A Technique to Exploit Free-Form Notes to Predict Customer Churn

2014 ◽  
Vol 4 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Gregory W. Ramsey ◽  
Sanjay Bapna
Keyword(s):  
Language Processing ◽  
Resource Planning ◽  
Classification Problem ◽  
Structured Data ◽  
Free Form ◽  
Care Providers ◽  
Health Records ◽  
Hospital Administrators ◽  
Customer Churn ◽  
Processing Techniques

As healthcare costs rise, hospitals are seeking ways to improve operations. This paper examines the usefulness of free-form notes to solve a classification problem commonly associated with customer churn. The authors show that classifiers which incorporate free-form notes, using natural language processing techniques, are up to 9% more accurate than classifiers that are solely developed using structured data. The authors suggest that hospitals and chronic disease management clinics can use structured data and free-form notes from electronic health records to predict which patients are likely to cease receiving care from their facilities. Classification tools for predicting patient churn are of interest to hospital administrators; such information can aid in resource planning and facilitate smoother handoffs between care providers.

Predicting Patient Turnover

2019 ◽  
pp. 108-132
Author(s):  
Gregory W. Ramsey ◽  
Sanjay Bapna
Keyword(s):  
Language Processing ◽  
Resource Planning ◽  
Classification Problem ◽  
Structured Data ◽  
Free Form ◽  
Care Providers ◽  
Hospital Administrators ◽  
Customer Churn ◽  
Patient Turnover ◽  
Processing Techniques

Predicting patient turnover within health services is beneficial for resource planning. In this chapter, patient turnover is viewed as a form of customer churn. As such, the authors examine whether free-form notes are useful for solving the classification problem typically associated with customer churn. The authors show that classifiers which incorporate free-form notes, using natural language processing techniques, are up to 11% more accurate than classifiers that are solely developed using structured data. In addition, the authors show that free-form notes aggregated for each account perform better than treating each note separately. It is suggested that hospitals and chronic disease management clinics can use structured data and free-form notes from electronic health records to predict which patients are likely to cease receiving care from their facilities. Classification tools for predicting patient churn are of interest to hospital administrators; such information can aid in resource planning and facilitates smoother handoffs between care providers.

A Humanist’s Legacy in Medical Informatics: Visions and Accomplishments of Professor Jean-Raoul Scherrer

2002 ◽  
Vol 41 (03) ◽  
pp. 237-242 ◽  
Author(s):  
C. Lovis ◽  
S. Spahni ◽  
R. D. Appel ◽  
O. Ratib ◽  
C. Boyer ◽  
...  
Keyword(s):  
Medical Informatics ◽  
Language Processing ◽  
Communication Systems ◽  
Medical Information ◽  
Image Management ◽  
Care Providers ◽  
Patient Centered ◽  
Picture Archiving And Communication ◽  
Processing Techniques ◽  
Clinical Research Data

Summary Objective: To report about the work of Prof. Jean-Raoul Scherrer, and show how his humanist vision, his medical skills and his scientific background have enabled and shaped the development of medical informatics over the last 30 years. Results: Starting with the mainframe-based patient-centered hospital information system DIOGENE in the 70s, Prof. Scherrer developed, implemented and evolved innovative concepts of man-machine interfaces, distributed and federated environments, leading the way with information systems that obstinately focused on the support of care providers and patients. Through a rigorous design of terminologies and ontologies, the DIOGENE data would then serve as a basis for the development of clinical research, data mining, and lead to innovative natural language processing techniques. In parallel, Prof. Scherrer supported the development of medical image management, ranging from a distributed picture archiving and communication systems (PACS) to molecular imaging of protein electrophoreses. Recognizing the need for improving the quality and trustworthiness of medical information on the Web, Prof. Scherrer created the Health-On-the-Net (HON) foundation. Conclusions: These achievements, made possible thanks to his visionary mind, deep humanism, creativity, generosity and determination, have made of Prof. Scherrer a true pioneer and leader of the human-centered, patient-oriented application of information technology for improving healthcare.

