A Data-Driven Approach for Analyzing Healthcare Services Extracted from Clinical Records

Cancer remains one of the major public health challenges worldwide. After cardiovascular diseases, cancer is one of the first causes of death and morbidity in Europe, with more than 4 million new cases and 1.9 million deaths per year. The suboptimal management of cancer patients during treatment and subsequent follows up are major obstacles in achieving better outcomes of the patients and especially regarding cost and quality of life In this paper, we present an initial data-driven approach to analyze the resources and services that are used more frequently by lung-cancer patients with the aim of identifying where the care process can be improved by paying a special attention on services before diagnosis to being able to identify possible lung-cancer patients before they are diagnosed and by reducing the length of stay in the hospital. Our approach has been built by analyzing the clinical notes of those oncological patients to extract this information and their relationships with other variables of the patient. Although the approach shown in this manuscript is very preliminary, it shows that quite interesting outcomes can be derived from further analysis. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Artificial Intelligence-Based Triage for Patients with Acute Abdominal Pain in emergency Department; a Diagnostic Accuracy Study

Introduction: Artificial intelligence (AI) is the development of computer systems which are capable of doing human intelligence tasks such as decision making and problem solving. AI-based tools have been used for predicting various factors in medicine including risk stratification, diagnosis and choice of treatment. AI can also be of considerable help in emergency departments, especially patients’ triage. Objective: This study was undertaken to evaluate the application of AI in patients presenting with acute abdominal pain to estimate emergency severity index version 4 (ESI-4) score without the estimate of the required resources. Methods: A mixed-model approach was used for predicting the ESI-4 score. Seventy percent of the patient cases were used for training the models and the remaining 30% for testing the accuracy of the models. During the training phase, patients were randomly selected and were given to systems for analysis. The output, which was the level of triage, was compared with the gold standard (emergency medicine physician). During the test phase of the study, another group of randomly selected patients were evaluated by the systems and the results were then compared with the gold standard. Results: Totally, 215 patients who were triaged by the emergency medicine specialist were enrolled in the study. Triage Levels 1 and 5 were omitted due to low number of cases. In triage Level 2, all systems showed fair level of prediction with Neural Network being the highest. In Level 3, all systems again showed fair level of prediction. However, in triage Level 4, decision tree was the only system with fair prediction. Conclusion: The application of AI in triage of patients with acute abdominal pain resulted in a model with acceptable level of accuracy. The model works with optimized number of input variables for quick assessment

Predictive model for acute myocardial infarction in working-age population: a machine learning approach

Cardiovascular diseases are the leading cause of mortality in Latin America, particularly acute myocardial infarction (AMI), which is the primary cause of atherosclerotic cardiovascular morbidity. This study aims to develop a predictive model for the probability of AMI occurrence in the working-age population, based on atherogenic indices, paraclinical variables, and anthropometric measures. The research conducted a cross-sectional study involving 427 workers aged 40 years or older in Popayán, Colombia. Out of this population, 202 individuals were screened with a 95% confidence interval and a 5% error margin. Epidemiological, anthropometric, and paraclinical data were collected. A binary logistic regression model was employed to identify variables directly associated with the probability of AMI. Predictive classification models were generated using statistical software JASP and the programming language Python. During the training stage, JASP produced a model with an accuracy of 87.5%, while Python generated a model with an accuracy of 90.2%. In the validation stage, JASP achieved an accuracy of 93%, and Python reached 95%. These results establish an effective model for predicting the probability of AMI in the working population

Random survival forests for predicting the bed occupancy in the intensive care unit

Predicting the bed occupancy of an intensive care unit (ICU) is a daunting task. The uncertainty associated with the prognosis of critically ill patients and the random arrival of new patients can lead to capacity problems and the need for reactive measures. In this paper, we work towards a predictive model based on Random Survival Forests which can assist physicians in estimating the bed occupancy. As input data, we make use of the Sequential Organ Failure Assessment (SOFA) score collected and calculated from 4098 patients at two ICU units of Ghent University Hospital over a time period of four years. We compare the performance of our system with a baseline performance and a standard Random Forest regression approach. Our results indicate that Random Survival Forests can effectively be used to assist in the occupancy prediction problem. Furthermore, we show that a group based approach, such as Random Survival Forests, performs better compared to a setting in which the length of stay of a patient is individually assessed

