Knowledge engineering complex decision support system in managing rheumatoid arthritis.

Background: The management of rheumatoid arthritis (RA) involves partially recursive attempts to make optimal treatment decisions that balance the risks of the treatment to the patient against the benefits of the treatment, while monitoring the patient closely for clinical response, as inferred from prior and residual disease activity, and unwanted drug effects, including abnormal laboratory findings. To the extent that this process is logical, based on best available evidence and determined by considered opinion, it should be amenable to capture within a Clinical Decision Support Systems (CDSSs). The formalisation of logical transformations and their execution by computer tools at point of patient encounter holds the promise of more efficient and consistent use of treatment rules and more reliable clinical decision making. Research Setting: The early Rheumatoid Arthritis (eRA) clinic of the Royal Adelaide Hospital (RAH) with approximately 20 RA patient visits per week, and involving 160 patients with a median duration of treatment of more than 4.5 years. Methods: The study applied a Knowledge Engineering approach to interpret the complexities of RA management, in order to implement a knowledge-based CDSS. The study utilised Knowledge Acquisition processes to elicit and explicitly define the RA management rules underpinning the development of the CDSS; the processes were (1) conducting a comprehensive literature review of RA management, (2) observing clinic consultations and (3) consulting with local clinical experts/leaders. Bayes’ Theorem and Bayes Net were used to generate models for assessing contingent probabilities of unwanted events. A questionnaire based on 16 real patient cases was developed to test the concordance agreement between CDSS generated guidance in response to real-life clinical scenarios and decisions of rheumatologists in response to the scenarios. Results: (1) Complex RA management rules were established which included (a) Rules for Changes in Dose/Agent and (b) Drug Toxicity Monitoring Rules. (2) A computer interpretable dynamic model for implementing the complex clinical guidance was found to be applicable. (3) A framework for a methotrexate (MTX) toxicity prediction model was developed, thereby allowing missing risk ratios (probabilities) to be identified. (4) Clinical decision-making processes and workflows were described. Finally, (5) a preliminary version of the CDSS which computed Rules for Changes in Dose/Agent and Drug Toxicity Monitoring Rules was implemented and tested. One hundred and twenty-eight decisions collected from the 8 participating rheumatologists established the ability of the CDSS to match decisions of clinicians accustomed to application of Rules for Changes in Dose/Agent; rheumatologists unfamiliar with the rules displayed lower concordance (0.7857 vs. 0.3929, P = 0.0027). Neither group of rheumatologists matched the performance of the CDSS in making decisions based on highly complex Drug Toxicity Monitoring Rules (0.3611 vs. 0.4167, P = 0.7215). Conclusion: The study has made important contributions to the development of a CDSS suitable for routine use in the eRA clinic setting. Knowledge Acquisition processes were used to elicit domain knowledge, and to refine, validate and articulate eRA management rules, that came to form the knowledge base of the CDSS. The development of computer interpretable guideline models underpinned the CDSS development. The alignment of CDSS guidance in response to clinical scenarios with questionnaire responses of rheumatologists familiar with and accepting of the management rules (and divergence with responses by rheumatologists not familiar with the rules) indicates that the CDSS can be used to guide toward evidence-based considered opinion. The poor correlation between CDSS generated guidance regarding out of range blood results and response of rheumatologists to questions regarding toxicity scenarios, underlines the value of computer aided guidance when decisions involve greater complexity. It also suggests the need for attention to rule development and considered opinion in this area. Discussion: Effective utilisation of extant knowledge is fundamental to knowledgebased systems in healthcare. CDSSs development for chronic disease management is a complex undertaking which is tractable using Knowledge Engineering and Knowledge Acquisition approaches coupled with modelling into computer interpretable algorithms. Complexities of drug toxicity monitoring were addressed using Bayes’ Theorem and Bayes Net for making probability based decisions under conditions of uncertainty. While for logistic reasons the system could not be developed to full implementation, preliminary analyses support the utility of the approach, both for intensifying treatment on a response contingent basis and also for complex drug toxicity monitoring. CDSSs are inherently suited to iterative refinements based on new knowledge including that arising from analyses of the data they capture during their use. This study has achieved important steps toward implementation and refinement.Thesis (Ph.D.) -- University of Adelaide, School of Medicine, 201

