Automated machine learning for healthcare and clinical notes analysis

Machine learning (ML) has been slowly entering every aspect of our lives and its positive impact has been astonishing. To accelerate embedding ML in more applications and incorporating it in real-world scenarios, automated machine learning (AutoML) is emerging. The main purpose of AutoML is to provide seamless integration of ML in various industries, which will facilitate better outcomes in everyday tasks. In healthcare, AutoML has been already applied to easier settings with structured data such as tabular lab data. However, there is still a need for applying AutoML for interpreting medical text, which is being generated at a tremendous rate. For this to happen, a promising method is AutoML for clinical notes analysis, which is an unexplored research area representing a gap in ML research. The main objective of this paper is to fill this gap and provide a comprehensive survey and analytical study towards AutoML for clinical notes. To that end, we first introduce the AutoML technology and review its various tools and techniques. We then survey the literature of AutoML in the healthcare industry and discuss the developments specific to clinical settings, as well as those using general AutoML tools for healthcare applications. With this background, we then discuss challenges of working with clinical notes and highlight the benefits of developing AutoML for medical notes processing. Next, we survey relevant ML research for clinical notes and analyze the literature and the field of AutoML in the healthcare industry. Furthermore, we propose future research directions and shed light on the challenges and opportunities this emerging field holds. With this, we aim to assist the community with the implementation of an AutoML platform for medical notes, which if realized can revolutionize patient outcomes

Doctor of Philosophy

dissertationThe primary objective of cancer registries is to capture clinical care data of cancer populations and aid in prevention, allow early detection, determine prognosis, and assess quality of various treatments and interventions. Furthermore, the role of cancer registries is paramount in supporting cancer epidemiological studies and medical research. Existing cancer registries depend mostly on humans, known as Cancer Tumor Registrars (CTRs), to conduct manual abstraction of the electronic health records to find reportable cancer cases and extract other data elements required for regulatory reporting. This is often a time-consuming and laborious task prone to human error affecting quality, completeness and timeliness of cancer registries. Central state cancer registries take responsibility for consolidating data received from multiple sources for each cancer case and to assign the most accurate information. The Utah Cancer Registry (UCR) at the University of Utah, for instance, leads and oversees more than 70 cancer treatment facilities in the state of Utah to collect data for each diagnosed cancer case and consolidate multiple sources of information.Although software tools helping with the manual abstraction process exist, they mainly focus on cancer case findings based on pathology reports and do not support automatic extraction of other data elements such as TNM cancer stage information, an important prognostic factor required before initiating clinical treatment. In this study, I present novel applications of natural language processing (NLP) and machine learning (ML) to automatically extract clinical and pathological TNM stage information from unconsolidated clinical records of cancer patients available at the central Utah Cancer Registry. To further support CTRs in their manual efforts, I demonstrate a new approach based on machine learning to consolidate TNM stages from multiple records at the patient level

