From 0c2ae1600ee064cd5d01d7ff8ba6b3ba650ee22b Mon Sep 17 00:00:00 2001
From: shikhareddy <66842982+shikhareddy@users.noreply.github.com>
Date: Thu, 29 Oct 2020 15:17:29 +0530
Subject: [PATCH 1/2] Update README.md
---
README.md | 27 +++++++++++++--------------
1 file changed, 13 insertions(+), 14 deletions(-)
diff --git a/README.md b/README.md
index 0890f99..5ba6493 100644
--- a/README.md
+++ b/README.md
@@ -10,28 +10,27 @@ What is Survival Analysis?
------------------------
**Survival Analysis** involves estimating when an event of interest, \( T \)
-would take places given some features or covariates \( X \). In statistics
-and ML these scenarious are modelled as regression to estimate the conditional
-survival distribution, \( \mathbb{P}(T>t|X) \). As compared to typical
-regression problems, Survival Analysis differs in two major ways:
-
-* The Event distribution, \( T \) has positive support ie.
- \( T \in [0, \infty) \).
-* There is presence of censoring ie. a large number of instances of data are
+would take place given some features or covariates \( X \). In statistics
+and ML, these scenarios are modelled as regression to estimate the conditional
+survival distribution, ℙ (T > t | X).
+As compared to typical regression problems, Survival Analysis differs in two major ways:
+
+* The Event distribution, \( T \) has positive support i.e. T ∈ [0, ∞).
+* There is presence of censoring i.e. a large number of instances of data are
lost to follow up.
Deep Survival Machines
----------------------
-
+
-**Deep Survival Machines (DSM)** is a fully parametric approach to model
-Time-to-Event outcomes in the presence of Censoring first introduced in
+**Deep Survival Machines (DSM)** is a **fully parametric** approach to model
+Time-to-Event outcomes in the presence of Censoring, first introduced in
[\[1\]](https://arxiv.org/abs/2003.01176).
In the context of Healthcare ML and Biostatistics, this is known as 'Survival
Analysis'. The key idea behind Deep Survival Machines is to model the
-underlying event outcome distribution as a mixure of some fixed \( k \)
+underlying event outcome distribution as a mixure of some fixed \( K \)
parametric distributions. The parameters of these mixture distributions as
well as the mixing weights are modelled using Neural Networks.
@@ -115,5 +114,5 @@ GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with Deep Survival Machines. If not, see .
-
-
+
+
From 049f90a0393fe480996a19f6a274854f8515ddbe Mon Sep 17 00:00:00 2001
From: shikhareddy <66842982+shikhareddy@users.noreply.github.com>
Date: Thu, 29 Oct 2020 15:19:10 +0530
Subject: [PATCH 2/2] Update README.md
---
README.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/README.md b/README.md
index 5ba6493..f70ee83 100644
--- a/README.md
+++ b/README.md
@@ -115,4 +115,4 @@ You should have received a copy of the GNU General Public License
along with Deep Survival Machines. If not, see .
-
+