Predicting lung cancer's metastats' locations using bioclinical model.

Background: Lung cancer is a global leading cause of cancer-related deaths, and metastasis profoundly influences treatment outcomes. The limitations of conventional imaging in detecting small metastases highlight the crucial need for advanced diagnostic approaches. Methods: This study developed a bioclinical model using three-dimensional CT scans to predict the spatial spread of lung cancer metastasis. Utilizing a three-layer biological model, we identified regions with a high probability of metastasis colonization and validated the model on real-world data from 10 patients. Findings: The validated bioclinical model demonstrated a promising 74% accuracy in predicting metastasis locations, showcasing the potential of integrating biophysical and machine learning models. These findings underscore the significance of a more comprehensive approach to lung cancer diagnosis and treatment. Interpretation: This study's integration of biophysical and machine learning models contributes to advancing lung cancer diagnosis and treatment, providing nuanced insights for informed decision-making.

A post-diagnosis information and support programme for dyads-People living with dementia or mild cognitive impairment and family carers: A feasibility study.

AIM: The aim of the study was to establish the feasibility of delivering a structured post-diagnosis information and support program to dyads (persons living with dementia or mild cognitive impairment and family carers) in two primary care settings. DESIGN: A two-phase explanatory mixed-method approach guided by the Bowen Feasibility Framework focused on acceptability, implementation, adaptation, integration and efficacy of a five-part programme. In phase 1, the quantitative impact of the programme on the dyadic programme recipients' self-efficacy, quality of life, dyadic relationship and volume of care was measured. In phase 2, inductive content analysis focused on nurse and dyad participant experiences of the programme. Quantitative and qualitative data were reviewed to conclude each element of feasibility. METHODS: Four registered nurses working within the participating sites were recruited, trained as programme facilitators and supported to deliver the programme. Eligible dyads attending the respective primary health clinics were invited to participate in the programme and complete surveys at three time points: recruitment, post-programme and 3-month follow-up. Post-programme semi-structured interviews were conducted with dyads and programme facilitators. RESULTS: Twenty-nine dyads completed the program; the majority were spousal dyads. The programme proved acceptable to the dyads with high retention and completion rates. Implementation and integration of the programme into usual practice were attributed to the motivation and capacity of the nurses as programme facilitators. Regarding programme efficacy, most dyads reported they were better prepared for the future and shared the plans they developed during the programme with family members. CONCLUSION: Implementing a structured information and support programme is feasible, but sustainability requires further adaptation or increased staff resources to maintain programme fidelity. Future research should consider selecting efficacy measures sensitive to the unique needs of people living with dementia and increasing follow-up time to 6 months. IMPACT: This study established the feasibility of registered nurses delivering a post-diagnosis information and support programme for people living with early-stage dementia or mild cognitive impairment and their informal carers in primary care settings. The motivation and capacity of nurses working as programme facilitators ensured the integration of the programme into usual work, but this was not considered sustainable over time. Family carer dyads reported tangible outcomes and gained confidence in sharing their diagnosis with family and friends and asking for assistance. Findings from this study can be used to provide direction for a clinical trial investigating the effectiveness of the structured information and support programme in the primary care setting. REPORTING METHOD: The authors have adhered to the EQUATOR STROBE Statement. PATIENT OR PUBLIC CONTRIBUTION: A public hospital memory clinic and general medical practice participated in project design, study protocol development and supported implementation.

Evaluating Technicians' Workload and Performance in Diagnosis for Corrective Maintenance.

The advancement in digital technology is transforming the world. It enables smart product-service systems that improve productivity by changing tasks, processes, and the ways we work. There are great opportunities in maintenance because many tasks require physical and cognitive work, but are still carried out manually. However, the interaction between a human and a smart system is inevitable, since not all tasks in maintenance can be fully automated. Therefore, we conducted a controlled laboratory experiment to investigate the impact on technicians' workload and performance due to the introduction of smart technology. Especially, we focused on the effects of different diagnosis support systems on technicians during maintenance activity. We experimented with a model that replicates the key components of a computer numerical control (CNC) machine with a proximity sensor, a component that requires frequent maintenance. Forty-five participants were evenly assigned to three groups: a group that used a Fault-Tree diagnosis support system (FTd-system), a group that used an artificial intelligence diagnosis support system (AId-system), and a group that used neither of the diagnosis support systems. The results show that the group that used the FTd-system completed the task 15% faster than the group that used the AId-system. There was no significant difference in the workload between groups. Further analysis using the NGOMSL model implied that the difference in time to complete was probably due to the difference in system interfaces. In summary, the experimental results and further analysis imply that adopting the new diagnosis support system may improve maintenance productivity by reducing the number of diagnosis attempts without burdening technicians with new workloads. Estimates indicate that the maintenance time and the cognitive load can be reduced by 8.4 s and 15% if only two options are shown in the user interface.

