancer (requiring ICU), end-stage dementia (requiring ICU and transitioned to comfort-care), intraabdominal conditions, and alcohol abuse/withdrawal.

Subfields: Artificial Intelligence, Computational Mathematics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jian Lou
2. Joyce Ho
3. Xiaoqian Jiang
4. Jing Ma
5. Payman Mohassel

In Computational Mathematics

1. Xiaoqian Jiang
2. Joyce Ho

In Molecular Biology

1. Xiaoqian Jiang
2. Jing Ma

Citations in the corpus

• Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis (2019, methodology): “For better interpretability, we adopt the rule in [19] and select 202 procedures ICD-9 codes and 316 diagnoses codes that have the”