Construction of Adverse Events Monitoring View for People Living with HIV Based on AIDS Database

Background Acquired immunodeficiency syndrome (AIDS), is a global malignant infectious disease with extremely high fatality rate caused by human immunodeficiency virus (HIV). Regarding there is still no AIDS vaccine or cure in the world so far, the usage of Highly Active Anti-Retroviral Therapy (HAART) is currently the most effective way to suppress viral replication and also the basic therapy. However, drug resistance and different degrees of adverse events (AE) on PLWHIV could occur and cause major impact on health and quality of life for PLWHIV. Therefore, continuous monitoring and assessment of AE play a key role for PLWHIV. At present, decentralized clinical data are suggested to be a major problem during AE monitoring process, thus digital unified view of AE monitoring is asked for badly from health professionals to simplify the tedious process of clinical data collection. However, current domestic and foreign research still lacks a unified view of AIDS-specific clinical information. Therefore, this study intends to design and construct an AE Monitoring View for PLWHIV who receive HAART based on AIDS database, through which clinicians and nurses are able to be assisted in clinical decision-making, nursing diagnosis as well as timely corresponding intervention measures. Objectives The Overall objective is to construct an AE Monitoring View for PLWHIV. There are 3 specific objectives, which are demonstrated respectively: (1) To explore the demand of AE Monitoring View for PLWHIV among clinicians and nurses, (2) To construct the framework of AE Monitoring View for PLWHIV, (3) To develop and perform functional tests on the implemented functions. Methods The research was comprised of 3 parts: Part 1: The demand exploration of AE Monitoring View for PLWHIV among clinicians and nurses The researcher conducted semi-structured interviews to learn about current monitoring process of AE for PLWHIV, current common and rare AE and interventions on AE for PLWHIV, current problems clinicians and nurses would meet with during AE monitoring process, and their usage requirements on AE Monitoring View. Part 2: The construction of the framework on AE Monitoring View for PLWHIV The researcher established a research team with clinicians and nurses and technicians to discuss about the framework and drafted a first version based on relevant literature, drug instructions, Common Terminology Criteria for Adverse Events (CTCAE), and interview results. The researchers then sent the version to 14 experts for expert argumentation, until all experts agreed and the final framework version was finalized and moved to the next stage. Part 3: Testing and application of AE Monitoring View for PLWHIV Based on the final version of framework previously developed for AE Monitoring View, the researcher developed and internally tested the view in collaboration with technicians from a medical technology company, which the researcher then handed over to the research team with a questionnaire investigated later to conduct internal feasibility pilot-test for usability evaluation. This view is yet unmature and will be put into use after the AIDS Database is fully constructed in the future. Results Part 1: The demand exploration of AE Monitoring View for PLWHIV among clinicians and nurses Based on the interview results of 11 clinicians and nurses, the researcher learned about the