Performance and clinical utility of a new supervised machine-learning pipeline in detecting rare ciliopathy patients based on deep phenotyping from electronic health records and semantic similarity.

BACKGROUND: Rare diseases affect approximately 400 million people worldwide. Many of them suffer from delayed diagnosis. Among them, NPHP1-related renal ciliopathies need to be diagnosed as early as possible as potential treatments have been recently investigated with promising results. Our objective was to develop a supervised machine learning pipeline for the detection of NPHP1 ciliopathy patients from a large number of nephrology patients using electronic health records (EHRs). METHODS AND RESULTS: We designed a pipeline combining a phenotyping module re-using unstructured EHR data, a semantic similarity module to address the phenotype dependence, a feature selection step to deal with high dimensionality, an undersampling step to address the class imbalance, and a classification step with multiple train-test split for the small number of rare cases. The pipeline was applied to thirty NPHP1 patients and 7231 controls and achieved good performances (sensitivity 86% with specificity 90%). A qualitative review of the EHRs of 40 misclassified controls showed that 25% had phenotypes belonging to the ciliopathy spectrum, which demonstrates the ability of our system to detect patients with similar conditions. CONCLUSIONS: Our pipeline reached very encouraging performance scores for pre-diagnosing ciliopathy patients. The identified patients could then undergo genetic testing. The same data-driven approach can be adapted to other rare diseases facing underdiagnosis challenges.

Validity of Diagnostic Support Model for Attention Deficit Hyperactivity Disorder: A Machine Learning Approach.

An accurate and early diagnosis of attention deficit hyperactivity disorder can improve health outcomes and prevent unnecessary medical expenses. This study developed a diagnostic support model using a machine learning approach to effectively screen individuals for attention deficit hyperactivity disorder. Three models were developed: a logistic regression model, a classification and regression tree (CART), and a neural network. The models were assessed by using a receiver operating characteristic analysis. In total, 74 participants were enrolled into the disorder group, while 21 participants were enrolled in the control group. The sensitivity and specificity of each model, indicating the rate of true positive and true negative results, respectively, were assessed. The CART model demonstrated a superior performance compared to the other two models, with region values of receiver operating characteristic analyses in the following order: CART (0.848) > logistic regression model (0.826) > neural network (0.67). The sensitivity and specificity of the CART model were 78.8% and 50%, respectively. This model can be applied to other neuroscience research fields, including the diagnoses of autism spectrum disorder, Tourette syndrome, and dementia. This will enhance the effect and practical value of our research.

Knowledge Is Power: Utilizing Human-Centered Design Principles with People Living with Dementia to Co-Design a Resource and Share Knowledge with Peers.

This paper describes the process used by a group of people living with young-onset dementia to inform the development and delivery of a post-diagnosis peer guide. It draws on the four stages of human-centered design and applies them in a new context of supporting resilience for people following a diagnosis of dementia. (1) Discover: The group discussed in-depth their perspectives on what it takes to be resilient while living with dementia and how this can be maintained. (2) Define: The group decided to collate practical information and knowledge based on their personal experiences into a booklet to support the resilience of others following a diagnosis of dementia. (3) Develop: The booklet was designed and developed together with input from other people living with dementia, facilitated by the authors. (4) Deliver: The group guided the professional production of the booklet 'Knowledge is Power'. Over 8000 copies have been distributed to memory clinics, post-diagnostic support organizations and people living with dementia across Wales. A bilingual English-Scottish Gaelic adaptation and an adaptation for people in England have since been developed. The success of 'Knowledge is Power' highlights the importance of working alongside people with dementia to share knowledge and support their resilience.

Artificial Intelligence-Based Speech Analysis System for Medical Support.

