Natural Language Processing for Improving the Sensitivity and Specificity of Clinical Decision Support

Natural language processing for improving the sensitivity and specificity of clinical decision support SYSTEM AND reduce alert fatigue

TABLE OF CONTENT

What Citation Styles Can You Use for My Essay?

Students often ask, “Can you write my essay in APA or MLA?”—and the answer’s a big yes! Our writers are experts in every style imaginable: APA, MLA, Chicago, Harvard, you name it. Just tell us what you need, and we’ll deliver a perfectly formatted paper that matches your requirements, hassle-free.

ABSTRACT:

Over the past ten years, the healthcare system in the United States has drastically evolved from a paper-based to an electronic based system. The electronic system stores enormous amount of data, formally called as Patient records, which are then processed and used by caregivers to make clinically informed decisions. The process of making the clinically informed decision is delivered by clinical decision support system, popularly known as CDS. The format and the capacity of data processed at a time is huge. Over the years, with the introduction of medical format standards such as FHIR and HL7, CDS has gotten around the practice of processing standardized data but also has developed some daunting challenges such as alert fatigue. The most neglected part of the patient records is the non-standardized data, stored as physician and registered nurses (RN) notes. Natural language processing (NLP) promises to break grounds in this aspect. The ability of the NLP concepts to process the non-structured notes is proving to make the CDS more sensitive and specific to the data. This paper is written in collaboration with Brigham and Women’s hospital as a part of the project CONCERN (Collins, 2018). In this paper, using literature review, we are going to identify NLP concepts that would process unstructured caregivers notes and do an exploratory analysis that will help to understand how it improves the sensitivity and specificity of CDS. The caregiver’s notes will be processed to identify onset of sepsis in patients. The results of the coding process will be used to demonstrate the NLP concept, suggest the future steps to integrate into the CDS, and hence possibly reduce CDS challenges such as alert fatigue.

BACKGROUND AND DESCRIPTION:

Clinical decision support system (CDS) is a tool used by care deliverers to effectively cater the needs of the patients by leveraging their health information (Bates et al., 2003). The introduction of clinical decision support system happened in 1959 as a stand-alone system, and evolved as service based system by 2005 (Wright & Sittig, 2008). The invasion of informatics in medicine began with Ledley and Lusted, as Ledley invented CT scanner and Lusted pioneered in Medical decision making (Ledley & Lusted, 1959)(Sittig, Ash, & Ledley, 2006). Fast forward to 2007,CDS had already evolved from a Phase 1:Stand-alone systems to Phase 4: Service models due to countless number of research and integration of machine learning and Artificial intelligence techniques  (Wright & Sittig, 2008).  As CDS evolved in past few years, challenges hindering the delivery of care has also increased. Some of these challenges are  improving human-computer interface, publicize best CDS practices, use of free text in CDS (Sittig et al., 2008). One of the great challenges in CDS today is the occurrence of alert fatigue. As the alerts are more than frequently deployed to the caregivers, they tend to be ignored. Historically, it has been observed that the alerts were the cause of potential drug interactions (Kesselheim, Cresswell, Phansalkar, Bates, & Sheikh, 2011).In most of the cases, these alerts are ignored because they are “clinically irrelevant, goes against patient tolerance or contradicts the providers intention to monitor the patient ”(McCoy, Thomas, Krousel-Wood, & Sittig, 2014). A systematic literature review done on the quality of decision support in computerized provider order entry strongly suggests that the CDS systems must adapt to optimizing the systems with knowledge base to possibly reduce the irrelevant alerts (Carli, Fahrni, Bonnabry, & Lovis, 2018).

Is It Legal to Use Your Writing Service?

Absolutely, it’s 100% legal! Our service provides sample essays and papers to guide your own work—think of it as a study tool. Used responsibly, it’s a legit way to improve your skills, understand tough topics, and boost your grades, all while staying within academic rules.

Research study says that the physician and RN notes contain vital information about onset of various diseases such as cardiac arrest and sepsis (Ohno-Machado, 2011). In an attempt to discover peripheral arterial disease cases in Mayo Clinic, researchers found that when analyzing radiology notes using predictive algorithms, the system became more specific and sensitive compared to just using structured data (Savova, Fan, et al., 2010). Not only this, as the CDS system become more specific and sensitive to the  incoming data, it proves to reduce the number of interruptive alerts (Coleman et al., 2013).Studies suggest that as we integrate more specific algorithms with our CDS, it can drastically improve the delivery of  patient risk specifying alerts and hence enhance the quality of care  (Coleman et al., 2013).

