Merge pull request #54 from YerevaNN/update_readme

Update readme

README.md

@@ -2,11 +2,13 @@ MIMIC-III Benchmarks
 =========================
 
 [![Join the chat at https://gitter.im/YerevaNN/mimic3-benchmarks](https://badges.gitter.im/YerevaNN/mimic3-benchmarks.svg)](https://gitter.im/YerevaNN/mimic3-benchmarks?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
-Python suite to construct benchmark machine learning datasets from the MIMIC-III clinical database. Currently, we are focused on building a multitask learning benchmark dataset that includes four key inpatient clinical prediction tasks that map onto core machine learning problems: prediction of mortality from early admission data (classification), real-time detection of decompensation (time series classification), forecasting length of stay (regression), and phenotype classification (multilabel sequence classification).
+
+Python suite to construct benchmark machine learning datasets from the MIMIC-III clinical database. Currently, the benchmark datasets cover four key inpatient clinical prediction tasks that map onto core machine learning problems: prediction of mortality from early admission data (classification), real-time detection of decompensation (time series classification), forecasting length of stay (regression), and phenotype classification (multilabel sequence classification).
 
 
 ## News
 
+* 2017 December 8: This work was presented as a spotlight presentation at NIPS 2017 [Machine Learning for Health Workshop](https://ml4health.github.io/2017/).
 * 2017 March 23: We are pleased to announce the first official release of these benchmarks. We expect to release a revision within the coming months that will add at least ~50 additional input variables. We are likewise pleased to announce that the manuscript associated with these benchmarks is now [available on arXiv](https://arxiv.org/abs/1703.07771).
 
 ## Citation
@@ -69,7 +71,7 @@ Here are the required steps to build the benchmark. It assumes that you already
 
        python -m mimic3benchmark.scripts.validate_events data/root/
 
-4. The next command breaks up per-subject data into separate episodes (pertaining to ICU stays). Time series of events are stored in ```{SUBJECT_ID}/episode{#}_timeseries.csv``` (where # counts distinct episodes) while episode-level information (patient age, gender, ethnicity, height, weight) and outcomes (mortality, length of stay, diagnoses) are stores in ```{SUBJECT_ID}/episode{#}.csv```. This script requires two files, one that maps event ITEMIDs to clinical variables and another that defines valid ranges for clinical variables (for detecting outliers, etc.).
+4. The next command breaks up per-subject data into separate episodes (pertaining to ICU stays). Time series of events are stored in ```{SUBJECT_ID}/episode{#}_timeseries.csv``` (where # counts distinct episodes) while episode-level information (patient age, gender, ethnicity, height, weight) and outcomes (mortality, length of stay, diagnoses) are stores in ```{SUBJECT_ID}/episode{#}.csv```. This script requires two files, one that maps event ITEMIDs to clinical variables and another that defines valid ranges for clinical variables (for detecting outliers, etc.). **Outlier detection is disabled in the current version**.
 
        python -m mimic3benchmark.scripts.extract_episodes_from_subjects data/root/