PURPOSE: Prior research has indicated that stroke can influence the symptoms and presentation of neurogenic bladder, with various patterns emerging, including abnormal facial and linguistic characteristics. Language patterns, in particular, can be easily recognized. In this paper, we propose a platform that accurately analyzes the voices of stroke patients with neurogenic bladder, enabling early detection and prevention of the condition. METHODS: In this study, we developed an artificial intelligence-based speech analysis diagnostic system to assess the risk of stroke associated with neurogenic bladder disease in elderly individuals. The proposed method involves recording the voice of a stroke patient while they speak a specific sentence, analyzing it to extract unique feature data, and then offering a voice alarm service through a mobile application. The system processes and classifies abnormalities, and issues alarm events based on analyzed voice data. RESULTS: In order to assess the performance of the software, we first obtained the validation accuracy and training accuracy from the training data. Subsequently, we applied the analysis model by inputting both abnormal and normal data and tested the outcomes. The analysis model was evaluated by processing 30 abnormal data points and 30 normal data points in real time. The results demonstrated a high test accuracy of 98.7% for normal data and 99.6% for abnormal data. CONCLUSION: Patients with neurogenic bladder due to stroke experience long-term consequences, such as physical and cognitive impairments, even when they receive prompt medical attention and treatment. As chronic diseases become increasingly prevalent in our aging society, it is essential to investigate digital treatments for conditions like stroke that lead to significant sequelae. This artificial intelligence-based healthcare convergence medical device aims to provide patients with timely and safe medical care through mobile services, ultimately reducing national social costs.

Supporting the Diagnosis of Fabry Disease Using a Natural Language Processing-Based Approach.

In clinical practice, the consideration of non-specific symptoms of rare diseases in order to make a correct and timely diagnosis is often challenging. To support physicians, we developed a decision-support scoring system on the basis of retrospective research. Based on the literature and expert knowledge, we identified clinical features typical for Fabry disease (FD). Natural language processing (NLP) was used to evaluate patients' electronic health records (EHRs) to obtain detailed information about FD-specific patient characteristics. The NLP-determined elements, laboratory test results, and ICD-10 codes were transformed and grouped into pre-defined FD-specific clinical features that were scored in the context of their significance in the FD signs. The sum of clinical feature scores constituted the FD risk score. Then, medical records of patients with the highest FD risk score were reviewed by physicians who decided whether to refer a patient for additional tests or not. One patient who obtained a high-FD risk score was referred for DBS assay and confirmed to have FD. The presented NLP-based, decision-support scoring system achieved AUC of 0.998, which demonstrates that the applied approach enables for accurate identification of FD-suspected patients, with a high discrimination power.

Analysis of Chest X-ray for COVID-19 Diagnosis as a Use Case for an HPC-Enabled Data Analysis and Machine Learning Platform for Medical Diagnosis Support.

The COVID-19 pandemic shed light on the need for quick diagnosis tools in healthcare, leading to the development of several algorithmic models for disease detection. Though these models are relatively easy to build, their training requires a lot of data, storage, and resources, which may not be available for use by medical institutions or could be beyond the skillset of the people who most need these tools. This paper describes a data analysis and machine learning platform that takes advantage of high-performance computing infrastructure for medical diagnosis support applications. This platform is validated by re-training a previously published deep learning model (COVID-Net) on new data, where it is shown that the performance of the model is improved through large-scale hyperparameter optimisation that uncovered optimal training parameter combinations. The per-class accuracy of the model, especially for COVID-19 and pneumonia, is higher when using the tuned hyperparameters (healthy: 96.5%; pneumonia: 61.5%; COVID-19: 78.9%) as opposed to parameters chosen through traditional methods (healthy: 93.6%; pneumonia: 46.1%; COVID-19: 76.3%). Furthermore, training speed-up analysis shows a major decrease in training time as resources increase, from 207 min using 1 node to 54 min when distributed over 32 nodes, but highlights the presence of a cut-off point where the communication overhead begins to affect performance. The developed platform is intended to provide the medical field with a technical environment for developing novel portable artificial-intelligence-based tools for diagnosis support.

Artificial Intelligence-Based Patient Monitoring System for Medical Support.

