Yleiskäyttöinen tekstinluokittelija suomenkielisille potilaskertomusteksteille

Medical texts are an underused source of data in clinical analytics. Extracting the relevant information from unstructured texts is difficult and while there are some tools available, they are often targeted for English texts. The situation is worse for smaller languages, such as Finnish. In this work, we reviewed literature in text mining and natural language processing fields in the scope of analyzing medical texts. Using the results of our literature review, we created an algorithm for information extraction from patient record texts. During this thesis work we created a decent text mining tool that works through text classification. This algorithm can be used detect medical conditions solely from medical texts. The usage of the algorithm is limited through the availability of large training data.Potilaskertomustekstejä käytetään kliinisessä analytiikassa huomattavan vähäisessä määrin. Olennaisen tiedon poimiminen tekstin joukosta on vaikeaa, ja vaikka siihen on työkaluja saatavilla, ovat ne useimmiten tehty englanninkielisille teksteille. Pienempien kielten, kuten suomen kohdalla tilanne on heikompi. Tässä työssä tehtiin kirjallisuuskatsaus tekstinlouhintaan ja luonnollisen kielen käsittelyyn liittyvään kirjallisuuteen, keskittyen erityisesti menetelmiin jotka soveltuvat lääketieteellisten tekstien analysointiin. Kirjallisuuskatsauksen pohjalta loimme algoritmin, joka soveltuu yleisesti lääketieteellisten tekstien luokitteluun. Tämän diplomityön osana luotiin tekstinlouhintatyökalu suomenkielisille lääketieteellisille teksteille. Kehitettyä algoritmia voidaan käyttää erilaisten tilojen tunnistamiseen potilaskertomusteksteistä. Algoritmin käyttöä kuitenkin rajoittaa tarve suurehkolle määrälle opetusdataa

Machine learning-based dynamic mortality prediction after traumatic brain injury

Our aim was to create simple and largely scalable machine learning-based algorithms that could predict mortality in a real-time fashion during intensive care after traumatic brain injury. We performed an observational multicenter study including adult TBI patients that were monitored for intracranial pressure (ICP) for at least 24 h in three ICUs. We used machine learning-based logistic regression modeling to create two algorithms (based on ICP, mean arterial pressure [MAP], cerebral perfusion pressure [CPP] and Glasgow Coma Scale [GCS]) to predict 30-day mortality. We used a stratified crossvalidation technique for internal validation. Of 472 included patients, 92 patients (19%) died within 30 days. Following cross-validation, the ICP-MAP-CPP algorithm's area under the receiver operating characteristic curve (AUC) increased from 0.67 (95% confidence interval [CI] 0.60-0.74) on day 1 to 0.81 (95% CI 0.75-0.87) on day 5. The ICP-MAP-CPP-GCS algorithm's AUC increased from 0.72 (95% CI 0.64-0.78) on day 1 to 0.84 (95% CI 0.78-0.90) on day 5. Algorithm misclassification was seen among patients undergoing decompressive craniectomy. In conclusion, we present a new concept of dynamic prognostication for patients with TBI treated in the ICU. Our simple algorithms, based on only three and four main variables, discriminated between survivors and non-survivors with accuracies up to 81% and 84%. These open-sourced simple algorithms can likely be further developed, also in low and middleincome countries.Peer reviewe

Machine learning-based dynamic mortality prediction after traumatic brain injury

Our aim was to create simple and largely scalable machine learning-based algorithms that could predict mortality in a real-time fashion during intensive care after traumatic brain injury. We performed an observational multicenter study including adult TBI patients that were monitored for intracranial pressure (ICP) for at least 24 h in three ICUs. We used machine learning-based logistic regression modeling to create two algorithms (based on ICP, mean arterial pressure [MAP], cerebral perfusion pressure [CPP] and Glasgow Coma Scale [GCS]) to predict 30-day mortality. We used a stratified crossvalidation technique for internal validation. Of 472 included patients, 92 patients (19%) died within 30 days. Following cross-validation, the ICP-MAP-CPP algorithm's area under the receiver operating characteristic curve (AUC) increased from 0.67 (95% confidence interval [CI] 0.60-0.74) on day 1 to 0.81 (95% CI 0.75-0.87) on day 5. The ICP-MAP-CPP-GCS algorithm's AUC increased from 0.72 (95% CI 0.64-0.78) on day 1 to 0.84 (95% CI 0.78-0.90) on day 5. Algorithm misclassification was seen among patients undergoing decompressive craniectomy. In conclusion, we present a new concept of dynamic prognostication for patients with TBI treated in the ICU. Our simple algorithms, based on only three and four main variables, discriminated between survivors and non-survivors with accuracies up to 81% and 84%. These open-sourced simple algorithms can likely be further developed, also in low and middleincome countries

Paalutuskoneen käytönaikaisen stabiliteetin varmistava järjestelmä

Tämän projektityön tavoitteena oli tutkia mahdollisuutta paalutuskoneen stabiliteetin valvonnassa käytön aikana. Projektin päätavoitteena oli luoda järjestelmä, joka tunnistaa koneen stabiliteettiin vaikuttavat tekijät sekä opastaa kuljettaa ja mahdollisesti estää turvallisuutta heikentävät toiminnot. Opinnäytetyön aikana tutustuttiin EN 16228 standardiin ja sen sisältämiin vaatimuksiin paalutuskoneen stabiliteetistä. Työssä tutkitaan paalutuskoneen sisältämää anturitekniikkaa ja mietitään niiden hyödyntämisestä stabiliteetin seurannassa. Opinnäytetyön selostuksessa tutkitaan myös paalutuskoneen rakennetta ja paalutuskoneen stabiliteettiin liittyviä suureita sekä muodostetaan erilaisia malleja stabiliteetin varmistavasta järjestelmästä. Työn tuloksena syntyi 2 mallia mahdollisesta paalutuskoneen stabiliteetin valvonnasta. Ensimmäisellä mallilla pyritään tarkastelemaan paalutuskoneen vakavuutta muuttuvien koneen osien myötä. Toisella mallilla pyritään tuomaan kuljettajalle tieto ylitetystä vakavuuskulmasta, jonka kone ylittää. Opinnäytetyön tuloksena syntyneet mallit testataan ja toimivuus todetaan käyttäen simulaattoria.The aim of this thesis was to investigate the possibility of supervising the stability of a piling machine during operation. The main goal of the thesis was to produce a system that identifies the factors affecting the stability of the machine and guides the driver to possibly prevent safety-impairing functions or actions. The thesis was started by getting familiar with the EN 16228 standard and its requirements for the stability of a piling machine. The sensor technology contained in the piling machine was studied and its utilization in stability monitoring was considered. In addition, the structure of the piling machine and the quantities related to the stability of the piling machine were studied as well as various models of the system ensuring stability were formed. As a result of the thesis, two models of possible piling machine stability control were created. The first model seeks to examine the stability of a piling machine with changing machine parts. The second model seeks to provide the driver with information about the exceeded angle of gravity that the machine exceeds. The models created as a result of the thesis were tested and the functionality was determined using a simulator