MLHO (pronounced as melo) is a thinkin’ Machine Learning framework that implements iterative sequential representation mining, and feature and model selection to predict health outcomes.


You can install the released version of mlho from Github with:


Data model

To implement MLHO you’ll need 2 tables, which can be extracted from any clinical CMD. The current examples are based on the i2b2 star schema.

1- a table with outcome labels (called labeldt) and patient numbers

patient_num label
character factor

2- a patient clinical data table (called dbmart) with 3 columns. Concepts are used as features by MLHO.

patient_num start_date phenx
character date character

The column phenx contains the entire feature space. In an i2b2 data model, for instance, this column is the equivalent of concept_cd.

3- a demographic table is optional, but recommended.

patient_num age gender
character character character character

see the instructions on how to use the MLHO package on the articles page