PURPOSE: In this paper, we present the development of a monitoring system designed to aid in the management and prevention of conditions related to urination. The system features an artificial intelligence (AI)-based recognition technology that automatically records a user's urination activity. Additionally, we developed a technology that analyzes movements to prevent neurogenic bladder. METHODS: Our approach included the creation of AI-based recognition technology that automatically logs users' urination activities, as well as the development of technology that analyzes movements to prevent neurogenic bladder. Initially, we employed a recurrent neural network model for the urination activity recognition technology. For predicting the risk of neurogenic bladder, we utilized convolutional neural network (CNN)-based AI technology. RESULTS: The performance of the proposed system was evaluated using a study population of 30 patients with urinary tract dysfunction, who collected data over a 60-day period. The results demonstrated an average accuracy of 94.2% in recognizing urinary tract activity, thereby confirming the effectiveness of the recognition technology. Furthermore, the motion analysis technology for preventing neurogenic bladder, which also employed CNN-based AI, showed promising results with an average accuracy of 83%. CONCLUSION: In this study, we developed a urination disease monitoring system aimed at predicting and managing risks for patients with urination issues. The system is designed to support the entire care cycle of a patient by leveraging AI technology that processes various image and signal data. We anticipate that this system will evolve into digital treatment products, ultimately providing therapeutic benefits to patients.

Machine learning in the loop for tuberculosis diagnosis support.

The use of machine learning (ML) for diagnosis support has advanced in the field of health. In the present paper, the results of studying ML techniques in a tuberculosis diagnosis loop in a scenario of limited resources are presented. Data are analyzed using a tuberculosis (TB) therapy program at a health institution in a main city of a developing country using five ML models. Logistic regression, classification trees, random forest, support vector machines, and artificial neural networks are trained under physician supervision following physicians' typical daily work. The models are trained on seven main variables collected when patients arrive at the facility. Additionally, the variables applied to train the models are analyzed, and the models' advantages and limitations are discussed in the context of the automated ML techniques. The results show that artificial neural networks obtain the best results in terms of accuracy, sensitivity, and area under the receiver operating curve. These results represent an improvement over smear microscopy, which is commonly used techniques to detect TB for special cases. Findings demonstrate that ML in the TB diagnosis loop can be reinforced with available data to serve as an alternative diagnosis tool based on data processing in places where the health infrastructure is limited.

A Scoring System for Predicting Neoadjuvant Chemotherapy Response in Primary High-Grade Bone Sarcomas: A Multicenter Study.

OBJECTIVE: Currently, there is a lack of good clinical tools for evaluating the effect of chemotherapy preoperatively on primary high-grade bone sarcomas. Our goal was to investigate the predictive value of the clinical findings and establish a scoring system to predict chemotherapy response. METHODS: We conducted a retrospective multicenter cohort study and reviewed 322 patients with primary high-grade bone sarcomas. Patients who routinely received neoadjuvant chemotherapy and underwent primary tumor resection with an assessment of tumor necrosis rate (TNR) were enrolled in this study. The medical records of patients were collected from November 1, 2011, to March 1, 2018, at Peking University People's Hospital (PKUPH) and Peking University Shougang Hospital (PKUSH). The mean age of the patients was 16.2 years (range 3-52 years), of whom 65.5% were male. The clinical data collected before and after neoadjuvant chemotherapy included the degree of pain, laboratory inspection, X-ray, CT, contrast-enhanced magnetic resonance (MR), and positron emission tomography-computed tomography (PET-CT). Several machine learning models, including logistic regression, decision trees, support vector machines, and neural networks, were used to classify the chemotherapy responses. Area under the curve (AUC) of the scoring system to predict chemotherapy response is the primary outcome measure. RESULTS: For patients without events, a minimum follow-up of 24 months was achieved. The median follow-up time was 43.3 months, and it ranged from 24 to 84 months. The 5 years progression-free survival (PFS) of the included patients was 54.1%. The 5 years PFS rate was 39.7% for poor responders and 74.9% for good responders. Features such as longest diameter reduction ratio (up to three points), clear bone boundary formation (up to two points), tumor necrosis measured by magnetic resonance (up to two points), maximum standard uptake value (SUVmax ) decrease (up to three points), and significant alkaline phosphatase decrease (up to 1 point) were identified as significant predictors of good histological response and constituted the scoring system. A score ≥4 predicts a good response to chemotherapy. The scoring system based on the above factors performed well, achieving an AUC of 0.893. For nonmeasurable lesions (classified by the revised Response Evaluation Criteria in Solid Tumors [RECIST 1.1]), the AUC was 0.901. CONCLUSION: We first devised a well-performing comprehensive scoring system to predict the response to neoadjuvant chemotherapy in primary high-grade bone sarcomas.

