This document summarizes a presentation given at the IntelliSys 2018 conference in London. The presentation proposed a machine learning approach to preventive healthcare that integrates data from electronic health records, home visits, and other sources to compute risk scores for elderly outpatients. It discussed using models like hidden Markov models and conditional random fields to analyze sensor and medical data. The approach aims to generate risk indicators to develop new clinical pathways and improve population health outcomes.

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IEEE BigDataService 2016, Oxford, UK
Machine Learning Infused Preventive Healthcare for High-Risk Outpatient Elderly
Anitha Ilapakurti, Santosh Kedari, Sharat Kedari, Jaya Shankar Vuppalapati, Chandrasekar Vuppalapati, Rajasekar Vuppalapati
Hanumayamma Innovations and Technologies ,Inc.

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Source: http://saiconference.com/Downloads//IntelliSys2017/Agenda.pdf
http://saiconference.com/IntelliSys2018/Agenda

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Session 18: Smart Healthcare
Presentation Room: Ludgate Suite
September 7th, 2018
London, UK
IntelliSys 2018, London, UK
Authors: Anitha Ilapakurti, Santosh Kedari, Sharat Kedari, Jaya Shankar Vuppalapati, Chandrasekar Vuppalapati, Mahesh Gudivada
Hanumayamma Innovations and Technologies Inc.
Conference Venue is America Square Conference Centre
Address: 1 America Square
17 Crosswall
London EC3N 2LB, United Kingdom
Tel: 020 7706 7700
Q (T) ‫׬‬ 𝑄out (T, Pin) 𝑑𝐹Fpn
Machine Learning Infused Preventive Healthcare for High-Risk Outpatient Elderly

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Source: http://saiconference.com/Downloads//IntelliSys2017/Agenda.pdf

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IntelliSys 2018, London, UKAll rights reserved – Hanumayamma Innovations and Technologies, Inc.

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Machine Learning Infused Preventive Healthcare
Preventive healthcare reduces the significant economic burden of disease in
addition to improving the length and quality of outpatients’ lives.
IntelliSys 2018, London, UK

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Machine Learning (ML) Infused Preventive Healthcare goes one step ahead
by application of algorithms for collection of multi-scale clinical,
biomedical, contextual, and environmental data about each outpatient (e.g.,
in Electronic Health Record (EHR)s, personal health records - PHR, etc.),
unified and extensibility of metadata standards, and decision support tools to
facilitate optimized patient-centered, evidence-based
decisions.
IntelliSys 2018, London, UK
Machine Learning Infused Preventive Healthcare
TF (d, t) =
ቊ
𝟎 𝒊𝒇 𝒇𝒓𝒆𝒒 𝒅, 𝒕 = 𝟎
𝟏 + 𝐥𝐨𝐠( 𝟏 + 𝐥𝐨𝐠(𝒇𝒓𝒆𝒒 𝒅, 𝒕 )) 𝒐𝒕𝒉𝒆𝒓𝒘𝒊𝒔𝒆.

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Through interweaving data, importantly, from traditional healthcare
data sources such as outpatient Electronic Health Records (EHR) and
revolutionary data sources such as mobile, voice and sensor generated
outpatient contextual and lifestyle data, the machine learning (ML) infused
preventive health care breeds new clinical pathways that are
not only beneficial to the individual outpatients but can also improve
overall population safety and health outcomes.
IntelliSys 2018, London, UK
Machine Learning Infused Preventive Healthcare
IDF (t) = 𝒍𝒐𝒈
𝟏+ 𝒅
|𝒅𝒕|

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Data Science Company
IntelliSys 201, London, UK
Proactive
Preventive
Prognostics
𝑃 𝑦 𝐷
= න 𝑃 𝑦 𝑤 𝑃 𝑤 𝐷 𝑑𝑤 ≈
1
𝑀
෍
𝑖=1
𝑀
𝑃 𝑦 𝑤𝑖 ,
𝑳𝑩𝑷 𝑷, 𝑹 𝒙𝒄, 𝒚𝒄 = ෍
𝒑=0
𝒑−1
𝒔 𝒈𝒑 − 𝒈𝒄 2 𝒑

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Engineering Architecture & Solution
Interweaving voice services generated data
with outpatient Electronic Health Records (EHR)
could breed new clinical pathways that are
not only beneficial to the individual outpatients but
can also improve overall population health outcomes.
IntelliSys 2017 conference, London, UK

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IntelliSys 2018, London, UK
Risk Stratification Models
IDF (t) = 𝒍𝒐𝒈
𝟏+ 𝒅
|𝒅𝒕|
Hierarchical Condition
Categories (HCC)
Center for Medicare & Medical Services implemented for
Medicare Advantage Plan. HCC contains 70 categories
selected from ICD 10 Codes. The International
Classification of Diseases – 10 (ICD –10) identifies
individual’s health conditions. The ICD-10 Codes (more
than 9000) map to 79 HCCa codes in the Risk Adjustment
model.