scholarly journals Natural language processing tool for automatic diseases and drugs recognition from electronic health records in polish- pilot study

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
C M Maciejewski ◽  
M K Krajsman ◽  
K O Ozieranski ◽  
M B Basza ◽  
M G Gawalko ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Electronic Health Records ◽  
Natural Language ◽  
Language Processing ◽  
Structured Data ◽  
Health Records ◽  
Funding Sources ◽  
Natural Language Processing Tool ◽  
Electronic Health ◽  
Automatic Tool

Abstract Background An estimate of 80% of data gathered in electronic health records is unstructured, textual information that cannot be utilized for research purposes until it is manually coded into a database. Manual coding is a both cost and time- consuming process. Natural language processing (NLP) techniques may be utilized for extraction of structured data from text. However, little is known about the accuracy of data obtained through these methods. Purpose To evaluate the possibility of employing NLP techniques in order to obtain data regarding risk factors needed for CHA2DS2VASc scale calculation and detection of antithrombotic medication prescribed in the population of atrial fibrillation (AF) patients of a cardiology ward. Methods An automatic tool for diseases and drugs recognition based on regular expressions rules was designed through cooperation of physicians and IT specialists. Records of 194 AF patients discharged from a cardiology ward were manually reviewed by a physician- annotator as a comparator for the automatic approach. Results Median CHA2DS2VASc score calculated by the automatic was 3 (IQR 2–4) versus 3 points (IQR 2–4) for the manual method (p=0.66). High agreement between CHA2DS2VASc scores calculated by both methods was present (Kendall's W=0.979; p<0.001). In terms of anticoagulant recognition, the automatic tool misqualified the drug prescribed in 4 cases. Conclusion NLP-based techniques are a promising tools for obtaining structured data for research purposes from electronic health records in polish. Tight cooperation of physicians and IT specialists is crucial for establishing accurate recognition patterns. Funding Acknowledgement Type of funding sources: None.

scholarly journals Topic Modeling of Committee Discussions in the Brazilian Chamber of Deputies

2021 ◽  
Author(s):  
M. A. dos Santos ◽  
N. Andrade ◽  
F. Morais
Keyword(s):  
Language Processing ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Structured Data ◽  
National Congress ◽  
Similarities And Differences ◽  
External Events ◽  
Latent Topics ◽  
Processing Techniques ◽  
Chamber Of Deputies

Ensuring that civil society can monitor and supervise the actions of its representatives is essential to build strong democracies. Despite significant advances in transparency, Brazilian National Congress committees are presently complex to follow and monitor due to the lack of open structured data about their discussions and the sheer volume of activity in these committees. This work presents two contributions to this context. First, we create and present an open dataset including structured speeches of the 25 Chamber of Deputies' standing committees over the last two decades. Second, we use Natural Language Processing techniques - especially Latent Dirichlet Allocation (LDA) - to identify themes addressed on these committees. Based on these latent topics, we explore similarities and differences between the standing committees, their relationships, and how their debates change over time. Our results show that committees accommodate conversations - including their main topic and opposing agendas - and describe how the topics discussed in the committees reverberate external events.

Prognostication Stereotype of Patients Morbidity and Mortality by Extraction of E-Health Records

2018 ◽  
Vol 6 (6) ◽  
pp. 215
Author(s):  
Sunitha .T ◽  
Shyamala .J ◽  
Annie Jesus Suganthi Rani.A
Keyword(s):  
At Risk ◽  
Risk Prediction ◽  
Health Risks ◽  
Preventive Intervention ◽  
Classification Problem ◽  
Health Examination ◽  
Disease Area ◽  
Health Records ◽  
Mortality And Morbidity ◽  
Main Motive

