BBig COPData Study: Predictive factors for hospitalizations in COPD patients
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What is the Big COPData Study about?
Chronic obstructive pulmonary disease (COPD) was the fifth leading cause of death in the world in 1990 and is now the third leading cause of death. Many people suffer from this disease or its complications for many years and die prematurely
- First study applying Big Data, Machine Learning and Natural Language Processing aimed to predict hospitalizations in COPD patients across Europe, Canada and USA.
- We aim to identify those factors that are potentially associated with hospital admissions in patients with COPD across Europe, Canada and USA in order to develop a risk prediction model for hospitalization.
- This is an observational, descriptive study, using data captured from Electronic Health Records (EHRs). Time span of data is the last 5 years of clinical practice included in the EHRs
Software application SAVANA
We use a software application (ie SAVANA), created in the context of the era of electronic health, to be able to reuse the information included in EHRs.
Contents of the EHRs
Powerful natural language processing (NLP) free-text analysis engine, capable of meaningfully interpreting the contents of the EHRs, regardless of the management system in which it operates.
Automated predictive model
This machine learning analytical method can be used to build a flexible, customized and automated predictive model using the information available in EHRs.
Dr. Ignacio H. Medrano.
Chief Medical Officer, Medsavana SL.
Chief Executive Officer, Medsavana SL.
Ana López Ballesteros.
RWE Generation Specialist, Medsavana SL.
Dr. Julio Ancochea.
Hospital Universitario de La Princesa
Dr. Alberto Fernández.
Hospital Universitario de Vigo
Dr. Borja Cosio.
Hospital Universitario Son Espases
Dr. José Luis Izquierdo.
Hospital Universitario de Guadalajara
Dr. José Luis López-Campos.
Hospital Universitario Virgen del Rocío
Dr. Marc Miravitlles.
Hospital Universitario Vall d’Hebron
Dr. José Miguel Rodríguez.
Hospital Universitario de Alcalá
Dr. Juan José Soler-Cataluña.
Hospital Universitario Arnau de Vilanova
Dr. Joan.B. Soriano.
Hospital Universitario de La Princesa
Defining clinical characteristics and predictive factors of evolution of patients with COVID-19 across Europe
Big Data defines a new method of generating knowledge that is possible due to two complimentary phenomena:
- Exponential accumulation of data, thanks to network collaboration (internet)
- Growing computing capacity to process it.
Big Data implies data reuse across different large volume databases with exploration purposes, other than those for which they were originally structured and populated.
Big Data is an extension of Statistics, as it virtually manages “the entire” set of events related to the studied fact, so that it interrelates a large enough number of variables, in order to infer correlations where the human mind is not able to do so.
Medical information is estimated to double every 5 years.
Artificial intelligence describes the technology that allows computers to perform operations originally attributed uniquely to humans, such as pattern recognition, language, prediction…
The process by which machines are able to learn after having seen numerous examples of the same element is known as «machine learning».
This computational task has become exponentially more efficient (it doubles its capacity every 2 months) thanks to the imitation of human neural networks, in what is called «deep learning».
The main applications of artificial intelligence in healthcare are:
- Medical natural language processing, which allows to exploit free or unstructured text from electronic health records.
- Identification of diagnostic or therapeutic patterns, which resemble the common practice of physicians but derived from insights gained from the collective data sets.
- Predictive analysis, which enables the anticipation of clinical events from patient clusters thereby stratifying their risk.
Electronic Health Records
The data recorded by clinicians during their usual practice generates a huge amount of valuable information. This is the representation of what happens with the attended casuistry in the real world, under the uncertain conditions of the environment.
A fundamental requirement in order to accelerate and expand the extraction process is the implementation of electronic health records systems, which not only allow information sharing (interoperability among levels of care) but also the reuse of it.
We can divide the reuse of the EHR into images, numbers and text:
- Images: more easily exploitable by an artificial intelligence and already existing cases today that overcome the predictive and diagnostic capacity of humans (the case of Google Deepmind diagnosing diabetic retinopathy, melanoma, breast cancer, pneumonia on chest x-ray...)
- Numbers: also, existing success cases today (prediction in ICU, prediction of hospital mortality, readmissions, stays...)
Medical Natural Language
It is just not possible for the clinician to structure or codify every concept recorded in health records.
The granularity of information cannot be achieved unless free text interpretation techniques are used, which can capture all the richness of them. These techniques are grouped under one of the branches of artificial intelligence, called «Natural Language Processing».
It implies the combination of several techniques, in order to detect concepts and relationships among them. Relying on linguistic tools, statistics, databases and medical knowledge, this technology is able to disambiguate the particularities of human language: spelling mistakes, different ways of expressing negatives or speculations, use of acronyms, subjectivity, anaphora, subordinates ... to determine which univocal concepts correspond to each clinical annotation.
Savana is not a data company. The hospital always owns the data. Savana provides its technology for hospitals to reuse their valuable free text information available in EHRs.
Savana has developed EHRead, a powerful technology that applies NLP and deep learning techniques to analyse the unstructured free text information written in EHRs. EHRead automatically processes anonymized, de-identified, unlinked patient text documents from EHRs and returns highly valuable medical aggregated information to the data source institution through a user-friendly SAAS application called «Savana Manager».
Standards established by the European General Data Protection Regulation (GDPR)
For the responsible use and disclosure of health data without the need for patient consent, Savana follows the standards established by the European General Data Protection Regulation (GDPR) (EU 2016/679). However, SAVANA’s technology solution only receives and processes anonymized, de-identified, unlinked patient data, and these data does not fall under GDPR.
First study applying Big Data, Machine Learning and Natural Language Processing aimed to predict hospitalizations in COPD patients
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