Healthcare Informatics is a phenomenon being talked about from the early 21st century in the era in which we are living. With evolution of new computing technologies huge amount of data in healthcare is produced opening several research areas. Managing the massiveness of this data is required while extracting knowledge for decision making is the main concern of today. For this task researchers are doing explorations in big data analytics, deep learning (advanced form of machine learning known as deep neural nets), predictive analytics and various other algorithms to bring innovation in healthcare. Through all these innovations happening it is not wrong to establish that disease prediction with anticipation of its cure is no longer unrealistic. First, Dengue Fever (DF) and then Covid-19 likewise are new outbreak in infectious lethal diseases and diagnosing at all stages is crucial to decrease mortality rate. In case of Diabetes, clinicians and experts are finding challenging the timely diagnosis and analyzing the chances of developing underlying diseases. In this paper, Louvain Mani-Hierarchical Fold Learning healthcare analytics, a hybrid deep learning technique is proposed for medical diagnostics and is tested and validated using real-time dataset of 104 instances of patients with dengue fever made available by Holy Family Hospital, Pakistan and 810 instances found for infectious diseases including prognosis of; Covid-19, SARS, ARDS, Pneumocystis, Streptococcus, Chlamydophila, Klebsiella, Legionella, Lipoid, etc. on GitHub. Louvain Mani-Hierarchical Fold Learning healthcare analytics showed maximum 0.952 correlations between two clusters with Spearman when applied on 240 instances extracted from comorbidities diagnostic data model derived from 15696 endocrine records of multiple visits of 100 patients identified by a unique ID. Accuracy for induced rules is evaluated by Laplace (Fig. 8) as 0.727, 0.701 and 0.203 for 41, 18 and 24 rules, respectively. Endocrine diagnostic data is made available by Shifa International Hospital, Islamabad, Pakistan. Our results show that in future this algorithm may be tested for diagnostics on healthcare big data.
The motivation for this research comes from the gap found in discovering the common ground for medical context learning through analytics for different purposes of diagnosing, recommending, prescribing or treating patients for uniform phenotype features from patients’ profile. Authors of this paper while searching for possible solutions for medical context learning found that unified corpora tagged with medical nomenclature was missing to train the analytics for medical context learning. Therefore, here we demonstrated a mechanism to come up with uniform NER (Named Entity Recognition) tagged medical corpora that is fed with 14407 endocrine patients’ dataset in CSV format diagnosed with DM and comorbidity diseases. The other corpus is of ICD-10-CM coding scheme in text format taken from www.icd10data.com. ICD-10-CM corpus is to be tagged for understanding the medical context with uniformity for which we are conducting different experiments using common NLP techniques and frameworks like; TensorFlow, Keras, LSTM, and Bi-LSTM.
In our preliminary experiments albeit label sets in form of (instance, label) pair were tagged with Sequential() model formed on TensorFlow.Keras and Bi-LSTM NLP algorithms. The maximum accuracy achieved for model validation was 0.8846.
Millions of people die because of diabetes each year. Furthermore, most adults living with this condition are juggling with one or more other major health concerns. These related diseases also known as comorbidities, coexist with the primary disease, but also stand as their own specific disease. The challenge that healthcare professionals face is that Diabetes Mellitus (DM) is difficult to differentiate into its six forms. This hinders timely and accurate diagnosis and proper treatment. This paper presents our research in developing a novel Artificial Intelligence (AI) based approach to analyze data of real patients having different comorbidity diseases for interpretation and finding inferences for diagnosis and prognosis of DM and its comorbidities in patients in different scenarios. Details are provided about the data models used, relevant feature sets and their association rule mining, deep learning analytical models developed, and results validation against various accuracy measures. The performance of several big data analytics platforms was validated for the different models for three different sizes of endocrine datasets with varying parameters. The data models were mapped to HL7 FHIR v4 schema that is flexible in adapting to diagnostic models for all diseases. Out of several analytical models evaluated, Louvain Mani-Hierarchical Fold Learning (LMHFL) was found to be the most promising in terms of efficiency and accurate explainable diagnosis through reflective visualizations of associated features.
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