Mathematical modelling of doctors' perceptions in the diagnosis of depression: a novel approach

Chattopadhyay, Subhagata

doi:10.1504/ijbet.2013.053702

Cited by 9 publications

(3 citation statements)

References 37 publications

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“…In other words, no work has been reported that has attempted to quantify and then translate the ‘clinical eye’ of a human physician into data‐validated diagnosis. The analytics within the proposed tool (VIRDOCD) is based on experience‐based weighing of the sign‐symptoms, then combining them towards a cumulative outcome as a probabilistic conclusion, that we call as the ‘medical diagnosis’ (Chattopadhay, 2013). The follow‐up therapeutic routine is based on the correctness of the diagnosis.…”

Section: Discussionmentioning

confidence: 99%

VIRDOCD: A VIRtual DOCtor to predict dengue fatality

Chattopadhyay

Chattopadhyay²

2021

Expert Systems

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Clinicians make routine diagnosis by scrutinizing patients' medical signs and symptoms, a skill popularly referred to as 'Clinical Eye'. This skill evolves through trialand-error and improves with time. The success of the therapeutic regime relies largely on the accuracy of interpretation of such sign-symptoms, analysing which a clinician assesses the severity of the illness. The present study is an attempt to propose a complementary medical front by mathematically modelling the 'Clinical Eye' of a VIRtual DOCtor, using statistical and machine intelligence tools (SMI), to analyse Dengue epidemic infected patients (100 case studies with 11 weighted sign-symptoms).The SMI in VIRDOCD reads medical data and translates these into a vector comprising multiple linear regression (MLR) coefficients to predict infection severity grades of dengue patients that clone the clinician's experience-based assessment. Risk managed through ANOVA, the dengue severity grade prediction accuracy from VIRDOCD is found higher (ca 75%) than conventional clinical practice (ca 71.4%, mean accuracy profile assessed by a team of 10 senior consultants). Free of human errors and capable of deciphering even minute differences from almost identical symptoms (to the Clinical eye), VIRDOCD is uniquely individualized in its decisionmaking ability. The algorithm has been validated against Random Forest classification (RF, ca 63%), another regression-based classifier similar to MLR that can be trained through supervised learning. We find that MLR-based VIRDOCD is superior to RF in predicting the grade of Dengue morbidity. VIRDOCD can be further extended to analyse other epidemic infections, such as COVID-19.

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Section: Discussionmentioning

confidence: 99%

VIRDOCD: A VIRtual DOCtor to predict dengue fatality

Chattopadhyay

Chattopadhyay²

2021

Expert Systems

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“…Thus, in this work, a set of synthetic data (N = 54) is prepared for the study. Three experienced medical doctors put their knowledge into creating it [15]. The aim is to mimic the way doctors practically diagnose and grade a disease using minimal but significant features or parameters obtained from patients' history, examinations, and clinical interpretations (together called the 'clinical eye') rather than including the whole set of features, which is mimicked in this hybrid MLMI model [16].…”

Section: Data Preparationmentioning

confidence: 99%

MLMI: A Machine Learning Model for Estimating Risk of Myocardial Infarction

Chattopadhyay

2024

Artificial Intelligence Evolution

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Cardiovascular diseases (CVD) are a global threat of high morbidity and mortality. Myocardial infarction (MI) due to coronary vessel malfunctions is one of the leading causes of mortality due to CVD. Interestingly, all CVD patients do not develop MI, and vice versa. Clinically, thus, it is a gray area. Therefore, an appropriate MI risk scoring (MIRS) tool could be useful to identify the high-risk (HR) population suffering from CVD. This research paper presents a hybrid machine learning (ML) model (MLMI) to identify MI risk where a) clustering of the CVD population with the help of the Gaussian mixture model (GMM) is used to identify the HR and not high-risk (NHR) groups, b) feature engineering of the members in both the HR and NHR populations using regression method that estimates the coefficient of determination (R2) to explore significant features to create the model by c) leveraging the R2 values > 0.7 as the key features of the input dataset to a d) Feed-forward neural network (FFNN) for scoring the risk on a set of synthetic patient data, created by three experienced medical doctors. The myocardial infarction risk scores (MIRS) would assist users in prioritizing the patients needing monitoring and treatment. Finally, the MIRS values are validated by another group of three medical doctors to curb the research bias. The sensitivity, specificity, precision, F1 scores, and accuracy of the MLMI model are computed to measure its efficiency. With limited input data, the proposed model shows an average accuracy, and precision of 77.33% each, while sensitivity and F1 score are 100% and 88%, respectively.

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“…Typhoid fever is a classic example of a systemic infectious disease that often leads to death if not treated at its early stage. Hence, DDSS tools can be proposed to be the 'assistive tools' to the healthcare staff (e.g., novice doctors and even nurses) in preventing further worsening the morbidity and mortality in the population at risk [11]. It is also important to mention here that, empirically used antibiotics in erratic doses, i.e., wrong strength and duration, antibiotics salad as poly-pharmacy popularly known as 'PolyRx' are the leading causes of multi-drug resistant (MDR) Typhoid, especially in the countries where antibiotics are available 'over the counter' and the number of clinicians is less compared to the size of the population as seen in the developing nations.…”

Section: Introductionmentioning

confidence: 99%

Decoding Medical Diagnosis with Machine Learning Classifiers

Chattopadhyay

2024

MEDIN

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Clinical Decision Support Systems (CDSS) are gaining popularity in disease screening and grading in the current era of digital healthcare. This paper attempts to model how a computer learns to grade an Infectious Disease (ID), e.g., Typhoid fever using the Machine Learning (ML)-based approach mimicking how a novice doctor learns to diagnose a case with the help of senior doctors. To achieve the goal, ten Virtual Junior Clinicians (VJCs) are developed using ten Machine Learning Classifiers (MLC)-based CDSS, which are then trained with 'weighted' [0, 1] sign-symptoms and the corresponding 'labeled'grade of synthetic Typhoid fever cases (N = 198). Weights and labels are assigned by ten Senior Clinicians (SC) providing their rich Clinical Knowledge Base (CKB). The performance of each VJC is then measured in terms of their diagnostic Accuracy, Precision, Recall, and F-score. Results show that Random Forest (RF, i.e., VJC9) and Decision Tree (DT, i.e., VJC4)-based CDSS can grade with an average of 87% accuracy, which is even higher than human clinicians' accuracy. The reason behind RF and DT's appreciable performance is that clinicians use tree-search-based methods with probabilistic 'yes' and 'no' logic to learn the disease patterns alike the working principles of DT and RF for diagnosing and grading any ID. Apart from modeling, the paper provides insight into how to select the right Machine Learning Classifier (MLC) algorithm in the field of ID diagnosis. It also throws light on various hardships and challenges with MLC-based CDSS implementations in the real-world scenario.

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Mathematical modelling of doctors' perceptions in the diagnosis of depression: a novel approach

Cited by 9 publications

References 37 publications

VIRDOCD: A VIRtual DOCtor to predict dengue fatality

VIRDOCD: A VIRtual DOCtor to predict dengue fatality

MLMI: A Machine Learning Model for Estimating Risk of Myocardial Infarction

Decoding Medical Diagnosis with Machine Learning Classifiers

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