Community-based Parent Education for Caregivers of Children Newly Diagnosed with Autism Spectrum Disorder.

Parents of children with autism spectrum disorder (ASD) frequently report high levels of stress related to the process of receiving an ASD diagnosis and navigating the intervention landscape. Parent education programs offer one approach to providing families with support, information, and resources following a child's diagnosis. Given the heterogeneity of such programs, there have been calls within the literature for increased characterization and systematic evaluation of this type of parent-focused intervention. The present study describes the structure and content of a community-based, group-format parent education program for families of children newly diagnosed with ASD. Following program participation, parents reported reductions in parenting stress, increases in knowledge and empowerment, and high levels of satisfaction. Implications and future research directions are discussed.

Health economic benefits through the use of diagnostic support systems and expert knowledge.

BACKGROUND: Rare diseases are difficult to diagnose. Due to their rarity, heterogeneity, and variability, rare diseases often result not only in extensive diagnostic tests and imaging studies, but also in unnecessary repetitions of examinations, which places a greater overall burden on the healthcare system. Diagnostic decision support systems (DDSS) optimized by rare disease experts and used early by primary care physicians and specialists are able to significantly shorten diagnostic processes. The objective of this study was to evaluate reductions in diagnostic costs incurred in rare disease cases brought about by rapid referral to an expert and diagnostic decision support systems. METHODS: Retrospectively, diagnostic costs from disease onset to diagnosis were analyzed in 78 patient cases from the outpatient clinic for rare inflammatory systemic diseases at Hannover Medical School. From the onset of the first symptoms, all diagnostic measures related to the disease were taken from the patient files and documented for each day. The basis for the health economic calculations was the Einheitlicher Bewertungsmaßstab (EBM) used in Germany for statutory health insurance, which assigns a fixed flat rate to the various medical services. For 76 cases we also calculated the cost savings that would have been achieved by the diagnosis support system Ada DX applied by an expert. RESULTS: The expert was able to achieve significant savings for patients with long courses of disease. On average, the expert needed only 27 % of the total costs incurred in the individual treatment odysseys to make the correct diagnosis. The expert also needed significantly less time and avoided unnecessary examination repetitions. If a DDSS had been applied early in the 76 cases studied, only 51-68 % of the total costs would have incurred and the diagnosis would have been made earlier. Earlier diagnosis would have significantly reduced costs. CONCLUSION: The study showed that significant savings in the diagnostic process of rare diseases can be achieved through rapid referral to an expert and the use of DDSS. Faster diagnosis not only achieves savings, but also enables the right therapy and thus an increase in the quality of life for patients.

Development of Pathological Diagnosis Support System Using Micro-computed Tomography.

In pathological diagnosis, the cutting position of pathological materials is subjectively determined by pathologists. This leads to a low cutting accuracy, which in turn may lead to incorrect diagnoses. In this study, we developed a system that supports the determination of the cutting position by visualizing and analyzing the internal structure of pathological material using micro-computed tomography (CT) before cutting. This system consists of a dedicated micro-CT and cutting support software. The micro-CT system has a fixture for fixing the target, enabling the scanning of easily deformable pathological materials. In the cutting support software, a function that interactively selects the extraction plane while displaying the volume rendering image and outputs a pseudo-histological image was implemented. Our results confirmed that the pseudo-histological image showed the fine structure inside the organ and that the latter image was highly consistent with the pathological image.

A new convolutional neural network predictive model for the automatic recognition of hypogranulated neutrophils in myelodysplastic syndromes.