most common clinical AE in AIDS patients and their monitoring status. In addition, the researcher also summarized the current clinical workers' requirements for electronic information systems on monitoring process of AE. The demand exploration shows: 1) Current AE monitoring process, including patient self-reports and regular patient review, 2) Common and rare AE for PLWHIV, such as rash, neurological symptoms, gastrointestinal disorders and so on, 3) Common interventions from clinicians and nurses on AE for PLWHIV, for instance, continuous monitoring, conventional conservative treatment and replacement of drug regimen， 4) The problems of current AE monitoring process, including continuity and accuracy, 5) Requirements on AE Monitoring View for PLWHIV, like visualization tool, list of AE with manifestations and interventions. Part 2: The construction of the framework on AE Monitoring View for PLWHIV The researcher established a research team of 11 members for frame design and content construction on AE Monitoring View for PLWHIV. After literature study and discussion, the researcher drafted out the preliminary framework of AE Monitoring View for PLWHIV. Meanwhile, through two rounds of Delphi expert consultation methods and collected expert opinions, the researcher optimized and improved the content of framework, and determined the final version of framework for AE Monitoring View, including 5 levels, which was drug name, system AE belongs to, specific AE, manifestations and corresponding interventions. According to the opinions of experts, the researcher finally deleted the items such as “allergic reaction”, “acidosis”, “hypophosphatemia”, etc., and added items such as “inattention” and “lactic acidosis”. At the same time, according to the specificity of AIDS and the uniqueness of AE caused by antiviral drugs, the researcher modified and improved the symptoms and corresponding interventions in a targeted manner. For example, most somatic symptoms such as dizziness and headache are mild Symptoms, which do not require intervention, will gradually improve after taking the drug for a period of time. These are slightly different from those described in the CTCAE, thus the researcher has made modifications based on the recommendations made by experts. Part 3: Testing and application of AE Monitoring View for PLWHIV The researcher presented the final version over the content framework of the AE Monitoring View for PLWHIV to technicians and collaborated on the development of the Monitoring View, which was internally functionally tested. The actual results were consistent with the expected results, and the research team subsequently conducted a pre-test usability evaluation of the Monitoring View, which indicated a high usability of the AE Monitoring View for PLWHIV. Conclusions (1) The current state of AE monitoring process and the demands of clinicians and nurses for an AE Monitoring View for PLWHIV were investigated through qualitative interviews, (2) Based on AIDS database, the content framework of the AE Monitoring View for PLWHIV was determined through two rounds of Delphi expert consultations based on the existing literature and CTCAE criteria as a guideline, (3) The researcher and the technicians from the medical technology company cooperated to develop and internally test the AE Monitoring View for PLWHIV. After the AIDS Database is successfully built, it will be released to public together.Tausta Immuunikato, Acquired immunodeficiency