Data mining suggest an innovative way of prognostication stereotype of Patients health risks. Large amount of Electronic Health Records (EHRs) collected over the years have provided a rich base for risk analysis and prediction. An EHR contains digitally stored healthcare information about an individual, such as observations, laboratory tests, diagnostic reports, medications, procedures, patient identifying information and allergies. A special type of EHR is the Health Examination Records (HER) from annual general health check-ups. Identifying participants at risk based on their current and past HERs is important for early warning and preventive intervention. By “risk”, we mean unwanted outcomes such as mortality and morbidity. This approach is limited due to the classification problem and consequently it is not informative about the specific disease area in which a personal is at risk. Limited amount of data extracted from the health record is not feasible for providing the accurate risk prediction. The main motive of this project is for risk prediction to classify progressively developing situation with the majority of the data unlabeled.

Survey of Electronic Health Record (EHR) Systems in Kenyan Public Hospitals: A mixed-methods survey (Preprint)

2018 ◽  
Author(s):  
Naomi Muinga ◽  
Steve Magare ◽  
Jonathan Monda ◽  
Mike English ◽  
Hamish Fraser ◽  
...  
Keyword(s):  
Management Information System ◽  
Health Management ◽  
Public Hospitals ◽  
Health Management Information System ◽  
Adoption Process ◽  
Management Information ◽  
Health Records ◽  
Hospital Administrators ◽  
Ehr Adoption ◽  
Electronic Health

BACKGROUND As healthcare facilities in Low- and Middle-Income Countries (LMICs) such as Kenya adopt Electronic Health Record (EHR) systems to improve hospital administration and patient care, it is important to understand the adoption process, identify the key stakeholders, and assess the capabilities of the systems in use. OBJECTIVE To describe the level of adoption of Electronic Health Records systems in public hospitals and understand the process of adoption from Health Management Information System (HMIS) system vendors and system users. METHODS We conducted a survey of County Health Records Information Officers (CHRIOs) in Kenya to determine the level of adoption of Electronic Health Records systems in public hospitals. We conducted site visits to hospitals to view systems in use and to interview hospital administrators and end users. We also interviewed Health Management Information System (HMIS) system vendors to understand the adoption process from their perspective. RESULTS From the survey of CHRIOs, all facilities mentioned had adopted some form of EHR. Hospitals commonly purchased systems for patient administration and hospital billing functions. Radiology and laboratory management systems were commonly standalone systems. There were varying levels of interoperability within facilities that had more than one system in operation. We only saw one in-patient EHR system in use although many vendors and hospital administrators we interviewed were planning to adopt or support such systems. From the user perspective, issues such as system usability, adequate training, availability of adequate infrastructure and system support emerged. From the vendor perspective, a wide range of services was available to the hospital though constrained by funding and the need to computerise service areas that were deemed as priority. Additionally, vendors were unable to implement some data sharing modules linking to national HMIS due to lack of appropriate policies to facilitate this and users’ lack of confidence in new technologies such as cloud services. CONCLUSIONS EHR adoption in Kenya has been underway for some years, particularly in comprehensive care clinics, and hospitals are increasing purchasing systems to support administrative functions. Considerable support from government, donors and regional health informatics organisations will be required to enable hospitals to move to full EHR adoption for in-patient care.

scholarly journals Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204
Author(s):  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
X. Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Data Science ◽  
Naive Bayes ◽  
The Philippines ◽  
Naïve Bayes ◽  
Social Networking Site ◽  
Bayes Model ◽  
The Government ◽  
Processing Techniques

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

scholarly journals Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 664
Author(s):  
Nikos Kanakaris ◽  
Nikolaos Giarelis ◽  
Ilias Siachos ◽  
Nikos Karacapilidis
Keyword(s):  
Language Processing ◽  
Scientific Knowledge ◽  
Link Prediction ◽  
Performance Metrics ◽  
Future Research ◽  
Knowledge Graph ◽  
Prediction Problem ◽  
Textual Information ◽  
Research Collaborations ◽  
Processing Techniques

We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.

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