BACKGROUND: Dysplastic neutrophils commonly show at least 2/3 reduction of the content of cytoplasmic granules by morphologic examination. Recognition of less granulated dysplastic neutrophils by human eyes is difficult and prone to inter-observer variability. To tackle this problem, we proposed a new deep learning model (DysplasiaNet) able to automatically recognize the presence of hypogranulated dysplastic neutrophils in peripheral blood. METHODS: Eight models were generated by varying convolutional blocks, number of layer nodes and fully connected layers. Each model was trained for 20 epochs. The five most accurate models were selected for a second stage, being trained again from scratch for 100 epochs. After training, cut-off values were calculated for a granularity score that discerns between normal and dysplastic neutrophils. Furthermore, a threshold value was obtained to quantify the minimum proportion of dysplastic neutrophils in the smear to consider that the patient might have a myelodysplastic syndrome (MDS). The final selected model was the one with the highest accuracy (95.5%). RESULTS: We performed a final proof of concept with new patients not involved in previous steps. We reported 95.5% sensitivity, 94.3% specificity, 94% precision, and a global accuracy of 94.85%. CONCLUSIONS: The primary contribution of this work is a predictive model for the automatic recognition in an objective way of hypogranulated neutrophils in peripheral blood smears. We envision the utility of the model implemented as an evaluation tool for MDS diagnosis integrated in the clinical laboratory workflow.

Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach.

BACKGROUND: Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical features and appropriate analytical approaches for enabling the early detection and effective diagnosis of patients with COVID-19. OBJECTIVE: We aimed to combine low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography (CT) imaging data, to accurately differentiate between healthy individuals, patients with COVID-19, and patients with non-COVID viral pneumonia, especially at the early stage of infection. METHODS: In this study, we recruited 214 patients with nonsevere COVID-19, 148 patients with severe COVID-19, 198 noninfected healthy participants, and 129 patients with non-COVID viral pneumonia. The participants' clinical information (ie, 23 features), lab testing results (ie, 10 features), and CT scans upon admission were acquired and used as 3 input feature modalities. To enable the late fusion of multimodal features, we constructed a deep learning model to extract a 10-feature high-level representation of CT scans. We then developed 3 machine learning models (ie, k-nearest neighbor, random forest, and support vector machine models) based on the combined 43 features from all 3 modalities to differentiate between the following 4 classes: nonsevere, severe, healthy, and viral pneumonia. RESULTS: Multimodal features provided substantial performance gain from the use of any single feature modality. All 3 machine learning models had high overall prediction accuracy (95.4%-97.7%) and high class-specific prediction accuracy (90.6%-99.9%). CONCLUSIONS: Compared to the existing binary classification benchmarks that are often focused on single-feature modality, this study's hybrid deep learning-machine learning framework provided a novel and effective breakthrough for clinical applications. Our findings, which come from a relatively large sample size, and analytical workflow will supplement and assist with clinical decision support for current COVID-19 diagnostic methods and other clinical applications with high-dimensional multimodal biomedical features.

Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention.

In this paper, we discuss hybrid decision support to monitor atrial fibrillation for stroke prevention. Hybrid decision support takes the form of human experts and machine algorithms working cooperatively on a diagnosis. The link to stroke prevention comes from the fact that patients with Atrial Fibrillation (AF) have a fivefold increased stroke risk. Early diagnosis, which leads to adequate AF treatment, can decrease the stroke risk by 66% and thereby prevent stroke. The monitoring service is based on Heart Rate (HR) measurements. The resulting signals are communicated and stored with Internet of Things (IoT) technology. A Deep Learning (DL) algorithm automatically estimates the AF probability. Based on this technology, we can offer four distinct services to healthcare providers: (1) universal access to patient data; (2) automated AF detection and alarm; (3) physician support; and (4) feedback channels. These four services create an environment where physicians can work symbiotically with machine algorithms to establish and communicate a high quality AF diagnosis.

ABC model: A tiered, integrated pathway approach to peri- and post-diagnostic support for families living with dementia (Innovative Practice).

Services for people with dementia and their families in England are commissioned with a lack of integration and an inconsistent approach creating gaps in service provision. Therefore, families affected by dementia are not receiving the appropriate care in a timely manner and often access support at crisis point. This reactive and crisis driven approach to care is costly financially and can have a negative impact and quality of life of those affected. The ABC model offers an adaptable framework that can inform service provision and improve opportunities to create seamless peri- and post-diagnosis dementia services for families affected by dementia.

A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual.

Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression, and k-Nearest Neighbor for regression and Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbor for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to consistently have higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R 2 = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.