Sepsis and Clinical decision support system:

Sepsis is one of the most common and fatal medical conditions associated with many patient deaths in the United States of America (Angus et al., 2001). Every year 50% of the patient death is associated to Sepsis(Apostolova & Velez, 2018). Most of these deaths are speculated to have occurred because the CDS system fails to identify the onset of the infection in a patient (Apostolova & Velez, 2018). Hence, an intervention to curb the infection by early detection is needed. In a study done at Rush University Medical Center, Chicago, IL, an attempt to standardize the early recognition and treatment workflow to reduce both mortality and cost of healthcare showed that the evidence based care facilitated improvement in the early recognition of sepsis (Dale, Barbee James, Casey, & Stein, 2016). Lab data and Vital data have been used to detect sepsis using SIRS and SOFA scores (Marik & Taeb, 2017). One of the most recent prediction model that has said to outrun both SIRS and SOFA scores is Insight, which is machine learning based algorithm (Mao et al., 2018).

How Much Does It Cost to Get a Paper Written?

Our pricing starts at $10 per page for undergrad work, $16 for bachelor-level, and $21 for advanced stuff. Urgency and extras like top writers or plagiarism reports tweak the cost—deadlines range from 14 days to 3 hours. Order early for the best rates, and enjoy discounts on big orders: 5% off over $500, 10% over $1,000!

A number of algorithms have been created to identify Sepsis using combination unstructured notes and structured data. Utilizing nursing notes, in combination structured health data, to create an automated alert system achieved an high F1-score and showed promising results by detecting patients at risk of sepsis (Apostolova & Velez, 2018). Another similar study done to create an automated trigger for Sepsis in emergency showed massive success when the unstructured data was used in combination with structured data. The data was fed into a Natural language processing (NLP) algorithm and then a predictive model was built to accurately predict the cases of Sepsis from the data (Horng et al., 2017). The results of this research showed that when the NLP algorithms were introduced into the predictive model, the system became more sensitive and specific to detecting positive cases of sepsis. In the following research we will use a different set of health data together with notes data to explore the possibilities with NLP and try to identify cases of sepsis.

Goal of this paper:

With the help of Natural language Processing (NLP) we will explore how we can utilize the unstructured care givers notes to improve the sensitivity and specificity of the CDS towards detecting cases of patients at risk to sepsis. NLP concepts will be identified using literature review and an exploratory analysis will be done to understand how these concepts process the unstructured notes. The results obtained will be used to suggest further steps for building a more specific predictive model and reduce alert fatigue.

 Literature review:

Will My Use of This Service Stay Private?

Yes, totally! We lock down your info with top-notch encryption—your school, friends, no one will know. Every paper’s custom-made to blend with your style, and we check it for originality, so it’s all yours, all discreet.

Natural Language processing and clinical decision support system:

A service based CDS provide efficient support due to the involvement of machine-learning and data mining techniques (Wright & Sittig, 2008). In the recent years, CDS based on Natural language processing (NLP) has shown major breakthrough in care delivery. A number of pre-existing NLP based system such as cTakes are said to show massive success in understanding clinical notes and eventually delivering timely care (Savova, Masanz, et al., 2010). NLP is said to have the potential to drastically improve the three major components of CDS – clinical knowledge, interventions , and decision rules (Demner-Fushman, Chapman, & McDonald, 2009).

NLP is a branch of computational science that processes free text to build different types of systems (Joshi, 1991). There are two types of CDS integrated with NLP – Active and Passive, facilitating functions such as sending out alerts, reminders, coding, and monitoring (Demner-Fushman et al., 2009). NLP has also shown a massive success in creation of task specific CDS system. In one such attempt, radiology notes were used to process the NLP concept based CDS, to detect peripheral artery diseases. The NLP based system, when compared to a baseline system, proved to have accuracy agreement and gold standard of 0.930 (Savova, Fan, et al., 2010). Not only does NLP increases the efficiency of CDS, it reduces the vigorous task of manual labor like data abstraction by 90% (Carrell et al., 2014).

The NLP system or module can be integrated with CDS in various ways. The aim of a NLP or multiple NLP modules involvement in a CDS system is to satisfy either of the three relationships; integration with CDS to achieve a general cohesiveness in terms of data, govern CDS to provide knowledge driven support, and/or specialization of the CDS for a specific task (Demner-Fushman et al., 2009). Keeping this relationship in mind there are two types of NLP systems- Coupled NLP system and Integrated NLP system. Coupled NLP system (Figure 1) is the type which is task specific and works only when evoked by the task centered data. Integrated NLP system (Figure 2) is the type which in general processes data irrespective of a specific task assigned (Demner-Fushman et al., 2009).  The systems are made up on the building blocks of NLP, namely, Text preprocessing and Entity recognition (Demner-Fushman et al., 2009).

Do You Use AI to Write Papers?

