Early detection of subclinical mastitis in lactating dairy cows using cow-level features.

Subclinical mastitis in cows affects their health, well-being, longevity, and performance, leading to reduced productivity and profit. Early prediction of subclinical mastitis can enable dairy farmers to perform interventions to mitigate its effect. The present study investigated how well predictive models built using machine learning techniques can detect subclinical mastitis up to 7 d before its occurrence. The data set used consisted of 1,346,207 milk-day (i.e., a day when milk was collected on both morning and evening) records spanning 9 yr from 2,389 cows producing on 7 Irish research farms. Individual cow composite milk yield and maximum milk flow were available twice daily, whereas milk composition (i.e., fat, lactose, protein) and somatic cell count (SCC) were collected once per week. Other features describing parity, calving dates, predicted transmitting ability for SCC, body weight, and history of subclinical mastitis were also available. The results of the study showed that a gradient boosting machine model trained to predict the onset of subclinical mastitis 7 d before a subclinical case occurs achieved a sensitivity and specificity of 69.45 and 95.64%, respectively. Reduced data collection frequency, where milk composition and SCC were recorded only every 15, 30, 45, and 60 d was simulated by masking data, to reflect the frequency of recording of this data on commercial dairy farms in Ireland. The sensitivity and specificity scores reduced as recording frequency reduced with respective scores of 66.93 and 80.43% when milk composition and SCC were recorded just every 60 d. Results demonstrate that models built on data that could be recorded routinely available on commercial dairy farms, can achieve useful predictive ability of subclinical mastitis even with reduced frequency of milk composition and SCC recording.

Testing two theories of conceptual combination: alignment versus diagnosticity in the comprehension and production of combined concepts.

People often interpret novel noun-noun combinations by transferring a property from one constituent concept of the combination to the other. Two theories make different predictions about these "property" interpretations. Dual-process theory predicts that properties transferred will be alignable differences of the concepts being combined. Constraint theory predicts that properties transferred will be diagnostic properties of the concepts in which they originate. An experimental study tested these contrasting predictions in interpretation comprehension and interpretation production tasks. The results showed that participants reliably preferred diagnostic property interpretations, whether alignable or nonalignable, in both tasks. There was no reliable preference for alignable interpretations in either task. This confirms constraint theory's predictions about property interpretations and goes against the predictions of dual-process theory.

What makes an analogy difficult? The effects of order and causal structure on analogical mapping.

In 4 experiments, the author tested 2 factors that affect the difficulty of analogies: order of presentation of information and causal structure. Experiments 1, 2, and 4 showed robust order effects for the positioning of sentences-sentence pairs in a variety of mapping problems. Experiments 2, 3, and 4 revealed the effects of causal structure in these analogies. Experiment 3 showed that the beneficial effects of causal structure are most marked in thematic, mapping problems presented in a casual question-answering context. Experiment 4 dealt with the interaction between order and causal structure and showed that order effects occur only in the presence of causal structure. Of all the analogy models in the literature, the incremental analogy machine is the best predictor of these results.

Effective retrieval in Hospital Information Systems: the use of context in answering queries to Patient Discharge Summaries.

The move towards the electronic storage of medical records in Hospital Information Systems (HISs) presents significant challenges for AI retrieval techniques. In this paper, we argue that adequate information retrieval in such systems will have to rely on the exploitation of the conceptual knowledge in those records rather than superficial string searches. However, this course of action is dependent on the developments of natural language processing techniques and on retrieval systems that can exploit semantic/conceptual knowledge. We present a retrieval system, which attempts to realise the second of these developments. This system, called CONIR [developed in the context of the European Community project MENELAS (AIM 2023)] operates in the domain of Patient Discharge Summaries on coronary illness. CONIR uses flexible retrieval techniques, that exploit conceptual context information, over a database of elaborated semantic records. In the course of the paper we outline the sorts of knowledge structures that are required to do this type of retrieval and indicate how they are constructed.