An AI Approach to Transform the E-Patient Records into Clinical Cases for Medical Diagnostic Systems

A major problem for researchers in medical informatics is the presence of ‘useful’ Electronic Patient Record (EPR) and its use in providing important information to and about the patient’s diseases and treatment. This paper discusses the importance of medical informatics and its role in health procurement branch. It also proposes an AI approach to transform the E-Patient records into clinical cases for medical diagnostic systems. This approach is based on using medical documents to generate E-Patient records, then mapping these records to build medical cases to form a cases repository that would be utilized in a CBR-medical diagnostic system

Improving patient record search: A meta-data based approach

The International Classification of Diseases (ICD) is a type of meta-data found in many Electronic Patient Records. Research to explore the utility of these codes in medical Information Retrieval (IR) applications is new, and many areas of investigation remain, including the question of how reliable the assignment of the codes has been. This paper proposes two uses of the ICD codes in two different contexts of search: Pseudo-Relevance Judgments (PRJ) and Pseudo-Relevance Feedback (PRF). We find that our approach to evaluate the TREC challenge runs using simulated relevance judgments has a positive correlation with the TREC official results, and our proposed technique for performing PRF based on the ICD codes significantly outperforms a traditional PRF approach. The results are found to be consistent over the two years of queries from the TREC medical test collection

Integrating Medical Ontology and Pseudo Relevance Feedback For Medical Document Retrieval

The purpose of this thesis is to undertake and improve the accuracy of locating the relevant documents from a large amount of Electronic Medical Data (EMD). The unique goal of this research is to propose a new idea for using medical ontology to find an easy and more reliable approach for patients to have a better understanding of their diseases and also help doctors to find and further improve the possible methods of diagnosis and treatments. The empirical studies were based on the dataset provided by CLEF focused on health care data. In this research, I have used Information Retrieval to find and obtain relevant information within the large amount of data sets provided by CLEF. I then used ranking functionality on the Terrier platform to calculate and evaluate the matching documents in the collection of data sets. BM25 was used as the base normalization method to retrieve the results and Pseudo Relevance Feedback weighting model to retrieve the information regarding patients health history and medical records in order to find more accurate results. I then used Unified Medical Language System to develop indexing of the queries while searching on the Internet and looking for health related documents. UMLS software was actually used to link the computer system with the health and biomedical terms and vocabularies into classify tools; it works as a dictionary for the patients by translating the medical terms. Later I would like to work on using medical ontology to create a relationship between the documents regarding the medical data and my retrieved results

Methods to Facilitate the Capture, Use, and Reuse of Structured and Unstructured Clinical Data.

Electronic health records (EHRs) have great potential to improve quality of care and to support clinical and translational research. While EHRs are being increasingly implemented in U.S. hospitals and clinics, their anticipated benefits have been largely unachieved or underachieved. Among many factors, tedious documentation requirements and the lack of effective information retrieval tools to access and reuse data are two key reasons accounting for this deficiency. In this dissertation, I describe my research on developing novel methods to facilitate the capture, use, and reuse of both structured and unstructured clinical data. Specifically, I develop a framework to investigate potential issues in this research topic, with a focus on three significant challenges. The first challenge is structured data entry (SDE), which can be facilitated by four effective strategies based on my systematic review. I further propose a multi-strategy model to guide the development of future SDE applications. In the follow-up study, I focus on workflow integration and evaluate the feasibility of using EHR audit trail logs for clinical workflow analysis. The second challenge is the use of clinical narratives, which can be supported by my innovative information retrieval (IR) technique called “semantically-based query recommendation (SBQR)”. My user experiment shows that SBQR can help improve the perceived performance of a medical IR system, and may work better on search tasks with average difficulty. The third challenge involves reusing EHR data as a reference standard to benchmark the quality of other health-related information. My study assesses the readability of trial descriptions on ClinicalTrials.gov and found that trial descriptions are very hard to read, even harder than clinical notes. My dissertation has several contributions. First, it conducts pioneer studies with innovative methods to improve the capture, use, and reuse of clinical data. Second, my dissertation provides successful examples for investigators who would like to conduct interdisciplinary research in the field of health informatics. Third, the framework of my research can be a great tool to generate future research agenda in clinical documentation and EHRs. I will continue exploring innovative and effective methods to maximize the value of EHRs.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135845/1/tzuyu_1.pd

Three Essays on Enhancing Clinical Trial Subject Recruitment Using Natural Language Processing and Text Mining