Personalized Clinical Treatment Selection Using Genetic Algorithm and Analytic Hierarchy Process

The development of Machine Learning methods and approaches offers enormous growth opportunities in the Healthcare field. One of the most exciting challenges in this field is the automation of clinical treatment selection for patient state optimization. Using necessary medical data and the application of Machine Learning methods (like the Genetic Algorithm and the Analytic Hierarchy Process) provides a solution to such a challenge. Research presented in this paper gives the general approach to solve the clinical treatment selection task, which can be used for any type of disease. The distinguishing feature of this approach is that clinical treatment is tailored to the patient's initial state, thus making treatment personalized. The article also presents a comparison of the different classification methods used to model patient indicators after treatment. Additionally, special attention was paid to the possibilities and potential of using the developed approach in real Healthcare challenges and tasks

Effect of oral pathogens on intrahospital mortality in patients surgically treated for infective endocarditis

Infektivni endokarditis (IE) upalna je bolest srčanih zalistaka ili septalnih defekata. Pojavnost IE-a je oko 5 slučajeva na 100 000 ljudi, a stopa smrtnosti iznosi oko 20%. Najčešće je uzrokovan bakterijama ili gljivama. Od bakterija najčešće su izolirani stafilokoki ili streptokoki. Liječi se konzervativno ili kirurški. Do IE-a koji je uzrokovan oralnim patogenima obično dolazi zbog translokacije bakterije iz usne šupljine u krvotok kao posljedice oštećenja parodontalne barijere u sklopu bolesti parodonta ili nakon provedenih dentalnih zahvata. Bolesti parodonta povezane su sa sustavnim upalnim odgovorom koji dovodi do povećanog afiniteta za razvoj kardiovaskularnih bolesti, a dokazano je da kod kardijalnih bolesnika dentalni zahvati u prijeoperacijskom razdoblju dovode do povećanja morbiditeta i mortaliteta u perioperacijskom razdoblju. Svrha ovog istraživanja je dokazati postoji li povezanost između IE-a koji je uzrokovan oralnim patogenima s unutarbolničkim mortalitetom kod bolesnika operiranih zbog IE-a. U istraživanje je uvršteno 65 bolesnika operiranih zbog IE-a u kliničkoj ustanovi u razdoblju od 3 godine. Bolesnici su podijeljeni u skupinu bolesnika kojima je IE uzrokovan oralnim patogenima i u skupinu u kojoj je uzrokovan ostalim patogenima. Uspoređivani su stopa smrtnosti, trajanje mehaničke ventilacije, razlika u prijeoperacijskom SOFA zbroju, trajanje boravka u JIM-u, razlika u bilanci tekućine tijekom boravka u JIM-u te razlika u dinamici promjene Carricova indeksa tijekom prva 24 h boravka u JIM-u između skupina. 25 bolesnika (38%) imalo je IE uzrokovan oralnim patogenima. Nije dokazana statistički značajna razlika u stopi smrtnosti između skupina, ali je dokazana statistički značajno niža vrijednost SOFA zbroja (4 vs 7,5, p < 0,001), kraće trajanje mehaničke ventilacije (16 h vs 18,5 h, p=0,028), kraći boravak u JIM-u (44 h vs 67,5 h, p=0,02), prisutan anamnestički dodatak o provedenom dentalnom zahvatu unutar 60 dana prije operacije (32% vs 10%, p=0,026) te nalaz pozitivnog brisa zubnog plaka (64% vs 5%, p < 0,001) kod bolesnika kojima je IE uzrokovan oralnim patogenima. Nije dokazana statistički značajna razlika u stopi korištenja bubrežnog nadomjesnog liječenja, kao niti u dinamici promjena Carricova indeksa tijekom prva 24 h nakon operacije.Infective endocarditis (IE) is an inflammatory disease of cardiac valves or septal defectscaused by bacteria or fungi. Incidence of IE is around 3 - 7 per 100 000 cases with in-hospital mortality ranging between 13% and 25%. Although transient bacteraemia is common, IE is not that common because intact endothelium is usually resistant to formation of microbial colonies. That is the reason why IE is mostly present on left-sided valves (mitral and aortic) which are exposed to increased stress caused by higher blood pressures in systemic circulation. Clinical features of IE are persistent fever, malaise, skin lesions, hemodynamic instability and dyspnea. Modified Duke criteria are the golden standard for diagnosis of IE. Two major (echocardiographic manifestation and positive blood cultures), 1 major and 3 minor or 5 minor (pre-existing cardiac conditions, fever, vascular phenomena, immunological phenomena and positive blood cultures) criteria need to be present to confirm the diagnosis of IE. IE can be caused by bacteria originating from oral cavity, mostly streptococci, but most often it is caused by staphylococci. Oral pathogens as causes of IE are usually present in the bloodstream after invasive