Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models

Tabular data is often hidden in text, particularly in medical diagnostic reports. Traditional machine learning (ML) models designed to work with tabular data, cannot effectively process information in such form. On the other hand, large language models (LLMs) which excel at textual tasks, are probably not the best tool for modeling tabular data. Therefore, we propose a novel, simple, and effective methodology for extracting structured tabular data from textual medical reports, called TEMED-LLM. Drawing upon the reasoning capabilities of LLMs, TEMED-LLM goes beyond traditional extraction techniques, accurately inferring tabular features, even when their names are not explicitly mentioned in the text. This is achieved by combining domain-specific reasoning guidelines with a proposed data validation and reasoning correction feedback loop. By applying interpretable ML models such as decision trees and logistic regression over the extracted and validated data, we obtain end-to-end interpretable predictions. We demonstrate that our approach significantly outperforms state-of-the-art text classification models in medical diagnostics. Given its predictive performance, simplicity, and interpretability, TEMED-LLM underscores the potential of leveraging LLMs to improve the performance and trustworthiness of ML models in medical applications

Theopolis Monk: Envisioning a Future of A.I. Public Service

Visions of future applications of artificial intelligence tend to veer toward the naively optimistic or frighteningly dystopian, neglecting the numerous human factors necessarily involved in the design, deployment and oversight of such systems. The dream that AI systems may somehow replace the irregularities and struggles of human governance with unbiased efficiency is seen to be non-scientific and akin to a religious hope, whereas the current trajectory of AI development indicates that it will increasingly serve as a tool by which humans exercise control over other humans. To facilitate the responsible development of AI systems for the public good, we discuss current conversations on the topics of transparency and accountability

Selection of the best consultant for SAP ERP project using combined AHP-IBA approach

In this paper we propose a combined AHP-IBA model for selecting the best SAP consultant for an SAP ERP project. The goal of the SAP Project Manager is to choose the best consultant, the one who is able to implement standard SAP functionalities with quality and on time. When making a decision on the basis of multiple criteria, the traditional Analytic Hierarchy Process (AHP) method does not take into account the fact that attributes may correlate, assuming that there are no dependencies between them. However, the dependencies of the attributes can often be used to model important knowledge for multiple criteria decision analysis. We propose an extension to the traditional AHP method by applying Interpolative realization of Boolean algebra (IBA), using AHP to determine the criteria weights, and IBA to model the logical interactions among criteria. The research conducted on ERP consultant selection suggests that the decision making process is modelled more accurately if logical interactions between attributes are modelled before applying AHP

Decision Support Models and Applications

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An FAHP-TOPSIS framework for analysis of the employee productivity in the Serbian electrical power companies

The aim of this paper is to apply an integrated model, which combines methods of classical and fuzzy Multi-criteria decision making (MCDM) in selected six large equity companies from the Serbian energy sector. The data considered are retrieved from the official financial statements. Four main criteria were analyzed, identified by the previous researchers and pointing to the employees productivity: Operating income/Number of employees, Equity/Number of employees, Net income/Number of employees and Total assets/Number of employees. The contribution of this paper lies in the application of a hybrid model that integrates two MCDM methods: Fuzzy Analytic Hierarchy Process (FAHP) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) to analyse the employee productivity in selected D-Electrical power supply companies operating in Serbia. The FAHP is an effective method for mathematical representation of uncertain and imprecise evaluations made by humans, while the TOPSIS method is an efficient way to rank the alternatives. Results show that operating income is of highest importance for estimating employee productivity and decision making, while equity is of the weakest. Furthermore, the most productive operations in large enterprises from selected companies of the sector D-Electrical power supply are found in the company PC EPS Beograd, and the lowest are in the ED Center llc Kragujevac

Towards a Collaborative Platform for Advanced Meta-Learning in Health care Predictive Analytics