Patient recruitment and enrollment are critical factors for a successful clinical trial; however, recruitment tends to be the most common problem in most clinical trials. The success of a clinical trial depends on efficiently recruiting suitable patients to conduct the trial. Every clinical trial research has a protocol, which describes what will be done in the study and how it will be conducted. Also, the protocol ensures the safety of the trial subjects and the integrity of the data collected. The eligibility criteria section of clinical trial protocols is important because it specifies the necessary conditions that participants have to satisfy. Since clinical trial eligibility criteria are usually written in free text form, they are not computer interpretable. To automate the analysis of the eligibility criteria, it is therefore necessary to transform those criteria into a computer-interpretable format. Unstructured format of eligibility criteria additionally create search efficiency issues. Thus, searching and selecting appropriate clinical trials for a patient from relatively large number of available trials is a complex task. A few attempts have been made to automate the matching process between patients and clinical trials. However, those attempts have not fully integrated the entire matching process and have not exploited the state-of-the-art Natural Language Processing (NLP) techniques that may improve the matching performance. Given the importance of patient recruitment in clinical trial research, the objective of this research is to automate the matching process using NLP and text mining techniques and, thereby, improve the efficiency and effectiveness of the recruitment process. This dissertation research, which comprises three essays, investigates the issues of clinical trial subject recruitment using state-of-the-art NLP and text mining techniques. Essay 1: Building a Domain-Specific Lexicon for Clinical Trial Subject Eligibility Analysis Essay 2: Clustering Clinical Trials Using Semantic-Based Feature Expansion Essay 3: An Automatic Matching Process of Clinical Trial Subject Recruitment In essay1, I develop a domain-specific lexicon for n-gram Named Entity Recognition (NER) in the breast cancer domain. The domain-specific dictionary is used for selection and reduction of n-gram features in clustering in eassy2. The domain-specific dictionary was evaluated by comparing it with Systematized Nomenclature of Medicine--Clinical Terms (SNOMED CT). The results showed that it add significant number of new terms which is very useful in effective natural language processing In essay 2, I explore the clustering of similar clinical trials using the domain-specific lexicon and term expansion using synonym from the Unified Medical Language System (UMLS). I generate word n-gram features and modify the features with the domain-specific dictionary matching process. In order to resolve semantic ambiguity, a semantic-based feature expansion technique using UMLS is applied. A hierarchical agglomerative clustering algorithm is used to generate clinical trial clusters. The focus is on summarization of clinical trial information in order to enhance trial search efficiency. Finally, in essay 3, I investigate an automatic matching process of clinical trial clusters and patient medical records. The patient records collected from a prior study were used to test our approach. The patient records were pre-processed by tokenization and lemmatization. The pre-processed patient information were then further enhanced by matching with breast cancer custom dictionary described in essay 1 and semantic feature expansion using UMLS Metathesaurus. Finally, I matched the patient record with clinical trial clusters to select the best matched cluster(s) and then with trials within the clusters. The matching results were evaluated by internal expert as well as external medical expert

Interactive Malayalam Question Answering System: A Neural Word Embedding And Similarity Measure Based Approach.

This innovative system operates as an automated, domain-specific knowledge repository designed specifically to furnish reliable Malayalam responses to inquiries pertaining to COVID-19. Leveraging advanced Natural Language Processing (NLP) algorithms, both Malayalam documents and questions undergo meticulous processing. The semantic modelling and document conversion stages employ the Word Embedding approach, specifically Continuous Bag of Words (CBOW), to enhance the system's understanding of the language nuances. Subsequently, the retrieved results for a given query are meticulously ranked using the cosine similarity measure, ensuring that the most relevant and accurate information is presented to the user. Integral to the system's efficacy is our proprietary Malayalam question-answering dataset. This dataset has been meticulously curated, drawing from reliable and publicly accessible sources related to COVID-19. It serves as the foundation for experimentation, reflecting the system's ability to provide accurate responses. The system's performance is quantified using the F1 score, a metric that combines precision and recall, yielding a comprehensive evaluation. In our experimentation, the F1 score of the Semantic Malayalam Question-Answering System is found to be 76%, attesting to its robustness and effectiveness in delivering trustworthy information in the Malayalam language within the context of COVID-19

Feedback-Driven Radiology Exam Report Retrieval with Semantics

Clinical documents are vital resources for radiologists to have a better understanding of patient history. The use of clinical documents can complement the often brief reasons for exams that are provided by physicians in order to perform more informed diagnoses. With the large number of study exams that radiologists have to perform on a daily basis, it becomes too time-consuming for radiologists to sift through each patient\u27s clinical documents. It is therefore important to provide a capability that can present contextually relevant clinical documents, and at the same time satisfy the diverse information needs among radiologists from different specialties. In this work, we propose a knowledge-based semantic similarity approach that uses domain-specific relationships such as part-of along with taxonomic relationships such as is-a to identify relevant radiology exam records. Our approach also incorporates explicit relevance feedback to personalize radiologists information needs. We evaluated our approach on a corpus of 6,265 radiology exam reports through study sessions with radiologists and demonstrated that the retrieval performance of our approach yields an improvement of 5% over the baseline. We further performed intra-class and inter-class similarities using a subset of 2,384 reports spanning across 10 exam codes. Our result shows that intra-class similarities are always higher than the inter-class similarities and our approach was able to obtain 6% percent improvement in intra-class similarities against the baseline. Our results suggest that the use of domain-specific relationships together with relevance feedback provides a significant value to improve the accuracy of the retrieval of radiology exam reports

Still a Lot to Lose: The Role of Controlled Vocabulary in Keyword Searching

In their 2005 study, Gross and Taylor found that more than a third of records retrieved by keyword searches would be lost without subject headings. A review of the literature since then shows that numerous studies, in various disciplines, have found that a quarter to a third of records returned in a keyword search would be lost without controlled vocabulary. Other writers, though, have continued to suggest that controlled vocabulary be discontinued. Addressing criticisms of the Gross/Taylor study, this study replicates the search process in the same online catalog, but after the addition of automated enriched metadata such as tables of contents and summaries. The proportion of results that would be lost remains high

Utilizing ChatGPT to Enhance Clinical Trial Enrollment

Clinical trials are a critical component of evaluating the effectiveness of new medical interventions and driving advancements in medical research. Therefore, timely enrollment of patients is crucial to prevent delays or premature termination of trials. In this context, Electronic Health Records (EHRs) have emerged as a valuable tool for identifying and enrolling eligible participants. In this study, we propose an automated approach that leverages ChatGPT, a large language model, to extract patient-related information from unstructured clinical notes and generate search queries for retrieving potentially eligible clinical trials. Our empirical evaluation, conducted on two benchmark retrieval collections, shows improved retrieval performance compared to existing approaches when several general-purposed and task-specific prompts are used. Notably, ChatGPT-generated queries also outperform human-generated queries in terms of retrieval performance. These findings highlight the potential use of ChatGPT to enhance clinical trial enrollment while ensuring the quality of medical service and minimizing direct risks to patients.Comment: Under Revie