Constructing Ontology-Based Cancer Treatment Decision Support System with Case-Based Reasoning

Decision support is a probabilistic and quantitative method designed for modeling problems in situations with ambiguity. Computer technology can be employed to provide clinical decision support and treatment recommendations. The problem of natural language applications is that they lack formality and the interpretation is not consistent. Conversely, ontologies can capture the intended meaning and specify modeling primitives. Disease Ontology (DO) that pertains to cancer's clinical stages and their corresponding information components is utilized to improve the reasoning ability of a decision support system (DSS). The proposed DSS uses Case-Based Reasoning (CBR) to consider disease manifestations and provides physicians with treatment solutions from similar previous cases for reference. The proposed DSS supports natural language processing (NLP) queries. The DSS obtained 84.63% accuracy in disease classification with the help of the ontology

Developing An Expert System Framework for Supporting Diagnosis and Treatment of Dyspepsia and Gastric Cancer Disease Using Local Language

Dyspepsia is a pain of the upper abdominal and it has the problem of impaired digestion like abdominal disease or other abdominal disease, which has the symptoms of heartburn, nausea, and belching, upper abdominal fullness [1]. It also related to the problem of indigestion for a group of symptoms that cause pain in the abdomen, which affects at least 25% of the world population every year [2].  From related disease of dyspepsia, Gastric cancer is the stomach cancer that develops from the lining of the stomach that affects the cell of digestive system and it is the third leading cause of death worldwide [3]. Both dyspepsia and gastric cancer is diseases that affect gastrointestinal part of human body. Therefore, this type of disease requires timely diagnosis and treatment; otherwise it can cause death and other chronic diseases. In developing countries like Ethiopia, treatment option for dyspepsia and gastric cancer is not readily available which support medical professional and also there is a scarcity of medical professional, to address such medical problems a medical expert system can play a significant role, consequently, the main objective of this research study is to develop an expert system framework for supporting diagnosis and treatment of dyspepsia and gastric cancer using local language (Amharic language). To develop this medical expert system, knowledge was acquired using both structured and unstructured interview from domain expert which are selected using purposive sampling techniques from Arba Minch General Hospital, and from document analysis. Domain knowledge is modeled using decision tree and rule-based knowledge representation was used. This medical expert system is developed by using backward chaining to infer the rule and provide an appropriate diagnosis. Finally, the performance of the system was evaluated by preparing 15 test cases by provided to domain experts and for user acceptance test, users evaluate the system through nine criteria prepared by the researcher and the system has scored 80% system performance and 85.2% user acceptance this result shows that the study has a promising result that achieves the objective of the study. The researchers recommended that to apply data mining techniques and to extract the hidden knowledge. Keywords: Expert System, Dyspepsia and Gastric Cancer, Diagnosis, and Treatment. DOI: 10.7176/CEIS/12-1-03 Publication date: January 31st 202

Common human diseases prediction using machine learning based on survey data

In this era, the moment has arrived to move away from disease as the primary emphasis of medical treatment. Although impressive, the multiple techniques that have been developed to detect the diseases. In this time, there are some types of diseases COVID-19, normal flue, migraine, lung disease, heart disease, kidney disease, diabetics, stomach disease, gastric, bone disease, autism are the very common diseases. In this analysis, we analyze disease symptoms and have done disease predictions based on their symptoms. We studied a range of symptoms and took a survey from people in order to complete the task. Several classification algorithms have been employed to train the model. Furthermore, performance evaluation matrices are used to measure the model's performance. Finally, we discovered that the part classifier surpasses the others.Comment: 11 pages, 6 figures, accepted in Bulletin of Electrical Engineering and Informatics Journa

On the road to personalised and precision geomedicine: medical geology and a renewed call for interdisciplinarity

