128 research outputs found
Clinical Decision Support System for Unani Medicine Practitioners
Like other fields of Traditional Medicines, Unani Medicines have been found
as an effective medical practice for ages. It is still widely used in the
subcontinent, particularly in Pakistan and India. However, Unani Medicines
Practitioners are lacking modern IT applications in their everyday clinical
practices. An Online Clinical Decision Support System may address this
challenge to assist apprentice Unani Medicines practitioners in their
diagnostic processes. The proposed system provides a web-based interface to
enter the patient's symptoms, which are then automatically analyzed by our
system to generate a list of probable diseases. The system allows practitioners
to choose the most likely disease and inform patients about the associated
treatment options remotely. The system consists of three modules: an Online
Clinical Decision Support System, an Artificial Intelligence Inference Engine,
and a comprehensive Unani Medicines Database. The system employs advanced AI
techniques such as Decision Trees, Deep Learning, and Natural Language
Processing. For system development, the project team used a technology stack
that includes React, FastAPI, and MySQL. Data and functionality of the
application is exposed using APIs for integration and extension with similar
domain applications. The novelty of the project is that it addresses the
challenge of diagnosing diseases accurately and efficiently in the context of
Unani Medicines principles. By leveraging the power of technology, the proposed
Clinical Decision Support System has the potential to ease access to healthcare
services and information, reduce cost, boost practitioner and patient
satisfaction, improve speed and accuracy of the diagnostic process, and provide
effective treatments remotely. The application will be useful for Unani
Medicines Practitioners, Patients, Government Drug Regulators, Software
Developers, and Medical Researchers.Comment: 59 pages, 11 figures, Computer Science Bachelor's Thesis on use of
Artificial Intelligence in Clinical Decision Support System for Unani
Medicine
Exploration and adaptation of large language models for specialized domains
Large language models have transformed the field of natural language processing (NLP). Their improved performance on various NLP benchmarks makes them a promising tool—also for the application in specialized domains. Such domains are characterized by highly trained professionals with particular domain expertise. Since these experts are rare, improving the efficiency of their work with automated systems is especially desirable. However, domain-specific text resources hold various challenges for NLP systems. These challenges include distinct language, noisy and scarce data, and a high level of variation. Further, specialized domains present an increased need for transparent systems since they are often applied in high stakes settings. In this dissertation, we examine whether large language models (LLMs) can overcome some of these challenges and propose methods to effectively adapt them to domain-specific requirements.
We first investigate the inner workings and abilities of LLMs and show how they can fill the gaps that are present in previous NLP algorithms for specialized domains. To this end, we explore the sources of errors produced by earlier systems to identify which of them can be addressed by using LLMs. Following this, we take a closer look at how information is processed within Transformer-based LLMs to better understand their capabilities. We find that their layers encode different dimensions of the input text. Here, the contextual vector representation, and the general language knowledge learned during pre-training are especially beneficial for solving complex and multi-step tasks common in specialized domains.
Following this exploration, we propose solutions for further adapting LLMs to the requirements of domain-specific tasks. We focus on the clinical domain, which incorporates many typical challenges found in specialized domains. We show how to improve generalization by integrating different domain-specific resources into our models. We further analyze the behavior of the produced models and propose a behavioral testing framework that can serve as a tool for communication with domain experts. Finally, we present an approach for incorporating the benefits of LLMs while fulfilling requirements such as interpretability and modularity. The presented solutions show improvements in performance on benchmark datasets and in manually conducted analyses with medical professionals.
Our work provides both new insights into the inner workings of pre-trained language models as well as multiple adaptation methods showing that LLMs can be an effective tool for NLP in specialized domains
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Phenotyping with Partially Labeled, Partially Observed Data
Identifying a group of individuals that share a common set of characteristics is a conceptually simple task, which is often difficult in practice. Such phenotyping problems emerge in various settings, including the analysis of clinical data. In this setting, phenotyping is often stymied by persistent data quality issues. These include a lack of reliable labels to indicate the presence of absence of characteristics of interest, and significant missingness in observed variables.