Modern medical research and clinical practice are more dependent than ever on multi-factorial data sets originating from various sources, such as medical imaging, DNA analysis, patient health records and contextual factors. This data drives research, facilitates correct diagnoses and ultimately helps to develop and select the appropriate treatments. The volume and impact of this data has increased tremendously through technological developments such as highthroughput genomics and high-resolution medical imaging techniques. Additionally, the availability and popularity of different wearable health care devices has allowed the collection and monitoring of fine-grained personal health care data. The fusion and combination of these heterogeneous data sources has already led to many breakthroughs in health research and shows high potential for the development of methods that will push current reactive practices towards predictive, personalized and preventive health care. This potential is recognized and has led to the development of many platforms for the collection and statistical analysis of health care data (e.g. Apple Health, Microsoft Health Vault, Oracle Health Management, Philips HealthSuite, and EMC Health care Analytics). However, the heterogeneity of the data, privacy concerns, and the complexity and multiplicity of health care processes (e.g. diagnoses, therapy control, and risk prediction) creates significant challenges for data fusion, algorithm selection and tuning. These challenges leave a gap between the actual and the potential data usage in health care, which prevents a paradigm shift from delayed generalized medicine to predictive personalized medicine In this work we present an extensions of the OpenML platform that will be addressed in our future work in order to meet the needs of meta-learning in health care predictive analytics: privacy preserving sharing of data, workflows and evaluations, reproducibility of the results, and rich meta-data spaces about both data and algorithms. OpenML.org [2] is a collaboration platform which is designed to organize datasets, machine learning workflows, models and their evaluations. Currently, OpenML is not fully distributed but can be installed on local instances which can communicate with the main OpenML database using mirroring techniques. The downside of this approach is that code (machine learning workflows), datasets, experiments (models and evaluations) are physically kept on local instances, so users cannot communicate and share. We plan to turn OpenML into a fully distributed machine learning platform, which will be accessible from different data mining and machine learning platforms such as RapidMiner, R, WEKA, KNIME, or similar. Such a distributed platform would allow the ease of sharing data and knowledge. Currently, regulations and privacy concerns often prevent hospitals to learn from each other's approaches (e.g. machine learning workflows), reproduce work done by others (data version control, preprocessing and statistical analysis), and build models collaboratively. On the other hand, meta-data such as type of the hospital, percentage of readmitted patients or indicator of emergency treatment, as well as the learned models and their evaluations can be shared and have great potential for the development of a cutting edge meta-learning system for ranking, selection and tuning of machine learning algorithms. The success of meta-learning systems is greatly influenced by the size of problem (data) and algorithm spaces, but also by the quality of the data and algorithm descriptions (meta-features). Thus, we plan to employ domain knowledge provided by expert and formal sources (e.g. ontologies) in order to extend the meta-feature space for meta-learning in health care applications. For example, in meta-analyses of gene expression microarray data, the type of chip is very important in predicting algorithm performance. Further, in fused data sources it would be useful to know which type of data contributed to the performance (electronic health records, laboratory tests, data from wearables etc.). In contrast to data descriptions, algorithm descriptions are much less analyzed and applied in the meta-learning process. Recent result

The predictive value of microbiological findings on teeth, internal and external implant portions in clinical decision making

International audienceAim: The primary aim of this study was to evaluate 23 pathogens associated with peri-implantitis at inner part of implant connections, in peri-implant and periodontal pockets between patients suffering peri-implantitis and participants with healthy peri-implant tissues; the secondary aim was to estimate the predictive value of microbiological profile in patients wearing dental implants using data mining methods.Material and Methods: Fifty participants included in the present case─control study were scheduled for collection of plaque samples from the peri-implant pockets, internal connection, and periodontal pocket. Real-time polymerase chain reaction was performed to quantify 23 pathogens. Three predictive models were developed using C4.5 decision trees to estimate the predictive value of microbiological profile between three experimental sites.Results: The final sample included 47 patients (22 healthy controls and 25 diseased cases), 90 implants (43 with healthy peri-implant tissues and 47 affected by peri-implantitis). Total and mean pathogen counts at inner portions of the implant connection, in peri-implant and periodontal pockets were generally increased in peri-implantitis patients when compared to healthy controls. The inner portion of the implant connection, the periodontal pocket and peri-implant pocket, respectively, presented a predictive value of microbiologic profile of 82.78%, 94.31%, and 97.5% of accuracy.Conclusion: This study showed that microbiological profile at all three experimental sites is differently characterized between patients suffering peri-implantitis and healthy controls. Data mining analysis identified Parvimonas micra as a highly accurate predictor of peri-implantitis when present in peri-implant pocket while this method generally seems to be promising for diagnosis of such complex infections