syndrome (AIDS), on maailmanlaajuinen pahanlaatuinen tartuntatauti, jolla on erittäin korkeat luvut kuolemantapauksien suhteen, jotka aiheuttavat HI-virus. Maailmassa ei ole vielä AIDS-rokotetta tai parannuskeinoa, mutta Highly Active Anti-Retroviral Therapy (HAART) käyttö on tällä hetkellä tehokkain tapa tukahduttaa viruksen replikaatio. HIV-potilailla voi kuitenkin esiintyä lääkeresistenssiä ja erilaisia haittavaikutuksia ja ne voivat aiheuttaa merkittäviä vaikutuksia HIV-potilaiden terveyteen ja elämänlaatuun. Tästä syystä haittatapahtumien jatkuva seuranta ja arviointi ovat avainasemassa HIV-potilailla. Nykyään, suurin ongelma haittatapahtumien seurannassa on ehdotettu olevan hajallaan olevat kliiniset tiedot. Siksi olisikin tärkeää yksinkertaistaa kliinisten tietojen keräämistä. Nykyisestä kansallisesta ja ulkomaisesta tutkimuksesta puuttuu kuitenkin edelleen yhtenäinen näkemys AIDS-spesifisestä kliinisestä tiedosta. Siksi tämän tutkimuksen tarkoituksena on suunnitella ja rakentaa haittatapahtumien seurantajärjestelmä HIV-potilaille, jotka saavat HAART-hoitoa. Haittatapahtumien seurantajärjestelmän avulla voidaan auttaa lääkäreitä ja sairaanhoitajia kliinisessä päätöksenteossa, hoitotyön diagnoosien tekemisessä sekä oikea-aikaisten hoitotoimenpiteiden valinnassa. Tavoitteet Tavoitteena on rakentaa haittatapahtumien seurantajärjestelmä HIV-potilaille. Tutkielmassa on kolme osatavoitetta: (1) Tutkia HIV-potilaiden haittatapahtumien seurantajärjestelmän tarvetta lääkäreiden ja hoitajien näkökulmasta (2) Rakentaa HIV-potilaiden haittatapahtumien seurantajärjestelmälle viitekehys (3) Kehittää ja suorittaa toiminnallisia testejä haittatapahtumien seurantajärjestelmälle Metodit Tutkimus toteutettiin kolmessa eri vaiheessa: Vaihe 1: Tarve HIV-potilaiden haittatapahtumien seurantajärjestelmälle lääkäreiden ja sairaanhoitajien näkökulmasta Tutkija suoritti puolistrukturoidut haastattelut oppiakseen HIV-potilaiden tämänhetkisestä haittatapahtumien seurannasta, oppiakseen HIV-potilaiden yleisistä ja harvinaisista haittatapahtumista, selvittääkseen, mitkä ovat nykyisiä ongelmia haittatapahtumien seurannassa, joita lääkärit ja sairaanhoitajat kohtaavat sekä selvittääkseen millaisia vaatimuksia lääkäreillä ja sairaanhoitajilla olisi haittatapahtumien seurantajärjestelmälle. Vaihe 2: HIV-potilaiden haittatapahtumien seurantajärjestelmän viitekehyksen rakentaminen Tutkija perusti tutkimusryhmän lääkäreiden, sairaanhoitajien ja teknikkojen kanssa keskustellakseen viitekehyksestä ja laati ensimmäiseen version, joka perustui kirjallisuuteen, lääkeohjeisiin, Common Terminology Criteria for Adverse Events (CTCAE) -kriteereihin ja haastattelun tuloksiin. Sen jälkeen ensimmäinen versio haittatapahtumien seurantajärjestelmästä lähetettiin 14 asiantuntijalle arvioitavaksi. Kunnes kaikki asiantuntijat olivat yhtä mieltä, lopullinen versio viimeisteltiin ja siirryttiin seuraavaan vaiheeseen. Vaihe 3: HIV-potilaiden haittavaikutusten seurantajärjestelmän testaus ja soveltaminen Tutkija kehitti ja testasi edellisessä vaiheessa kehitettyä lopullista versiota haittavaikutusten seurantajärjestelmästä yhteistyössä lääketieteellisen teknologian yrityksen teknikoiden kanssa. Tämän jälkeen tutkimusryhmän jäsenet arvioivat seurantajärjestelmän kyselylomakkeen avulla. Käytettävyyskyselyn tuloksia hyödynnetään tulevaisuudessa, kun AIDS-tietokantaa kehitetään edelleen. Tulokset Vaihe 1: Tarve HIV-potilaiden haittavaikutusten seurantajärjestelmälle lääkäreiden ja sairaanhoitajien