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IntelliSys 2018, London, UK
Risk Stratification Models

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IntelliSys 2018, London, UK
Risk Stratification Models
Adjusted Clinical
Groups (ACG)
Developed by the Johns Hopkins University to
predict morbidity – used for inpatient and
outpatient
Support =
𝑿 𝑼 𝒀 .𝒄𝒐𝒖𝒏𝒕
𝒏

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IntelliSys 2018, London, UK
Risk Stratification Models
Elder Risk Assessment
(ERA)
ERA model identifies patient who’re 60 years
and older – who’re at risk for hospitalization
and Emergency Department (ED) visits.
Confidence =
𝑿 𝑼 𝒀 .𝒄𝒐𝒖𝒏𝒕
𝑿.𝒄𝒐𝒖𝒏𝒕

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IntelliSys 2018, London, UK
Risk Stratification Models
Minnesota Tiering
(MT)
The Model groups patients into one of five
complexity tiers based on their number of
major conditions

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IntelliSys 2018, London, UK
Risk Stratification Models

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Machine Learning
• Supervised & Un-supervised algorithms
EHR
IntelliSys 2017, London, UK

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IntelliSys 2018, London, UK
Hidden Markov Model (HMM)
The HMM is a popular tool for modeling data that can be characterized by an
underlying process generating a sequence of observations, such as sensor
events. HMMs are generative probabilistic models consisting of a hidden
variable and an observable variable at each time step.

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IntelliSys 2018, London, UK
Hidden Markov Model (HMM)
The shaded nodes represent the observed variables—the sensor events or
the feature vectors from the sensor stream (Example: heart pulse rate). The
white nodes represent the hidden variables (physical activity), which
correspond to the underlying activity labels.

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IntelliSys 2018, London, UK
Conditional Random Field (CRF)
A CRF is a discriminative probabilistic graphical model that is used for
segmenting and labeling sequence data. While it comes in different forms,
the form that most closely resembles its generative counterpart, the HMM,
and that is commonly used for activity recognition, is known as a linear chain
CRF.

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IntelliSys 2018, London, UK
Conditional Random Field (CRF)
In contrast to classifiers such as naïve Bayes, a CRF can consider the "context"
of a sample (neighboring samples) while classifying a sample. To understand
the relevance of CRF for activity recognition, consider the problem where a
sequence of (hidden) activities generates a sequence of sensor events that
can be observed. As an activity can generate multiple sensor events, it is
worthwhile to consider the preceding and succeeding sensor events as the
context for an event that has to be classified. These relationships can be
modeled using the graphical structure of a CRF.

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https://www.cms.gov/regulations-and-guidance/legislation/ehrincentiveprograms/clinicalqualitymeasures.html
IEEE BigDataService 2016, Oxford, UK

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IEEE BigDataService 2016, Oxford, UKIntelliSys 2017 conference, London, UK

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IEEE BigDataService 2016, Oxford, UK

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NQF 22: https://www.google.co.uk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=6&ved=0ahUKEwjPppWT-
pHWAhWsDMAKHWKGD9cQFghNMAU&url=http%3A%2F%2Fwww.qualityforum.org%2FWorkArea%2Flinkit.aspx%3FLinkIdentifier%3Did%26ItemID%3D
69392&usg=AFQjCNFEy63K1EHHtmd_E3re47dJLDtGRQ

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Case Study

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IntelliSys 2017 conference, London, UK

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IntelliSys 2017 conference, London, UK
http://sanjeevani-ehr.com

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Conclusion
This paper presented an innovative approach to preventive healthcare by integrating outpatient data
domains with stratification models that compute Risk Score for outpatients based on data collected via
EHR, House calls and Home Visits.
We staunchly believe that machine learning infused preventive healthcare analytics will play an
important role in generating valuable outpatient high-risk indicators that could result in developing
newer clinical pathways to address outpatient healthcare issues. In other words, machine learning with
traditional and revolutionary data domains could be considered as new input in continuous healthcare
improvement process.
IntelliSys 2018 conference, London, UK

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Questions?
Technologies and Innovations for helping humanity
IntelliSys 2018 conference, London, UK

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References
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http://onlinelibrary.wiley.com/doi/10.1002/14651858.CD007457.pub2/f ull , Access Date: November
30, 2017
Stephan P. Kudyba , “ Healthcare Informatics: Improving Efficiency through Technology, Analytics, and
Management”, April 16 2016, Auerbach Publications; Revised edition, ISBN-13: 978- 1498746359
Jessica Davis, “Remote patient monitoring market booming amid readmission fines, doctor shortages,
report says”, December 15, 2015
IEEE BigDataService 2018, London, UK