This dissertation introduces methods for learning phenotypes when the data contain missing values (partially observed) and labels are scarce (partially labeled). Aim 1 utilizes an unsupervised probabilistic graphical model to learn phenotypes from partially observed data. Aim 2 introduces a related semi-supervised probabilistic graphical model for learning phenotypes from partially labeled clinical data. Finally, Aim 3 describes a method for training deep generative models when the training data contain missing values. The algorithm is then applied in a semi-supervised setting where it accounts for partially labeled data as well
Using machine learning for automated de-identification and clinical coding of free text data in electronic medical records
The widespread adoption of Electronic Medical Records (EMRs) in hospitals continues to increase the amount of patient data that are digitally stored. Although the primary use of the EMR is to support patient care by making all relevant information accessible, governments and health organisations are looking for ways to unleash the potential of these data for secondary purposes, including clinical research, disease surveillance and automation of healthcare processes and workflows.
EMRs include large quantities of free text documents that contain valuable information. The greatest challenges in using the free text data in EMRs include the removal of personally identifiable information and the extraction of relevant information for specific tasks such as clinical coding. Machine learning-based automated approaches can potentially address these challenges.
This thesis aims to explore and improve the performance of machine learning models for automated de-identification and clinical coding of free text data in EMRs, as captured in hospital discharge summaries, and facilitate the applications of these approaches in real-world use cases. It does so by 1) implementing an end-to-end de-identification framework using an ensemble of deep learning models; 2) developing a web-based system for de-identification of free text (DEFT) with an interactive learning loop; 3) proposing and implementing a hierarchical label-wise attention transformer model (HiLAT) for explainable International Classification of Diseases (ICD) coding; and 4) investigating the use of extreme multi-label long text transformer-based models for automated ICD coding.
The key findings include: 1) An end-to-end framework using an ensemble of deep learning base-models achieved excellent performance on the de-identification task. 2) A new web-based de-identification software system (DEFT) can be readily and easily adopted by data custodians and researchers to perform de-identification of free text in EMRs. 3) A novel domain-specific transformer-based model (HiLAT) achieved state-of-the-art (SOTA) results for predicting ICD codes on a Medical Information Mart for Intensive Care (MIMIC-III) dataset comprising the discharge summaries (n=12,808) that are coded with at least one of the most 50 frequent diagnosis and procedure codes. In addition, the label-wise attention scores for the tokens in the discharge summary presented a potential explainability tool for checking the face validity of ICD code predictions. 4) An optimised transformer-based model, PLM-ICD, achieved the latest SOTA results for ICD coding on all the discharge summaries of the MIMIC-III dataset (n=59,652). The segmentation method, which split the long text consecutively into multiple small chunks, addressed the problem of applying transformer-based models to long text datasets. However, using transformer-based models on extremely large label sets needs further research.
These findings demonstrate that the de-identification and clinical coding tasks can benefit from the application of machine learning approaches, present practical tools for implementing these approaches, and highlight priorities for further research
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Toward Annotation Efficiency in Biased Learning Settings for Natural Language Processing
The goal of this thesis is to improve the feasibility of building applied NLP systems for more diverse and niche real-world use-cases of extracting structured information from text. A core factor in determining this feasibility is the cost of manually annotating enough unbiased labeled data to achieve a desired level of system accuracy, and our goal is to reduce this cost. We focus on reducing this cost by making contributions in two directions: (1) easing the annotation burden by leveraging high-level expert knowledge in addition to labeled examples, thus making approaches more annotation-efficient; and (2) mitigating known biases in cheaper, imperfectly labeled real-world datasets so that we may use them to our advantage. A central theme of this thesis is that high-level expert knowledge about the data and task can allow for biased labeling processes that focus experts on only manually labeling aspects of the data that cannot be easily labeled through cheaper means. This combination allows for more accurate models with less human effort. We conduct our research on this general topic through three diverse problems with immediate applications to real-world settings.
First, we study an applied problem in biased text classification. We encounter a rare-event text classification system that has been deployed for several years. We are tasked with improving this system's performance using only the severely biased incidental feedback provided by the experts over years of system use. We develop a method that combines importance weighting and an unlabeled data imputation scheme that exploits the selection-bias of the feedback to train an unbiased classifier without requiring additional labeled data. We experimentally demonstrate that this method considerably improves the system performance.