näkökulmasta Haastattelun tulosten perusteella (n=11 lääkäriä ja sairaanhoitajaa) tutkija oppi, mitkä ovat AIDS-potilaiden yleisimpiä kliinisiä haittavaikutuksia ja miten niitä seurataan. Lisäksi tutkija kokosi kliinisten työntekijöiden tarpeet ja vaatimukset elektroniseen haittavaikutusten seurantajärjestelmään liittyen. Tulokset osoittavat: 1) Haittavaikutusten nykyisen seurantaprosessin, mukaan lukien potilaan itseraportit ja säännöllinen potilasarviointi, 2) Yleiset ja harvinaiset haittavaikutukset kuten ihottuman, neurologiset oireet, ruoansulatuskanavan oireet 3) Yleiset hoitokeinot, kuten jatkuva seuranta, tavanomainen konservatiivinen hoito ja lääkehoidon korvaaminen 4) Ongelmat nykyisessä seurantajärjestelmässä, kuten ongelmat jatkuvuudessa ja tarkkuudessa 5) Vaatimukset HIV-potilaiden haittavaikutusten seurantaohjelmalle, kuten visualisointityökalu, luettelo haittavaikutuksista ja niiden hoitokeinoista Vaihe 2: HIV-potilaiden haittatapahtumien seurantajärjestelmän viitekehyksen rakentaminen Tutkija perusti 11 henkilön tutkimusryhmän haittavaikutusten seurantajärjestelmän viitekehyksen suunnittelemiseksi ja sisällön rakentamiseksi. Kirjallisuuteen tutustumisen jälkeen, tutkija teki ensimmäisen luonnoksen. Kahden Delphi-asiantuntijapaneelin konsultointikierroksen jälkeen tutkija kehitti ensimmäistä versiota palautteiden perusteella ja lopulta haittavaikutusten seurantajärjestelmän luonnos koostui viidestä eri tasosta, mitkä olivat: lääkkeen nimi, haittavaikutuksen kategoria, haittavaikutus, ilmenemismuodot ja hoitotoimenpiteet. Asiantuntijoiden palautteiden mukaan tutkija poisti lopulta nimikkeet, kuten ”allerginen reaktio”, ”asidoosi”, ”hypofosfatemia” ja lisäsi nimikkeitä, kuten ”tarkkaamattomuus” ja ”maitohappoasidoosi”. Samaan aikaan AIDS:n spesifisyyden ja epävirallisten lääkkeiden aiheuttamien haittavaikutusten ainutlaatuisuuden vuoksi, tutkija muutti ja paransi oireiden ja hoitotoimenpiteiden nimikkeitä kohdennetusti. Esimerkiksi useimmat somaattiset oireet, kuten huimaus ja päänsärky, ovat lieviä oireita, jotka eivät vaadi hoitotoimenpiteitä, paranevat vähitellen lääkkeen ottamisen jälkeen jonkin aikaa. Nämä ovat hieman erilaisia kuin CTCAE:ssä kuvatut, joten tutkija on tehnyt muutoksia asiantuntijoiden suositusten perusteella. Vaihe 3: HIV-potilaiden haittavaikutusten seurantajärjestelmän testaus ja soveltaminen Tutkija esitteli teknikoille lopullisen version HIV-potilaiden haittavaikutusten seurantajärjestelmän sisältökehyksestä ja teki yhteistyötä seurantajärjestelmän kehittämisessä, joka testattiin ryhmän sisäisesti. Tulokset olivat yhdenmukaisia odotettujen tulosten kanssa, ja tutkimusryhmä teki vielä myöhemmin seurantajärjestelmän käytettävyystestauksen, joka osoitti, että HIV-potilaiden haittavaikutusten seurantajärjestelmän käytettävyys on korkealla tasolla. Johtopäätökset (1) Haittavaikutusten seurantajärjestelmän nykytilaa sekä lääkäreiden ja sairaanhoitajien vaatimuksia seurantajärjestelmään liittyen tutkittiin laadullisten haastattelujen avulla, (2) AIDS-tietokannan perusteella haittavaikutusten seurantajärjestelmän sisältökehys määritettiin kahdella Delphin asiantuntijakuulemiskierroksella, jotka perustuivat olemassa olevaan kirjallisuuteen ja CTCAE:n kriteereihin, (3) Tutkija ja lääketieteellisen teknologiayrityksen teknikot yhteistyössä kehittivät ja testasivat HIV-potilaiden haittavaikutusten seurantajärjestelmän. Kun AIDS-tietokanta on rakennettu onnistuneesti, se julkaistaan laajemmalle yleisölle