dental procedures, but their release can also be triggered by routine dental activities such as using dental floss, especially in patients with poor levels of dental hygiene. Poor levels of dental hygiene have been linked with higher incidence of periodontal disease which can lead to higher affinity to development of atherosclerotic disease, as well as increased mortality and rate of complications after cardiac surgery. Aims: Aim of this prospective observational study is to assess whether oral pathogens as cause of IE in patients surgically treated for IE are linked to increase in in-hospital mortality rate compared to patients who had IE caused by pathogens which are not of oral origin. Length of stay in the intensive care unit (ICU), duration of mechanical ventilation, rate of ICU re-admissions and surgical revisions were assessed, as well as differences of cumulative fluid balance, need for renal replacement therapy and PaO2/FiO2 indices measured at ICU admission, 3, 6, 12 and 24 hours post admission were compared between groups. Quantitative values were also compared between survivors and non-survivors. Primary hypothesis of this research is that patients who were surgically treated for IE caused by oral pathogens will have higher in-hospital mortality rate compared to patients who had IE caused by other pathogens. Patients and methods: Following the approval of institutional ethics board, 65 patients surgically treated for native valve IE were included in this research. Patients who have had valvular surgery earlier in their lifetime, as well as patients with acute pneumonia or chronic lung disease were excluded. Demographical data, aerobic and anaerobic blood culture results, microbiology analysis of excised valve, laboratory data and clinical parameters needed to assess preoperative SOFA score and other measured variables were analysed from medical documentation. Dental plaque was sampled at ICU admission and microbiologically analysed where applicable. PaO2/FiO2 ratios at 0, 3, 6, 12 and 24h after ICU admission were calculated using blood gas analysis values sampled from radial or femoral artery. Dental procedure anamnestic data was collected from medical documentation and from patients or their families. All patient related data was coded to preserve patient anonymity. After data collection, statistical analysis using StatsDirect (StatsDirect Ltd, Altrincham, UK) v3.0.187 and jamovi v0.8.1.11. (www.jamovi.org) software was performed to compare measured data between groups. Results: 25 patients had IE caused by oral pathogens and 40 patients had IE caused by other pathogens. Primary hypothesis that in-hospital mortality will be higher in patients surgically treated for IE caused by oral pathogens was disproven. There was no statistically significant difference between groups. There was also no statistically significant difference between groups regarding valve involvement (aortic, mitral or tricuspid). However, patients who had IE caused by oral pathogens had significantly higher incidence of positive plaque swab cultures (64% vs 5%, p<0,001) and dental procedures 60 days preceding surgery (32% vs 10%, p=0,026). These patients also had lower SOFA scores (4 vs 7,5, p < 0,001) before surgery, as well as shorter length of ICU stay (44 h vs 67,5h, p=0,02) and shorter duration of mechanical ventilation(16h vs 18,5h, p=0,028). There was no statistically significant difference in rates of reintubation and ICU readmission between groups. There was also no difference in dynamics of changes in PaO2/FiO2 ratio at 0, 3, 6, 12 and 24 hours after ICU admission between groups. Survivors had statistically significant lower SOFA scores preoperatively, shorter duration of ICU stay, as well as higher PaO2/FiO2 ratio at 24h post ICU admission. Lower SOFA scores had predictive values for length of ICU stay and duration of mechanical ventilation. Conclusion: Although the primary hypothesis of this research was disproved, results such as incidence of IE which occurred in patients who had dental procedures performed 60 days before surgery show that IE prophylaxis for dental procedures is still not adequately enforced. Due to the fact that patients who were included in this research had native valve IE, and as such were not the population which is routinely prophylactically treated with antimicrobial drugs, further prospective multicentre trials are needed to determine the optimal prophylactic treatment for this preventable disease. Also, these results show that the course of disease is not as severe in patients who had IE caused by oral pathogens, and that lower preoperative SOFA scores are a good prognostic factor for length of ICU stay and duration of mechanical ventilation