Extracting clinical knowledge from electronic medical records

As the adoption of Electronic Medical Records (EMRs) rises in the healthcare institutions, these resources’ importance increases due to all clinical information they contain about patients. However, the unstructured information in the form of clinical narratives present in these records makes it hard to extract and structure useful clinical knowledge. This unstructured information limits the potential of the EMRs because the clinical information these records contain can be used to perform essential tasks inside healthcare institutions such as searching, summarization, decision support and statistical analysis, as well as be used to support management decisions or serve for research. These tasks can only be done if the unstructured clinical information from the narratives is appropriately extracted, structured and processed in clinical knowledge. Usually, this information extraction and structuration in clinical knowledge is performed manually by healthcare practitioners, which is not efficient and is error-prone. This research aims to propose a solution to this problem, by using Machine Translation (MT) from the Portuguese language to the English language, Natural Language Processing (NLP) and Information Extraction (IE) techniques. With the help of these techniques, the goal is to develop a prototype pipeline modular system that can extract clinical knowledge from unstructured clinical information contained in Portuguese EMRs, in an automated way, in order to help EMRs to fulfil their potential and consequently help the Portuguese hospital involved in this research. This research also intends to show that this generic prototype system and approach can potentially be applied to other hospitals, even if they don’t use the Portuguese language.Com a adopção cada vez maior das instituições de saúde face aos Processos Clínicos Electrónicos (PCE), estes documentos ganham cada vez mais importância em contexto clínico, devido a toda a informação clínica que contêm relativamente aos pacientes. No entanto, a informação não estruturada na forma de narrativas clínicas presente nestes documentos electrónicos, faz com que seja difícil extrair e estruturar deles conhecimento clínico. Esta informação não estruturada limita o potencial dos PCE, uma vez que essa mesma informação, caso seja extraída e estruturada devidamente, pode servir para que as instituições de saúde possam efectuar actividades importantes com maior eficiência e sucesso, como por exemplo actividades de pesquisa, sumarização, apoio à decisão, análises estatísticas, suporte a decisões de gestão e de investigação. Este tipo de actividades apenas podem ser feitas com sucesso caso a informação clínica não estruturada presente nos PCE seja devidamente extraída, estruturada e processada em conhecimento clínico. Habitualmente, esta extração é realizada manualmente pelos profissionais médicos, o que não é eficiente e é susceptível a erros. Esta dissertação pretende então propôr uma solução para este problema, ao utilizar técnicas de Tradução Automática (TA) da língua portuguesa para a língua inglesa, Processamento de Linguagem Natural (PLN) e Extração de Informação (EI). O objectivo é desenvolver um sistema protótipo de módulos em série que utilize estas técnicas, possibilitando a extração de conhecimento clínico, de uma forma automática, de informação clínica não estruturada presente nos PCE de um hospital português. O principal objetivo é ajudar os PCE a atingirem todo o seu potencial em termos de conhecimento clínico que contêm e consequentemente ajudar o hospital português em questão envolvido nesta dissertação, demonstrando também que este sistema protótipo e esta abordagem podem potencialmente ser aplicados a outros hospitais, mesmo que não sejam de língua portuguesa

Relations of EEG and Perceived Response to Methylphenidate among Children with Attention Deficit Hyperactivity Disorder

Methylphenidate (MPH) is a common stimulant medication that has demonstrated efficacy in treatment among individuals with attention deficit hyperactivity disorder (ADHD) as well as those with co-occurring oppositional defiant disorder (ODD) symptoms (Connor et al., 2002, Cortese et al., 2018). However, there are currently no known reliable markers to predict response to MPH (Kim et al., 2015) and current approaches rely on trial-and-error by patients. Electroencephalographic (EEG) methods show promise as one tool to identify and predict MPH response. The current study examined relations between EEG frequencies and perceived response to MPH across both ADHD and ODD symptoms utilizing caregiver report on the Strengths and Weaknesses of Attention-Deficit/Hyperactivity Symptoms and Normal Behaviors (SWAN; Swanson et al., 2012). Participants included 30 children with ADHD (70% male) between the ages of 7 -11 years (MAge = 121.27 months, SD = 16.47 months) and their primary caregivers. Children’s absolute power frequencies were gathered during a resting state EEG paradigm. Caregivers completed measures regarding their child’s medication history, and retrospectively rated their child’s ADHD and ODD symptoms across pre-MPH and optimal MPH dosage timepoints. Results indicated that alpha frequency was marginally predictive of SWAN scores at optimal-MPH dosage while controlling for SWAN scores prior to MPH (p = .058). No other frequency bands examined demonstrated significant relations. Given the small sample size and low statistical power of this study, the results may underestimate relations between EEG frequencies and SWAN scores. These findings provide preliminary support for EEG spectral power as a potential predictor of MPH response, lending credence for future investigation and potential clinical utility

An Overview on Patient-Centered Clinical Services

Drug-related problems (DRPs) had often been a concern in the system that needed to be detected, avoided, and addressed as soon as possible. The need for a clinical pharmacist becomes even more important. He is the one who can not only share the load but also be an important part of the system by providing required advice. They fill out the patient's pharmacotherapy reporting form and notify the medical team's head off any drug-related issues. General practitioners register severe adverse drug reactions (ADRs) yearly. As a result of all of this, a clinical pharmacist working in and around the healthcare system is expected to advance the pharmacy industry. Its therapy and drugs can improve one's health quality of life by curing, preventing, or diagnosing a disease, sign, or symptom. The sideshows, on the other hand, do much harm. Because of the services they offer, clinical pharmacy has grown in popularity. To determine the overall effect and benefits of the emergency department (ED) clinical pharmacist, a systematic review of clinical practice and patient outcomes will be needed. A clinical pharmacist's anatomy, toxicology, pharmacology, and medicinal chemistry expertise significantly improves a patient's therapy enforcement. It is now important to examine the failure points of healthcare systems as well as the individuals involved