Recent advancement in Disease Diagnostic using machine learning: Systematic survey of decades, comparisons, and challenges

Computer-aided diagnosis (CAD), a vibrant medical imaging research field, is expanding quickly. Because errors in medical diagnostic systems might lead to seriously misleading medical treatments, major efforts have been made in recent years to improve computer-aided diagnostics applications. The use of machine learning in computer-aided diagnosis is crucial. A simple equation may result in a false indication of items like organs. Therefore, learning from examples is a vital component of pattern recognition. Pattern recognition and machine learning in the biomedical area promise to increase the precision of disease detection and diagnosis. They also support the decision-making process's objectivity. Machine learning provides a practical method for creating elegant and autonomous algorithms to analyze high-dimensional and multimodal bio-medical data. This review article examines machine-learning algorithms for detecting diseases, including hepatitis, diabetes, liver disease, dengue fever, and heart disease. It draws attention to the collection of machine learning techniques and algorithms employed in studying conditions and the ensuing decision-making process

The Use Of Non-Invasive Fibrosis Markers In Stratification Care Pathways For The Management Of Chronic Liver Disease

The health, societal and economic consequences of chronic liver disease (CLD) are substantial and increasing exponentially. Cirrhosis is typically detected in the latter stages when prognosis is poor. Timely diagnosis is hindered by reliance on non-discriminatory tests for fibrosis. I explored the role of non-invasive tests (NITs) of liver fibrosis in primary care to promote earlier disease detection. In this thesis, a systematic review revealed a paucity of published studies evaluating NIT in the community setting. A national survey demonstrated that UK specialists consider current fibrosis assessment methods to be sub-optimal, and NIT are important in improving disease stratification in primary care. To benchmark standard care, a one-year retrospective study of GP referrals for non-alcoholic fatty liver disease (NAFLD) established 93% of referrals to have non-significant fibrosis (Brunt ≤ F2) as assessed by liver specialists. Over two-thirds had a low-risk FIB-4 (<1.30) and could have avoided referral, although a quarter of patients with indeterminate FIB-4 (1.30 – 3.25) had significant liver fibrosis suggesting patients in this subgroup warrant further evaluation. As part of the Camden and Islington liver working group, I developed and evaluated a NAFLD pathway that employs FIB-4 and ELF to identify patients with advanced fibrosis or cirrhosis (Brunt ≥ F3 fibrosis). The pathway processed nearly 1500 patients over two years, resulting in a reduction in the proportion of total patients referred and an 81% decrease in referral of patients with non-significant fibrosis. The pathway achieved a 5-fold increase in the referral of patients with advanced fibrosis and 3-fold increase in the detection of liver cirrhosis. To further extrapolate these findings, I developed a probabilistic decision analytical model which tested FIB-4, ELF and fibroscan, either alone or in combination in primary care pathways. Cost consequence analyses revealed all strategies to be clinically effective and cost-saving compared to standard care

The Utility Of Thromboelastography In Acute Perioperative Trauma Resuscitation Of The Adult Coagulopathic Patient

One quarter of all patients admitted to level I trauma centers receive transfused blood, and approximately 25% of trauma transfusion recipients are diagnosed with coagulopathies during the resuscitation process (Hess et al., 2008; Kutcher & Cohen, 2021; Maegele et al., 2007). Such pathologies have been associated with negative clinical outcomes such as increased transfusion requirements, organ failure, sepsis, and death. (Barash et al., 2013; Cole et al., 2019; Hess et al., 2008; Sayce et al., 2020). Current laboratory standards of care to diagnose coagulopathies such as prothrombin time (PT), international normalized ratio (INR), and activated partial thromboplastin time (aPTT) are time consuming to obtain and may not reflect a trauma patient’s ongoing coagulation status (Baksaas-Aasen et al., 2020; Barash et al., 2013; Davenport et al., 2011). Consequently, point-of-care tests of hemostatic function such as thromboelastography (TEG) may be of use to the anesthesia provider. An examination of the history of TEG, review of the current literature, and analysis of future research directions has revealed certain limitations such as a potentially extensive clinician learning curve, minimal integration into existing hospital structures, and a lack of level-1 evidence. However, thromboelastography has the potential to optimize outcomes in the coagulopathic patient when used in conjunction with conventional coagulation tests—providing a complementary real-time graphic depiction of the poorly understood syndrome of trauma induced coagulopathy

User Interfaces for Patient-Centered Communication of Health Status and Care Progress

The recent trend toward patients participating in their own healthcare has opened up numerous opportunities for computing research. This dissertation focuses on how technology can foster this participation, through user interfaces to effectively communicate personal health status and care progress to hospital patients. I first characterize the design space for electronic information communication to patients through field studies conducted in multiple hospital settings. These studies utilize a combination of survey instruments, and low- and high-fidelity prototypes, including a document-editing prototype through which users can view and manage clinical data to automatically associate it with progress notes. The prototype, activeNotes, includes the first known techniques supporting clinical information requests directly within a document editor. A usage study with ICU physicians at New York-Presbyterian Hospital (NYP) substantiated our design and revealed how electronic information related to patient status and care progress is derived from a typical Electronic Health Record system. Insights gained from this study informed following studies to understand how to design abstracted, plain-language views suitable for patients. We gauged both patient and physician responses to information display prototypes deployed in patient rooms for a formative study exploring their design. Following my reports on this study, I discuss the design, development and pilot evaluations of a prototype Personal Health Record application providing live, abstracted clinical information for patients at NYP. The portal, evaluated by cardiothoracic surgery patients, is the first of its kind to allow patients to capture and monitor live data related to their care. Patient use of the portal influenced the subsequent design of tools to support users in making sense of online medication information. These tools, designed with nurses and pharmacists and evaluated by cardiothoracic surgery patients at NYP, were developed using topic modeling approaches and text analysis techniques. Embodied in a prototype called Remedy, they enable rapid filtering and comparison of medication-related search results, based on a number of website features and content topics. I conclude by discussing how findings from this series of studies can help shape the ongoing design and development of patient-centered technology