Second, we tackle an applied problem in named entity recognition (NER) concerning learning tagging models from data that have very low recall for annotated entities. To solve this issue we propose a novel loss, the Expected Entity Ratio (EER), that uses an uncertain estimate of the proportion of entities in the data to counteract the false-negative bias in the data, encouraging the model to have the correct ratio of entities in expectation. We justify the principles of our approach by providing theory that shows it recovers the true tagging distribution under mild conditions. Additionally we provide extensive empirical results that show it to be practically useful. Empirically, we find that it meets or exceeds performance of state-of-the-art baselines across a variety of languages, annotation scenarios, and amounts of labeled data. We also show that, when combined with our approach, a novel sparse annotation scheme can outperform exhaustive annotation for modest annotation budgets.
Third, we study the challenging problem of syntactic parsing in low-resource languages. We approach the problem from a cross-lingual perspective, building on a state-of-the-art transfer-learning approach that underperforms on ``distant'' languages that have little to no representation in the training corpus. Motivated by the field of syntactic typology, we introduce a general method called Expected Statistic Regularization (ESR) to regularize the parser on distant languages according to their expected typological syntax statistics. We also contribute general approaches for estimating the loss supervision parameters from the task formalism or small amounts of labeled data. We present seven broad classes of descriptive statistic families and provide extensive experimental evidence showing that using these statistics for regularization is complementary to deep learning approaches in low-resource transfer settings.
In conclusion, this thesis contributes approaches for reducing the annotation cost of building applied NLP systems through the use of high-level expert knowledge to impart additional learning signal on models and cope with cheaper biased data. We publish implementations of our methods and results, so that they may facilitate future research and applications. It is our hope that the frameworks proposed in this thesis will help to democratize access to NLP for producing structured information from text in wider-reaching applications by making them faster and cheaper to build
Non-communicable Diseases, Big Data and Artificial Intelligence
This reprint includes 15 articles in the field of non-communicable Diseases, big data, and artificial intelligence, overviewing the most recent advances in the field of AI and their application potential in 3P medicine
HEALTH OUTCOME PATHWAY PREDICTION. A GRAPH-BASED FRAMEWORK
This dissertation is part of the project FrailCare.AI, which aims to detect frailty in the
elderly Portuguese population in order to optimize the SNS24 (telemonitoring) service,
with the goal of suggesting health pathways to reduce the patients frailty. Frailty can be
defined as the condition of being weak and delicate which normally increases with age
and is the consequence of several health and non-health related factors.
A patient health journey is recorded in Eletronic Health Record (EHR), which are rich
but sparse, noisy and multi-modal sources of truth. These can be used to train predictive
models to predict future health states, where frailty is just one of them. In this work, due
to lack of data access we pivoted our focus to phenotype prediction, that is, predicting
diagnosis. What is more, we tackle the problem of data-insufficiency and class imbalance
(e.g. rare diseases and other infrequent occurrences in the training data) by integrating
standardized healthcare ontologies within graph neural networks. We study the broad
task of phenotype prediction, multi-task scenarios and as well few-shot scenarios - which
is when a class rarely occurs in the training set. Furthermore, during the development
of this work we detect some reproducibility issues in related literature which we detail,
and also open-source all of our implementations introduding a framework to aid the
development of similar systems.A presente dissertação insere-se no projecto FrailCare.AI, que visa detectar a fragilidade
da população idosa portuguesa com o objectivo de optimizar o serviço de telemonitoriza-
ção do Sistema Nacional de Saúde Português (SNS24), e também sugerir acções a tomar
para reduzir a fragilidade dos doentes. A fragilidade é uma condição de risco composta
por multiplos fatores.
Hoje em dia, grande parte da história clinica de cada utente é gravada digitalmente.
Estes dados diversos e vastos podem ser usados treinar modelos preditivos cujo objectivo
é prever futuros estados de saúde, sendo que fragilidade é só um deles.
Devido à falta de accesso a dados, alteramos a tarefa principal deste trabalho para
previsão de diágnosticos, onde exploramos o problema de insuficiência de dados e dese-
quilÃbrio de classes (por exemplo, doenças raras e outras ocorrências pouco frequentes
nos dados de treino), integrando ontologias de conceitos médicos por meio de redes neu-
ronais de gráfos. Exploramos também outras tarefas e o impacto que elas têm entre si.
Para além disso, durante o desenvolvimento desta dissertação identificamos questões a
nivel de reproducibilidade da literatura estudada, onde detalhamos e implementamos
os conceitos em falta. Com o objectivo de reproducibilidade em mente, nós libertamos o
nosso código, introduzindo um biblioteca que permite desenvlver sistemas semelhantes
ao nosso
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