Our health depends on where we currently live, as well as on where we have lived in the past and for how long in each place. An individual’s place history is particularly relevant in conditions with long latency between exposures and clinical manifestations, as is the case in many types of cancer and chronic conditions. A patient’s geographic history should routinely be considered by physicians when diagnosing and treating individual patients. It can provide useful contextual environmental information (and the corresponding health risks) about the patient, and should thus form an essential part of every electronic patient/health record. Medical geology investigations, in their attempt to document the complex relationships between the environment and human health, typically involve a multitude of disciplines and expertise. Arguably, the spatial component is the one factor that ties in all these disciplines together in medical geology studies. In a general sense, epidemiology, statistical genetics, geoscience, geomedical engineering and public and environmental health informatics tend to study data in terms of populations, whereas medicine (including personalised and precision geomedicine, and lifestyle medicine), genetics, genomics, toxicology and biomedical/health informatics more likely work on individuals or some individual mechanism describing disease. This article introduces with examples the core concepts of medical geology and geomedicine. The ultimate goals of prediction, prevention and personalised treatment in the case of geology-dependent disease can only be realised through an intensive multiple-disciplinary approach, where the various relevant disciplines collaborate together and complement each other in additive (multidisciplinary), interactive (interdisciplinary) and holistic (transdisciplinary and cross-disciplinary) manners

propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans

**Background:** Accurate 3D CT scan segmentation of gastric tumors is pivotal for diagnosis and treatment. The challenges lie in the irregular shapes, blurred boundaries of tumors, and the inefficiency of existing methods. **Purpose:** We conducted a study to introduce a model, utilizing human-guided knowledge and unique modules, to address the challenges of 3D tumor segmentation. **Methods:** We developed the PropNet framework, propagating radiologists' knowledge from 2D annotations to the entire 3D space. This model consists of a proposing stage for coarse segmentation and a refining stage for improved segmentation, using two-way branches for enhanced performance and an up-down strategy for efficiency. **Results:** With 98 patient scans for training and 30 for validation, our method achieves a significant agreement with manual annotation (Dice of 0.803) and improves efficiency. The performance is comparable in different scenarios and with various radiologists' annotations (Dice between 0.785 and 0.803). Moreover, the model shows improved prognostic prediction performance (C-index of 0.620 vs. 0.576) on an independent validation set of 42 patients with advanced gastric cancer. **Conclusions:** Our model generates accurate tumor segmentation efficiently and stably, improving prognostic performance and reducing high-throughput image reading workload. This model can accelerate the quantitative analysis of gastric tumors and enhance downstream task performance

Attitudes of surgeons to the use of postoperative markers of the systemic inflammatory response following elective surgery

Background: Cancer is responsible for 7.6 million deaths worldwide and surgery is the primary modality of a curative outcome. Postoperative care is of considerable importance and it is against this backdrop that a questionnaire based study assessing the attitudes of surgeons to monitoring postoperative systemic inflammation was carried out. Method: A Web based survey including 10 questions on the “attitudes of surgeons to the use of postoperative markers of the systemic inflammatory response following elective surgery” was distributed via email. Two cohorts were approached to participate in the survey. Cohort 1 consisted of 1092 surgeons on the “Association of Coloproctology of Great Britain and Ireland (ACPGBI)” membership list. Cohort 2 consisted of 270 surgeons who had published in this field in the past as identified by two recent reviews. A reminder email was sent out 21 days after the initial email in both cases and the survey was closed after 42 days in both cases. Result: In total 29 surgeons (2.7%) from cohort 1 and 40 surgeons (14.8%) from cohort 2 responded to the survey. The majority of responders were from Europe (77%), were colorectal specialists (64%) and were consultants (84%) and worked in teaching hospitals (54%) and used minimally invasive techniques (87%). The majority of responders measured CRP routinely in the post-operative period (85%) and used CRP to guide their decision making (91%) and believed that CRP monitoring should be incorporated into postoperative guidelines (81%). Conclusion: Although there was a limited response the majority of surgeons surveyed measure the systemic inflammatory response following elective surgery and use CRP measurements together with clinical findings to guide postoperative care. The present results provide a baseline against which future surveys can be compared