Leveraging Knowledge-Based Approaches to Promote Antiretroviral Toxicity Monitoring in Underserved Settings

As access and use of antiretroviral therapy continue to increase, the need to improve antiretroviral toxicity monitoring becomes more critical. This is particularly so in underserved settings, where patterns of antiretroviral toxicities possibly alter the need for and frequency of antiretroviral toxicity monitoring. However, barriers such as few skilled healthcare providers and poor infrastructure make antiretroviral toxicity monitoring in underserved settings difficult. The purpose of this dissertation was to investigate how standard clinical guidelines, knowledge-based clinical decision support, and task delegation could be leveraged to overcome barriers to antiretroviral toxicity monitoring in underserved settings. The strategy adopted in this dissertation was guided by the Design Science Research Methodology that emphasizes the generation of scientific knowledge through building novel artifacts. Two qualitative descriptive studies were conducted to characterize the contextual factors associated with antiretroviral toxicity monitoring in underserved settings. Supported by the findings from these studies, a knowledge-based software application prototype that implements clinical practice guidelines for antiretroviral toxicity monitoring was developed. Next, a quantitative validation study was used to evaluate the structure and behavior of the prototype’s knowledge base. Lastly, a quantitative usability study was conducted to assess lay health worker perceptions of the satisfaction and mental effort associated with the use of checklists generated by the prototype. This dissertation research produced empirical evidence about the broad motives and strategies for promoting medication adherence, safety, and effectiveness in underserved settings. It also identified barriers and facilitators of antiretroviral toxicity monitoring within ambulatory HIV care workflows in underserved settings. Additionally, it provided evidence about the extent to which antiretroviral toxicity domain knowledge could be implemented in a knowledge-based application for supporting point-of-care antiretroviral toxicity monitoring. Lastly, the research provided previously unavailable empirical evidence about the perceptions of lay peer health workers on the use of checklists for the documentation of antiretroviral toxicities