No way—our papers are 100% human-crafted. Our writers are real pros with degrees, bringing creativity and expertise AI can’t match. Every piece is original, checked for plagiarism, and tailored to your needs by a skilled human, not a machine.

Figure 1: A coupled NLP system (Demner-Fushman et al., 2009)

Figure 2 : An integrated NLP-CDS system (Demner-Fushman et al., 2009)

Why Choose You for Research Papers?

We’re the best because our writers are degree-holding experts—Bachelor’s to Ph.D.—who nail any topic. We obsess over quality, using tools to ensure perfection, and offer free revisions to guarantee you’re thrilled with the result, even on tight deadlines.

Generally, the basic approach of building an NLP based system is driven by understanding various theories, constructing knowledge bases and application to a reference standard training corpus (Carrell et al., 2014). As the name suggests, NLP takes the data through a number of processing steps before it is finally ready to be used by any system. The unstructured data goes through a number of processes which includes – text parsing, text representation, Tagging, and Modelling or Pattern Mining (Sarkar, 2018). In a more simpler term, NLP would interpret the possible meaning of the sentence and identify clinical concepts in the unstructured dataset using the rules of linguistics (Liao et al., 2015). As the process considers the identified concepts and tags each word in the text to the concepts, the data extracted will be the result of a more sensitive search. The CDS system becomes more specific as other data processing techniques, such as Machine learning algorithms and statistical analysis, are incorporated with NLP’s data extraction (Liao et al., 2015) .

Figure 3: An overview of data processing . (Liao et al., 2015).

In the past few years, a number of studies for understanding the trend of specific disease in a given population, have leveraged the processing power of various NLP concepts (Imler, Morea, & Imperiale, 2014). Like any other concept, NLP has various concepts that can be used strategically for a specific task. In order to process the free text available in medical record, one of the most used NLP concepts is Bag of words (bow) model. In order to predict the cases of sepsis in an emergency department, bow model was used to tokenize and transform the data. The data was then processed using a predictive model and the cases of sepsis were accurately recognized (Horng et al., 2017). An automated trigger was then designed for the emergency department using the same NLP and machine learning concepts. This study demonstrated the use of bow model in a predictive algorithm with clinical notes and showed promising result (Long, 2018). In order to determine the predictive value of the clinical notes, the bag of word model was used to extract information and successfully understand the representation of words in a clinical context (Boag, n.d.).

Who’s Writing My College Essays?

Our writers are top-tier—university grads, many with Master’s degrees, who’ve passed tough tests to join us. They’re ready for any essay, working with you to hit your deadlines and grading standards with ease and professionalism.

Another NLP model used to process clinical notes is topic modeling. Topic modeling was used to train clinical documents as a query expansion method and showed best performance for retrieving clinically relevant documents (Zeng, Redd, Rindflesch, & Nebeker, 2012). The F1- scores of topic model was better than the other two models used for term expansion. Topic modeling also showed promising results on an investigative analysis done using narrative notes. Topic modeling used with a Bayesian predictive model accurately predicted cases of  high risk patients in an intensive care unit (Luo & Rumshisky, 2017). Latent Dirichlet allocation (LDA), a type of topic modeling approach was used in predicting medication and diagnosis in the National Taiwan University Hospital, Taiwan (Lu, Wei, & Hsiao, 2016) . The model outperformed logistic regression and k-nearest-neighbor prediction model while processing the healthcare data.

Word embeddings is another NLP model that is used in clinical natural language processing. A qualitative and quantitative analysis done on clinical notes demonstrated the ability of word embeddings in vector representation and capturing linguistic relationships between words (Wang et al., 2018). The dataset trained by word embeddings showed more accurate clinical results than the ones from Google news and GloVe (Wang et al., 2018). Another research study to understand the mortality rate in an Intensive Care unit used word2vec, a word embeddings model, to be incorporated in the predictive neural network method (Grnarova, Schmidt, Hyland, & Eickhoff, 2016). The research concluded that understanding linguistic relations of the words in the caregivers notes is a vital part of predictive models and needs to be accounted to reduce patient mortality (Grnarova et al., 2016) .

METHOD

For the following project, natural language processing concepts discovered by the help of literature review will be used to run some preliminary analysis on healthcare data and propose what can be the future steps to improve the specificity and sensitivity of CDS towards sepsis and reduce alerts fatigue.  We will load the data in Jupyter, which is an online, open source software that allows the use of different coding languages and we will use Python to process NLP on the data (“Project Jupyter,” n.d.). Our focus would be to explore the NLP concept using caregivers notes and then suggest future supervised or unsupervised predictive model that would improve the CDS.

Are Your Papers Original?

Always! We start from scratch—no copying, no AI—just pure, human-written work with solid research and citations. You can even get a plagiarism report to confirm it’s 95%+ unique, ready for worry-free submission.