Ontology driven clinical decision support for early diagnostic recommendations

Diagnostic error is a significant problem in medicine and a major cause of concern for patients and clinicians and is associated with moderate to severe harm to patients. Diagnostic errors are a primary cause of clinical negligence and can result in malpractice claims. Cognitive errors caused by biases such as premature closure and confirmation bias have been identified as major cause of diagnostic error. Researchers have identified several strategies to reduce diagnostic error arising from cognitive factors. This includes considering alternatives, reducing reliance on memory, providing access to clear and well-organized information. Clinical Decision Support Systems (CDSSs) have been shown to reduce diagnostic errors. Clinical guidelines improve consistency of care and can potentially improve healthcare efficiency. They can alert clinicians to diagnostic tests and procedures that have the greatest evidence and provide the greatest benefit. Clinical guidelines can be used to streamline clinical decision making and provide the knowledge base for guideline based CDSSs and clinical alert systems. Clinical guidelines can potentially improve diagnostic decision making by improving information gathering. Argumentation is an emerging area for dealing with unstructured evidence in domains such as healthcare that are characterized by uncertainty. The knowledge needed to support decision making is expressed in the form of arguments. Argumentation has certain advantages over other decision support reasoning methods. This includes the ability to function with incomplete information, the ability to capture domain knowledge in an easy manner, using non-monotonic logic to support defeasible reasoning and providing recommendations in a manner that can be easily explained to clinicians. Argumentation is therefore a suitable method for generating early diagnostic recommendations. Argumentation-based CDSSs have been developed in a wide variety of clinical domains. However, the impact of an argumentation-based diagnostic Clinical Decision Support System (CDSS) has not been evaluated yet. The first part of this thesis evaluates the impact of guideline recommendations and an argumentation-based diagnostic CDSS on clinician information gathering and diagnostic decision making. In addition, the impact of guideline recommendations on management decision making was evaluated. The study found that argumentation is a viable method for generating diagnostic recommendations that can potentially help reduce diagnostic error. The study showed that guideline recommendations do have a positive impact on information gathering of optometrists and can potentially help optometrists in asking the right questions and performing tests as per current standards of care. Guideline recommendations were found to have a positive impact on management decision making. The CDSS is dependent on quality of data that is entered into the system. Faulty interpretation of data can lead the clinician to enter wrong data and cause the CDSS to provide wrong recommendations. Current generation argumentation-based CDSSs and other diagnostic decision support systems have problems with semantic interoperability that prevents them from using data from the web. The clinician and CDSS is limited to information collected during a clinical encounter and cannot access information on the web that could be relevant to a patient. This is due to the distributed nature of medical information and lack of semantic interoperability between healthcare systems. Current argumentation-based decision support applications require specialized tools for modelling and execution and this prevents widespread use and adoption of these tools especially when these tools require additional training and licensing arrangements. Semantic web and linked data technologies have been developed to overcome problems with semantic interoperability on the web. Ontology-based diagnostic CDSS applications have been developed using semantic web technology to overcome problems with semantic interoperability of healthcare data in decision support applications. However, these models have problems with expressiveness, requiring specialized software and algorithms for generating diagnostic recommendations. The second part of this thesis describes the development of an argumentation-based ontology driven diagnostic model and CDSS that can execute this model to generate ranked diagnostic recommendations. This novel model called the Disease-Symptom Model combines strengths of argumentation with strengths of semantic web technology. The model allows the domain expert to model arguments favouring and negating a diagnosis using OWL/RDF language. The model uses a simple weighting scheme that represents the degree of support of each argument within the model. The model uses SPARQL to sum weights and produce a ranked diagnostic recommendation. The model can provide justifications for each recommendation in a manner that clinicians can easily understand. CDSS prototypes that can execute this ontology model to generate diagnostic recommendations were developed. The decision support prototypes demonstrated the ability to use a wide variety of data and access remote data sources using linked data technologies to generate recommendations. The thesis was able to demonstrate the development of an argumentation-based ontology driven diagnostic decision support model and decision support system that can integrate information from a variety of sources to generate diagnostic recommendations. This decision support application was developed without the use of specialized software and tools for modelling and execution, while using a simple modelling method. The third part of this thesis details evaluation of the Disease-Symptom model across all stages of a clinical encounter by comparing the performance of the model with clinicians. The evaluation showed that the Disease-Symptom Model can provide a ranked diagnostic recommendation in early stages of the clinical encounter that is comparable to clinicians. The diagnostic performance can be improved in the early stages using linked data technologies to incorporate more information into the decision making. With limited information, depending on the type of case, the performance of the Disease-Symptom Model will vary. As more information is collected during the clinical encounter the decision support application can provide recommendations that is comparable to clinicians recruited for the study. The evaluation showed that even with a simple weighting and summation method used in the Disease- Symptom Model the diagnostic ranking was comparable to dentists. With limited information in the early stages of the clinical encounter the Disease-Symptom Model was able to provide an accurately ranked diagnostic recommendation validating the model and methods used in this thesis

Machine Learning/Deep Learning in Medical Image Processing

Many recent studies on medical image processing have involved the use of machine learning (ML) and deep learning (DL). This special issue, “Machine Learning/Deep Learning in Medical Image Processing”, has been launched to provide an opportunity for researchers in the area of medical image processing to highlight recent developments made in their fields with ML/DL. Seven excellent papers that cover a wide variety of medical/clinical aspects are selected in this special issue